GIS1

A Simple Linear Model:

Inputs Parameters Outputs

Multi-view stereo

' f' f Step 3 Next, a dense point cloud and 3D surface are determined using the known camera parameters and the sparse point cloud as input. All pixels in all images are used so the dense model is similar in resolution to the raw photographs (typically 100s- 1000s point/m2). This step is called "multi-view stereo matching" (MVS)

Geographically Weighted Regression (GWR)

(GWR) is one of several spatial regression techniques, increasingly used in geography and other disciplines. GWR provides a local model of the variable or process you are trying to understand/predict by fitting a regression equation to every feature in the dataset. When used properly, these methods provide powerful and reliable statistics for examining and estimating linear relationships.

SfM Workflow to Create a Product

1) Find the same features in multiple overlapping photographs. 2) Find camera locations and orientation and extract a low density point cloud 3) Multi-view stereo matching: Takes the sparse point cloud and camera locations to populate a model with more points. 4) Georectification: converts the point cloud from an internal, arbitrary coordinate system into a geographical coordinate system 5) Triangulation to estimate the 3-D point cloud

Model Taxonomy

1) Rule based or logical models 2) Empirical or black box models (Regression models) 3) Process based models

Local Interpolation Methods

1) Thiessen Polygons 2) Inverse Distance Weighting (IDW) 3) Spline Deterministic

Main goals of GIS

1) exploratory spatial data analysis (esda): data visualization, computing descriptive analysis, understanding distribution, understanding relationships. 2) formulating hypotheses 3) spatial problem solving

ESDA- descriptive statistics

1) measures of central tendency: mode, median, mean 2) measures of dispersion: range, variance, standard deviation.

General guidelines for fitting models

1. First, plot the experimental variogram. 2. Choose several models with a similar shape and fit each in turn by weighted least squares. 3. Plot the fitted models on the graph of the experimental variogram and assess whether the fit looks reasonable. If all plausible models seem to fit well, choose the one with the smallest residual sum of squares (RSS) or smallest mean square. 4. If the models have unequal numbers of parameters, compute the AIC and choose the model for which the AIC is least.

Global Polynomial Interpolation

1. Fitting a surface to the sample points when the surface varies slowly from region to region over the area of interest (pollution over an industrial area). 2. Examining and/or removing the effects of long-range or global trends which is referred to as trend surface analysis.

7) Check How Regression Models Go Bad :

1. Omitted explanatory variables 2. Nonlinear relationships between explanatory and dependent variables 3. Data outliers 4. Nonstationarity: You might find that an income variable, for example, has strong explanatory power in region A but is insignificant or even switches signs in region B Model relationships are not stationary across the study area. Notice that the relationship between the number of 911 calls and the number of people is different in high-population tracts and southwestern low-population tracts. 5. Multicollinearity. One or a combination of explanatory variables is redundant. Using more than one of these explanatory variables in a single regression model would introduce redundancy and lead to an unstable model. Each explanatory variable in your regression model should get at a different facet of the dependent variable you are trying to predict or model. 6. Heteroscedasticity = Inconsistent variance in residuals. It may be that the model predicts well for small values of the dependent variable but becomes unreliable for large values. The cone-shaped scatterplot indicates that the model performs differently depending on the magnitude of the estimated value. In this case, the model performs better (the residuals are smaller) for tracts with fewer crimes than for tracts with many crimes. 7. Spatially autocorrelated residuals Notice how the model underpredictions (red) are spatially clustered. Statistically significant spatial clustering of residuals (model over- and underpredictions) is evidence that your model is missing key explanatory variables.

How to Build a Model with Model Builder

1. Open Model Builder 2. Add Data 3. Add geoprocessing tools 4. Connect data and tools 5. Change tool parameters 6. Set model parameters 7. Run the model

Building the 3D Model Workflow

1. The photographs, after a quality check, will be aligned. This means the matching features are found, generating a sparse point cloud and the camera locations. 2. Generate the high density point cloud. This is one of the products you may end up using; millions of points will be in the point cloud, creating a very realistic model of the feature. 3. Calculate a mesh from the point cloud. The "mesh" uses the points to create a surface composed of thousands of triangles. This mesh can be draped with the texture atlas, like draping a photograph

UAS Main Components

1. Unmanned Aerial Vehicle (UAV) 2. Ground control Station 3. Communication data links

History of remote sensing

1827- The first photograph was obtained by Joseph Nicephore Niepce 1858 - Gaspard Tournachon took the first aerial photograph from a balloon near Paris. Unfortunately, this first aerial photograph did not survive In 1903, Julius Neubronner patented a breast-mounted camera for carrier pigeons that weighed only 70 grams. A squadron of pigeons is equipped with light-weight 70-mm aerial cameras 1972- NASA Launched Earth Resource Technology Satellite (ERTS-1) 1975- ERTS-2 launched renamed to Land Remote Sensing Satellite (Landsat The Lake Eyre Basin in the interior of Australia is among the driest places on the continent. With less than 125 mm/yr (5 inches) of rain, the streams and creeks that drain into Lake Eyre are usually bone dry, barren, and brown. Occasionally, the channels do fill after heavy downpours and create a carpet of green.

8) Check How Regression Models Go Bad

8. Normal distribution bias: When the regression model residuals are not normally distributed with a mean of zero, the p-values associated with the coefficients are unreliable.

Confidence level

90, 95, 99 99% indicates that you are unwilling to reject the null unless the probability that the pattern was random is less than 1%

GIS

A computer system that stores, organizes, analyzes, and displays geographic data. (Hardware, software, data, people, apps, methods)

Geometric Correction

A critical consideration in the application of remote sensing is preparation of planimetrically correct versions of aerial and satellite images so that they will match to other imagery and to maps. Geometric correction will provide the basis for accurate measurements of distance and area from an image. Remotely sensed imagery typically contain internal and external geometric errors

Creating a depressionless DEM

A digital elevation model (DEM) free of sinks—a depressionless DEM—is the desired input to the flow direction process. Sinks are areas of internal drainage, that is, areas that do not drain out anywhere. The presence of sinks may result in an erroneous flow-direction raster

Metadata

A key aspect of working with ArcGIS is documenting the content and project items you create and use—your maps, projects, geoprocessing models, geodatabase datasets, and so on. "Metadata is information about data. Similar to a library catalog record, metadata records document the who, what, when, where, how, and why of a data resource. Geospatial metadata describes maps, Geographic Information Systems (GIS) files, imagery, and other location-based data resources".

Stream order

A method for identifying and classifying types of streams based on their numbers of tributaries.

Physical Model

A physical model or a scaled model is made from the same materials as those of the natural system. A physical model of a housing development:

Raster Data

A raster consists of a matrix of cells (or pixels) organized into rows and columns. Each cell contains a value representing information, such as temperature, elevation, etc The area (or surface) represented by each cell consists of the same width and height and is an equal portion of the entire surface represented by the raster. Cell values can be either positive or negative, integer, or floating point. NoData cells are typically have value of -9999

Remote sensing measurements

A remote sensing instrument collects information about an object or phenomenon within the instantaneous-field-of- view (IFOV) of the sensor system without being in direct physical contact with it. The sensor maybe located a few meters above the ground, an/or onboard an aircraft or satellite platform

Arc GIS Pro User Interface

A view is a window that represents primary work area, can be a map, scene, table, layout, or chart You can have multiple views at once.

Watershed

A watershed is the basic hydrologic unit within which all measurements, calculations and predictions are made in hydrology

What is remote sensing?

ASPRS adopted a combined formal definition of photogrammetry and remote sensing as (Colwell, 1997): "the art, science, and technology of obtaining reliable information about physical objects and the environment, through the process of recording, measuring and interpreting imagery and digital representations of energy patterns derived from noncontact sensor systems

Absorption

Absorption is the process by which radiant energy is absorbed and converted into other forms of energy

Summary

Accuracy assessment is a very complex process. • It is critical to inventory, mapping and monitoring projects • No project should be considered complete without an accuracy assessment • The same techniques can be applied equally well to aerial photography and photo interpretation • Must be well planned and statistically valid • Should be reported as an error matrix and associated statistics • It's expensive -- so budget for it

Accuracy and Precision

Accuracy defines "correctness"; it measures the agreement between a standard assumed to be correct and a classified image of unknown quality. Precision defines a measure of the sharpness (or certainty) of a measurement

Sample size - experimental variogram

Accuracy of variogram depends on having enough data at a suitable density or separating interval. At shortest lags, number of paired comparison might be small. As lag interval between data decreases, number of comparisons increase.

ASTER

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Onboard Terra 14 different bands 15 - 90 meters spatial resolution Every 16 days

AVHRR

Advanced Very High Resolution Radiometer (AVHRR) Imagery AVHRR: 1-km multispectral data from the NOAA satellite series (1979 to 2019

ArcGIS pro licensing model

Advanced: using advanced spatial analysis Named User License

Kappa Statistic

After an initial inspection of the error matrix, there is often a need for a more objective assessment of the classification. For example, we may ask if the two maps are in agreement—a question that is very difficult to answer because the notion of "agreement" may be difficult to define and implement. κ (kappa) is a measure of the difference between the observed agreement between two maps (as reported by the diagonal entries in the error matrix) and the agreement that might be attained solely by chance matching of the two maps. Not all agreement can be attributed to the success of the classification. κ attempts to provide a measure of agreement that is adjusted for chance agreement. Observed designates the accuracy reported in the error matrix = "overall accuracy" Expected designates the correct classification that can be anticipated by chance agreement between the two images. Expected values are calculated using the row and column totals. K > 80% Strong agreement of accuracy 40% < k < 80% Moderate agreement K < 40% Poor agreement

catchment/watershed

All the upstream area which contributes to the open channel flow at a given point along a river (Brutsaert, 2005 ). Drainage basins are separated topographically from adjacent basins by a geographical barrier such as a ridge, hill or mountain - known as a water divide.

Box plot

Allows you to visualize and compare distribution and central tendency of numeric values through their quartiles

Spatial analysis process

Always formulate hypothesis first. GIS software are powerful. Effective spatial analysis requires an intelligent user.

6) Assess Residual Spatial Autocorrelation:

Always run the Spatial Autocorrelation (Moran's I) tool on the regression residuals to ensure that they are spatially random. Use the Spatial Autocorrelation tool to ensure that model residuals are not spatially autocorrelated

aerial/regional perspective

An aerial perspective like this helps to identify objects and patterns

Definition of UAS

An unmanned aircraft system (UAS) is an unmanned aircraft and the equipment necessary for the safe and efficient operation of that aircraft. An unmanned aircraft is a component of a UAS. It is defined by statute as an aircraft that is operated without the possibility of direct human intervention from within or on the aircraft (Public Law 112-95, Section 331(8)).

Analog Groundwater Models

Analog models are consists of resistors and capacitors. The flow of electrical current is representative of fluid flow. Measurements of voltage at different points represent the hydraulic head of an aquifer (Fetter, 2001). http://www.isws.illinois.edu/hilites/achieve/gwmodpic.asp?p=234 © Ajami (Not for Distribution) • Geometry • Materials properties • Boundary conditions

Ripley's K Function

Analysis of dispersion and clustering over a range of scales by computing the k function for all distances. Compute the number of events inside a circle of radius d centered on each of the events. Compute mean count for all events at distance d. Divide mean count by overall study area density to estimate k(d). Repeat for all distances

logical operators

And, or, not , complex selection

Raster functions

ArcGIS Pro provides many raster functions to process and analyze imagery and raster data.7

Sharing Your GIS Work in ArcGIS Pro

ArcGIS Pro provides numerous options for sharing your work with others. • Share web layers : designed for map visualization, editing, and query • Share web maps • Share web scenes • Share Packages: A package is a compressed file containing GIS data. Examples: project package, map package, geoprocessing package and etc.

Layout

Arrangement of one or more maps and supporting elements such as title, legend, and text. Shared as a printed map, poster, or pdf.

Scatterplots

Assess a linear relationship between two variables Each axis corresponds to one of the variables

Zero autocorrelation

Attributes are independent of location

Universal Transverse Mercator (UTM) Projection

Based on Mercator projection, analogous to wrapping a cylinder around poles rather than equator, 60 zones to line of longitude each 6 degrees wide, coordinates in meters, north hem equator is origin, south hem equator false northing

Model Taxonomy- Logical Models

Based on logic and straightforward set theory operations Modeling hazards of groundwater pollution by fertilizers:

Natural Breaks Classification

Based on natural groupings in data. Best group similar values that maximize the differences between classes. Minimize the average deviation from the class mean, while maximizing the deviation from the means of the other groups. Class ranges are tailored to one data set, so difficult to compare maps for different data sets.

Nearest neighbor method

Based on nearest neighbor distance for an event in a point pattern. G function: based on cumulative frequency of the nearest neighbor distances. Increases rapidly at short distances.

3) Assess Model Statistical Significance:

Both the Joint F-Statistic and Joint Wald Statistic are measures of overall model statistical significance. Both the Joint F-Statistic and Joint Wald Statistic are measures of overall model statistical significance. The null hypothesis for both of these tests is that the explanatory variables in the model are not effective. For a 95 percent confidence level, a p-value (probability) smaller than 0.05 indicates a statistically significant model. The Joint F-Statistic is trustworthy only when the Koenker (BP) statistic is not statistically significant. If the Koenker (BP) statistic is significant, you should consult the Joint Wald Statistic to determine overall model significance.

Kernel Density Funtion

Calculates the density of features in a neighborhood around those features. It can be calculated for point and line. Every point is replaced by its Kernel function, all function added to create a density surface.

Euclidean distance

Calculates, for each cell, the Euclidean distance to the closest source Map showing the distance to the nearest town for each location

First-order effects

Causes variations in the intensity of the process across space= spatial density of events. Absolute location is an important determinant of observations.

Queens case

Cells only meet at a corner vertex

Rook's Case

Cells sharing a common edge

Battery Safety

Charging Always have a fire extinguisher available Never charge unattended Charge in a fire resistant pouch or container Storage Store in a fire resistant pouch or container Charge/discharge to recommended storage voltage Disposal Submerge in salt water Pierce with a nail

2) Assess Explanatory Variables

Check the p-values for statistical significance • Check the VIF: explanatory variables associated with VIF values larger than about 7.5 should be removed (one by one) from the regression model.

Sampling Interval- Experimental variogram

Choice depends on the scale of variation that the practitioner wishes to resolve (experimental plot, field, farm, catchment). Shows no spatial structure!

Classification Error

Classification error is the assignment of a pixel belonging to one category (as determined by ground observation) to another category during the classification process. Classification errors of omission and commission, along with correctly- classified Wet and Dry class pixels

Co-Kriging

Cokriging uses information on several variable types to interpolate variable of interest (Z1). It is appealing to use information from other variables to help make predictions, but it comes at a price. Cokriging requires much more estimation, including estimating the autocorrelation for each variable as well as all cross-correlations.

Geographic Layer

Collection of geographic entities of the same geometric type (points, lines, polygons)

Project Workflow

Collection of maps, layouts, layers, tables, tools, and connection to servers, databases, folders, and styles.

Exploratory spatial data analysis (esda)

Collection of techniques to describe and visualize spatial distributions, identify spatial outliers, discover patterns of spatial association (spatial clusters), and suggest spatial regimes. Data driven, bottom-up approach for hypothesis Provides foundation for spatial modeling. Analyzing single variable through space or time.

What do You Create?

Colored point cloud

Keys

Columns that uniquely identify every row in a table, used to join data from one table to associated data in another table.

Measuring Map Accuracy

Comparing remotely sensed data (the map ) with another data from a different source that is assumed to be accurate (reference data). Requirements: • Both dataset have to be co-registered. • They both use the same classification system and minimum mapping unit • They have been classified at comparable levels of detail.

Experimental Variogram from regular 1D sampling

Comparisons for computing Variogram for 3 lag intervals from a regular shape every 10m along transect. Semivariances plotted against the first 3 lag intervals

Spatial data models represent

Computers represent binary digits, every item reduced to 0s and 1s. Standard to convert to TIFF or JPEG

Intersect

Computes a geometric intersection of the input features. Features or portions of features which overlap in all layers and/or feature classes will be written to the output feature class.

Union

Computes a geometric union of the input features. All features and their attributes will be written to the output feature class

Types of Models

Conceptual Models • Physical Models • Analog Models • Mathematical Models © Ajami (Not for Distribution) All modeling projects start with the development of a conceptual model.

Two map projection properties

Conformal property: ensures that shapes of small features of earths surface are preserved on projection, scales in x,y directions are always equal. Equal area property: areas measured on map are always in the same proportion to areas measured on earths surface.

How Many Samples?

Congalton and Green (2009) suggest acquiring 50 samples per category as a general guideline, with increases to 75-100 samples per category as either the area or the number of categories increases beyond certain thresholds (1 million acres and/or 12 categories). Some analyst recommend defining the sample size based on the Binomial probability theory or multinomial distribution

Equivalent Projection

Conic projection, two standard parallels, scale and shape not preserved, but distortion minimal, East/to-west land masses

Spatial data models

Contain information about the LOCATION using (x,y), contain information about PROPERTIES or ATTRIBUTES (name, color, pH, cash value), contain Metadata (data about data)

Geodatabase

Container to hold a collection of datasets: Table, feature class, raster dataset

Contrast Enhancement

Contrast enhancement involves changing the original pixel values so that more of the available range is used, thereby increasing the contrast between targets and their backgrounds. It is done based on the image histogram.

Raster vector conversion

Converts a raster to a feature dataset, as points, lines, or polygons

Deterministic interpolation method

Create surfaces from measured points, based on either the extent of similarity (inverse distance weighted) or the degree of smoothing (radial basis function).

Erase

Creates a feature class by overlaying the input features with the polygons of the erase features. Only those portions of the input features falling outside the erase features boundaries are copied to the output feature class.

Creating flow accumulation

Creates a raster of accumulated flow into each cell. Number of cells draining into a given cell along the flow

Conformal Projection

Cylindrical projection using cylinder tangent at equator, parallels and meridians at right angles, size/shape/area of large objects distorted.

Calculating Radiances from DNs

DNs can be converted to radiances using data derived from the instrument calibration provided by the instrument's manufacturer.

PPDAC- Data Phase

Data quality & sourcing issues Quality Cost Licensing Availability Completeness & consistency Timeliness Detail/resolution Intangible/qualitative data Data quality & GIS tools - examples Boundary definition and density computation Missing data handling and masking Classification Data transformation Error mapping

relational database management system (RDBMS)

Database tables can be joined, tables are related through keys

Attributes

Describes the non-spatial properties of each object, presented in tables

Spatial process

Description of how a spatial pattern might be generated. Processes create patterns

Global techniques

Deterministic, calculate prediction using the entire dataset

Local techniques

Deterministic, calculate predictions from the measured points within neighborhoods that are smaller than the study area.

Geostatistical Interpolation methods

Developed in mining industry by Daniel Krige (1951) First application in gold mining. Assumes that all values are result of random process. Random doesn't mean independent. Random Process with dependence, autocorrelation. Includes body of statistical technique based on spatial random processes. Makes unbiased predictions with minimum and known variance or error.

Calculating Radiances from DNs

Digital data formatted for distribution to the user community present pixel values as digital numbers (DNs). DNs expressed as integer values to facilitate computation and transmission and to scale brightness for convenient display. DNs express accurate relative brightness within an image but cannot be used to: • examine brightness over time (from one date to another) • compare brightness from one instrument to another • match one scene with another • prepare mosaics of large regions • as input for models of physical processes DNs can be converted to radiances using data derived from the instrument calibration provided by the instrument's manufacturer. To do this, we need to discuss radiant flux density.

digital image classification

Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition

Raster

Divide the world into arrays of cells and assign attributes to the cells

Equal interval classification

Divides the range of attribute values into equal-sized subranges Emphasizes the amount of an attribute value relative to other values

quantile classification

Each class contains an equal number of features. Well-suited for linear data. Assigns the same number of data values to each class, no empty classes or classes with too few or too many values. Features with similar values may end up in different classes, exaggerating their differences.

User-defined tables

Each dataset in the geodatabase is stored in one or more tables. Work with the system to mage data.

Hydrologic units

Each hydrologic unit is identified by a unique hydrologic unit code (HUC) consists of two to twelve digits based on the six levels of classification: 2-digit HUC first-level (region) 4-digit HUC second-level (subregion) 6-digit HUC third-level (accounting unit) 8-digit HUC fourth-level (cataloguing unit) 10-digit HUC fifth-level (watershed) 12-digit HUC sixth-level (subwatershed)

One-to-many

Each object of the origin table can be related to multiple objects in the destination table.

One-to-one

Each object of the original table can be related to zero or one object of the destination table.

Digital image

Each pixel has an original brightness value or Digital Number (DN). The dataset may consists of multiple number of bands

Spatial weight matrix

Each w(Ij) value is independent of the spatial relationship between locations I and j =1 if two locations are adjacent =0 if they are not adjacent Based on how closely features are related

Continuous fields

Elevation, rainfall, pollution concentration (represents where each location is a measure of the concentration level)

Pathways of EMR

Energy-matter interactions in the atmosphere, at the study area, and at the remote sensor detector

Image Enhancement

Enhancements are used to make it easier for visual interpretation and understanding of imagery. Although radiometric corrections for illumination, atmospheric influences, and sensor characteristics may be done prior to distribution of data to the user, the image may still not be optimized for visual interpretation.

Properties of Classification Errors

Errors are not randomly distributed over the image but they display a degree of systematic, ordered occurrence in space. • Often erroneously assigned pixels are not spatially isolated but occur in groups of varied size and shape. • Errors may have specific spatial relationships to the parcels to which they pertain; for example, they may tend to occur at the edges or in the interiors of parcels.

Sources of Classification Error

Errors are present in any classification. They can be caused by: • Data acquisition errors: Sensor performance, stability, view angle, atmosphere • Data processing errors: Misregistration • Scene-dependent errors: Resolution, mixed pixels • Misclassification: Errors of omission and commission • Inaccurate reference data Temporal inconsistencies between reference map and imagery Incorrectly identified data used for training the classifier If reference data class is incorrect in the map used for accuracy assessment, it will be counted as an error even if the pixels are correctly classified

Correcting External Geometric Errors

Errors caused as a result of altitude and attitude change can be corrected using ground control points (GCP) and an appropriate mathematical model. Analyst need to obtain two sets of coordinates for each GCP: 1) Image coordinates specified in rows and columns 2) Map coordinates (x,y measured in degrees of latitude and longitude or meters in a UTM projection There are two common types of Geometric Correction: 1) Image to map rectification 2) Image to image registration

PPDAC- Conclusion Phase:

Expectations on delivery met? • Identify and report strengths, weaknesses and possible sources of error • Recommendations • Future actions

PPDAC- Analysis Phase

Exploratory analysis & visualization • Interpretation of spatial patterns • Construction of hypotheses • Build-test-criticize 'model' cycle • Match to expectations? Simple dynamic residential growth model Sample risk assessment model

Raster extraction tools

Extraction tools allow you to extract a subset of cells from a raster by: • Attribute value (extraction of cells higher than 100 meters in elevation from an elevation raster) • Geometry of their spatial location (Extract by Circle, Polygon or Rectangle) • Extract by points or mask13

Clip

Extracts input features that overlay the clip features. Useful for creating a new feature class

Extract by circle

Extracts the cells of a raster based on a circle by specifying the circle's center and radius

Extract by mask

Extracts the cells of a raster that correspond to the areas defined by a mask

negative spatial autocorrelation

Features that are close together in space tend to be more dissimilar in attributes than features that are further apart

Problems associated with in Situ data collection

Field data collection is always prone to error. Such errors can be introduced by: • Sampling design does not capture the spatial variability of the phenomena under investigation (i.e., some phenomena or geographic areas are oversampled while others are under-sampled); • Improper operation of in situ measurement instruments • uncalibrated in situ measurement instruments5

Photogrammetric Measurements

From a single vertical aerial photograph: • Photo scale • Object height • Object length • Areas/perimeters • Tone/color Multiple (overlapping) stereoscopic aerial photographs: • Precise planimetric (x,y) locations • Precise object height (z) • Digital Elevation Models (x,y,z) • Bathymetric Models (x,y,z) • Slope and aspect4

The Power of GIS

GIS technology is the central component of the larger category of geospatial technologies. As you learned so far, GIS is beyond map making. Through analysis, apps, smart mapping, and systems for connecting workflows, "GIS fosters collaboration to tackle the world's most complex problems"

system tables

Geodatabase system tables keep track of the contents of each geodatabase, rules and relationships

Georeferencing Systems

Geographic location is the element that distinguishes geographic information from all other types. 1) Linear referencing systems: widely used in transportation 2) Cadasters and the US Public Land Survey System: cadaster is map of land ownership, for taxes, parcels as number or code 3) Geographic Coordinate system

Statistical distribution- experimental variogram

Geostatistical analysis does not require data to follow a normal distribution. Variograms comprise sequences of variances, and these can be unstable where data are strongly skewed and contain outliers. If your data do not have a near normal distribution and have a skewness coefficient outside the limits of +-1 you should consider transforming them. Variogram is sensitive to outliers, all outliers should be considered as potentially erroneous before they are allowed to remain. Largest values are of most interest!

Hot Spot Analysis

Geovisualization and spatial analysis help you to explore patterns

Experimental variogram from 2D samples

Grid can be treated as a series of transects in two dimensions. Variogram can be computed along rows, columns, and diagonal. Can be computed over all directions (omnidirectional) for both regular and irregular shapes.

Ground control station

Ground Control Stations (GCSs) are stationary or transportable hardware/software devices to monitor and command the UAs

Overlay analysis

Group of methodologies applied in optimal site selection or suitability modeling. Where to site new housing development? 1. Define problem 2. Break problem into sub models 3. Determine significant layers 4. Reclassify or transform data within a layer 5. Weight the input layers 6. Add or combine layers 7. Select best locations 8. Analyze

Edge effects or boundary problems

Happens when you impose artificial boundary on a particular region.

Density based methods- overall density

Idea of Kernel Density Equation is pattern has a density at any location in study location, not just locations of event. KDE counts number of events in a region or a kernel centered at that location.

Hot spot analysis

Identifies statistically significant spatial clusters of high and low values. Computes Getis-ord Gi statistic by looking at each feature within the context of neighboring features. Resultant z-scores and p-values will tell you where features cluster spatially.

Spatial analysis aimed at

Identify and describing the patterns in data Identifying and understanding the processes causing patterns

Cross validation for interpolation accuracy

If a large number of data points are available: 1) Use subset of the data points to perform interpolation. 2) Evaluate errors in the interpolated results by comparing the predictions to the unused control points. 3) Repeat step 1 and 2 using different sets of data points. The preferred interpolation method is the one the gives the smallest errors

Line or Column Drop Outs

If an individual detector in a scanning system fails to function properly, an entire line containing no spectral information may be produced. This line drop out will show as a black line in the k band of the imagery. As no information is collected by the sensor, you may just use an averaging algorithm to interpolate values between the neighboring lines.

Cross-Validation for Interpolation Accuracy

If large number of data points available: 1) use subset of data points. 2) evaluate errors by comparing predictions to unused control points.. 3) repeat, preferred is smallest error.

Sill Variance

If the process is second-order stationary then the variogram will reach an upper bound after the initial increase

Atmospheric Correction

If you are extracting biophysical parameters from an imagery, you need to perform atmospheric correction. Biophysical parameters are: chlorophyll a, suspended sediment, biomass, leaf area index Even for computing NDVI, atmospheric corrections need to be made Preprocessing operations to correct for atmospheric degradation fall into three rather broad categories: 1) Radiative transfer code (RTC) computer models, which model the physical behavior of solar radiation as it passes through the atmosphere. 2) Image based atmospheric correction based on the spectra of objects of known brightness. 3) Advanced techniques that are based on models and consider interrelationships between imagery from multiple bands. Examples: MODTRAN, ATCOR

Problems with deterministic interpolation methods

Ignoring variability and error associated with environmental measurements as they are often single snapshot in time. Ignoring overall spatial behavior of a given pattern using information from sample data.

Image classification

Image classification is the task of assigning classes to all the pixels in a remotely sensed image. The output raster from image classification can be used to create thematic maps. Image classification can be a lengthy workflow with many stages of processing There are two main methods of classification: • Supervised classification is where you decide what class categories you want to assign pixels or segments to. These class categories are referred to as your classification schema. • Unsupervised classification is where you let the computer decide which classes are present in your image based on statistical differences in the spectral characteristics of pixels

Image Preprocessing

Image preprocessing steps can be classified to two main groups: 1) Radiometric preprocessing 2) Geometric preprocessing Although certain preprocessing procedures are frequently used, there can be no definitive list of "standard" preprocessing steps. Each project requires individual attention, and some preprocessing decisions may be a matter of personal preference.

Ordinal scale

Imply a ranking or order (high, mid, low)

Stationarity

In general, statistics rely on some notion of replication. Estimates can be derived and the variation and uncertainty of the estimates can be understood from repeated observations. The idea of stationarity is to obtain the necessary replication. Mean: assumes that the mean is constant between samples and is independent of location Second-order stationarity for Covariance: assumes that the covariance is the same between any two points that are at the same distance and direction apart no matter which two points you choose

Logistic Regression Models

In logistic regression models, the response variable is a binary (0,1) variable. Explanatory variables are categorical or continuous. Explore the relationship between AGE and the presence or absence of coronary heart disease (CHD). Therefore, in logistic regression the mean of a binary variable is a probability (π). The logistic regression model relates that probability, through a logistic function, to variation in the explanatory variables Logistic regression fits probability functions of the following form: This equation describes a family of sigmoidal curves, three examples of which are given below .Equivalently, the logistic regression model relates the log odds [log (π / 1 - π)] in a linear way to variation in the explanatory variables. logit or log-odds function: When we use the logit as a link function, we have the Logistic regression model:

What is an Algorithm?

In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks

No Atmospheric Correction

In the following situations, atmospheric correction is not required: • If you are using a single imagery for image classification, typically atmospheric correction is not required. • If you are performing change detection by comparing two images from two different dates, no atmospheric correction is required.

Scale Over variable relief terrain

Infinite number of different scales present due to the different elevation of terrain. Higher elevations: larger scale (closer) Lower elevations: smaller scale (farther away) Usually an average scale is used

Water balance of a watershed

Inflow of Water: Precipitation Groundwater inflow Surface water inflows Outflow of Water: Groundwater Outflow Stream Outflow Evapotranspiration Diversions Storage: Soil moisture Groundwater Snow and ice Channels and lakes Vegetation System =watershed

Model Components

Inputs: Precipitation, PET, and ... Parameters: depends on model structure Outputs: Stream flow and ... Each geoprocessing tool performs a small yet essential operation on geographic data.

geographic database

Integrated set of data on a particular subject, collection of tables

Second-order effects

Interaction between locations depending on distance between them. Relative location is important. Causes decreases or increases in distances between neighboring events.

Internal Geometric Error:

Internal geometric errors are caused by: 1) Remote sensing system 2) Earth rotation 3) Curvature characteristics These distortions are often systematic (predictable) and maybe identified and corrected pre-launch considering the sensor characteristics.

Catalog pane

Inventory of items in your project and commands to manage them

Bootstrapping

Involves fitting a surface as many times as there are sample points, each time withholding one of the points. Then subtract the withheld measured value from the interpolated values. Repeat n times.

Scale Invariant Feature Transform (SIFT)

It identifies features in each image that are invariant to the image scaling and rotation and partially invariant to changes in illumination conditions and 3-D camera viewpoint • Finds matching features in multiple photographs • Scale, perspective, and illumination of the feature in the photograph do not affect algorithm • Used as the input for calculating camera locations

Watershed Delineation

It involves multiple steps: 1. Creating a depressionless DEM 2. Flow direction 3. Flow accumulation 4. Watershed outlet points 5. Delineating watersheds19 Determines the contributing area above a set of cells in a raster

Hybrid Approach to Image Registration

It involves two steps: 1) Image to map rectification 2) Image to image registration It is recommended for change Detection methods.

Geostatistical prediction- Kriging

Kriging is the Geostatistical method of prediction. • It is the best linear unbiased predictor: its prediction error variances are minimized. • In practice, it is a weighted moving average in which the weights depend on the variogram and the configuration of the sample points within the neighborhood of its targets. • The biggest strength is in providing the error estimates in its interpolations. • There are many types of Kriging methods The equations for kriging are contained in matrices and vectors that depend on the spatial autocorrelation among the measured sample locations and prediction location. The autocorrelation values come from the semivariogram model. The matrices and vectors determine the kriging weights that are assigned to each measured value in the searching neighborhood.

Levels of Data Processing and Spatial Resolution

Level 1 and Level 2 data products have the highest spatial and temporal resolution. Level 3 and 4 products are derived products with equal or lower spatial and temporal resolution than Level 2 products.

Represent geospatial data in digital

Levels or abstraction in representation of spatial entities, more machine compatible but humanly obscure forms

Lidar

LiDAR uses invisible laser pulses to measure the distance between a laser source and an object (e.g., topography, outcrops). The laser pulses, called "shots", are emitted from a laser scanner at 10s to 100s kHz depending on the type of scanner. The travel times of the laser shots from the scanner to the object and back are measured. By knowing the position and orientation of the scanner, we can produce a "point cloud" of the reflected laser pulses that represents the scanned object

Geoprocessing pane

List of geoprocessing toolboxes installed with ArcGIS Pro.

Contents pane

List of your map contents

ArcGIS Enterprise

Main benefit is improved processing time for large volumes of data through: • GeoAnalytics Server • Image Server

Map Algebra

Map Algebra provides a rich suite of tools for performing comprehensive, raster-based spatial analysis and modeling. Map Algebra expressions can consist of a single tool or operator. It can also consist of multiple tools and operators. A simple Map Algebra expression used to execute a single tool includes the tool name followed by the input dataset and the tool parameters within parentheses Outras (output)=slope(function) *("elevation"(input) , "percent_rise"(parameters))

Mathematical Modeling

Mathematical models are abstraction of reality Mathematical models are useful tools for: Hypotheses testing to understand system behavior Practical applications such as examining the impact of various scenarios on system response. Making predictions Fill the observational data gaps

Error Metrics

Mean Absolute Error root mean squared error

spatial autocorrelation

Measures deal simultaneously with similarities in the location of spatial objects and their attributes.

Global Moran's I

Measures spatial autocorrelation based on feature locations and attribute values using Global Morans I statistic (-1 = dispersed ; 1= clustered) Inferential meaning results are always interpreted within the context of its null hypothesis(random)

Directional distribution

Measures trend for a set of points or areas by calculating standard distance separately in the x,y, z directions. Define axes of an ellipse encompassing distribution features. Standard deviations help understand the dispersion. Use either locations of the features or the locations and attribute values.

Medical

Medicine causing drowsiness should be checked Easy way to check is to call local FAA medical examiner Courtesy of Michael Wing, AirCTEMPS All must abide to the medical restrictions: • Remote pilot operator handling radio controls • Ground control software operator • Visual observers

Centrography

Method based on computing the mean center and standard deviation of a pattern. Advantages: comparing point patterns, tracking changes in pattern with time. Disadvantages: does not provide any information about any pattern itself, extremely sensitive to the borders of a chosen region.

Mie scattering

Mie scattering takes place when there are essentially spherical particles present in the atmosphere with diameters approximately equal to the wavelength of radiation being considered. The greater the amount of smoke and dust particles in the atmospheric column, the more violet and blue light will be scattered away and only the longer orange and red wavelength light will reach our eyes

Quartiles

Minimum, first (25th percentile), median, third (75th percentile, maximum

Model Builder

Model Builder is a visual programming language for building geoprocessing workflows. You create and modify geoprocessing models in Model Builder, where a model is represented as a diagram that chains together sequences of processes and geoprocessing tools, using the output of one process as the input to another process. The model runs the following tools in sequence: 1. Select Layer By Attribute—Select the correct vegetation type from a Vegetation map layer. 2. Buffer—Create areas within a distance of 1,500 feet around major roads. 3. Erase—Erase the buffer areas from the selected vegetation areas. 4. Intersect—Overlay the output of the Erase tool with other map layers, including slope, elevation, and climate. This identifies the areas that meet all criteria. Model Builder in ArcGIS Pro allows you to • Build a model by adding geoprocessing tools, map layers, datasets, and other data types, and connecting them to a process. • Iteratively process every feature class, raster, file, or table in a workspace. • Visualize your workflow sequence as an easy-to-understand diagram. • Run a model step-by-step, up to a selected step, or run the entire model. • Make your model into a geoprocessing tool that can be shared with others or can be used in Python scripting and other models

Example Geospatial Model- Suitability Analysis

Modeling mountain lion habitat in Southern California The mountain lions (cougars) are the largest carnivores that live, hunt, and breed in this Southern California area. Our challenge is to ensure they survive. By connecting their remaining natural habitats to one another, in theory, this will allow the animals to seamlessly move between them Objectives: • Connect cougars located in several core areas with cougars in other geographically separated core areas. • Develop physical connections between cougar habitats located in the Santa Susana Mountains with habitats in the Santa Monica Mountains, the San Gabriel Mountains, and in the Los Padres National Forest.

MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS) Onboard Terra First satellite that provides daily global coverage (250 m resolution) 2,330 km swath width

Many-to-many

Multiple objects of the origin table can be related to multiple objects in the destination table.

Multispectral Images from Landsat 7:

Multispectral imaging allows the examination of: • Single-band images • Natural color composites (True color composites, RGB) • False color composites

Topology rules for line

Must not have dangles: the end of a line must touch any part of one other line or any part of itself within a feature class

Topology rules for polygons

Must not overlap Must not have gaps: requires that polygons must not have a void between them within a feature class.

Examples of Data Applications

NASA satellite images, remote sensing and modeling data, along with other sources of data, are used directly or in statistical or physical modeling tools for a variety of applications.

PPDAC- Plan Phase

Nature of project • Costing requirements • Decision-support requirements • Public participation issues • Operational requirements • Timing and critical dates • Funding and resourcing • Feasibility and risk analysis • Client expectations • Specifications and standards • Data • Sampling • Handling exceptions and rare

Distance based point pattern measures

Nearest neighbor Ripleys K function

Ground Control or Targets

Necessary when you wish to add scale or Georeferencing your image. Scale allows you to determine area, elevation and length Georeferencing allows you to associate your imagery to other spatial datasets Using targets requires a survey grade GPS, much more precise than the GPS in your cell phone The targets should be visible in the imagery

Experimental Variogram

Need to estimate semivariance at lag h distances.

neighborhood functions

Neighborhood tools create output values for each cell location based on the location value and the values identified in a specified neighborhood. The neighborhood can be of two types: Block statistic: neighborhoods do not overlap Focal statistics: neighborhoods overlap

Imagining the GIS of the Future

No limits on data storage and computing • Public Image sharing • Ready to use analytics • Hyperlocal custom missions • Custom satellite missions • Custom satellite missions

nonselective scattering

Non-selective scattering is produced when there are particles in the atmosphere several times the diameter of the radiation being transmitted. This type of scattering is non-selective, i.e. all wavelengths of light are scattered, not just blue, green, or red. Thus, water droplets, which make up clouds and fog banks, scatter all wavelengths of visible light equally well, causing the cloud to appear white

Types of Accuracy Assessment

Non-site specific accuracy • Site Specific accuracy Non-site specific accuracy = Inventory Error • Site Specific accuracy = Classification Error Important Considerations: (1) What sampling scheme should be used? (2) How many samples should be collected?

Non-Site Specific Accuracy

Non-site-specific accuracy. Here two images are compared only on the basis of total areas in each category. Because total areas may be similar even though placement of the boundaries differs greatly, this approach can give misleading results, as shown here.

Spatial density

Number of events/ unit area

Atmospheric energy-matter interactions

Once electromagnetic radiation is generated, it is propagated through the earth's atmosphere almost at the speed of light in a vacuum. Unlike a vacuum in which nothing happens, the atmosphere may affect: • Speed of radiation • Wavelength • Intensity • Spectral distribution, and/or direction

Hyperspectral Data

One healthy (#37) and three stressed Bahia grass image-derived endmember spectral profiles (#15, 25 and 36) derived using a pixel purity index image and n-dimensional visualization endmember analysis

Catchment Delineation using GIS

Open source GIS software: grass GIS Commercial GIS software: esri Digital Elevation Model (DEM): The representation of continuous elevation values over a topographic surface by a regular array of z-values, referenced to a common vertical datum

Products

Optional Step 5 Generate derivative products: • Digital Surface Model • Orthomosaic for texture mapping

Regression Models - OLS

Ordinary Least Squares (OLS) model is the best known of all regression techniques. It provides a global model of the variable or process you are trying to understand or predict. It creates a single regression equation to represent that process. Minimize sum of squared errors (or residuals) When the relationship is positive, the sign for the associated coefficient is also positive. Coefficients for negative relationships have negative signs. When the relationship is a strong one, the coefficient is relatively large (relative to the units of the explanatory variable it is associated with). Weak relationships are associated with coefficients near zero; β0 is the regression intercept. It represents the expected value for the dependent variable if all the independent (explanatory) variables are zero.

Information systems

Organizing and storing data, accessing and retrieving data, manipulating and synthesizing

Extract by attribute

OutRas = ExtractByAttributes(InRas1, "Value > 0" )

Deterministic processes

Outcome is completely and exactly known based on known input. No randomness.

Stochastic processes

Outcome is subject to variation that cannot be given precisely by mathematical function. Included randomness.

Weighted overlay

Overlays several rasters using a common measurement scale and weights each according to its importance Find an area of suitable land use, such as vacant land, in a neighborhood of high population density to provide green space in crowded areas that are not already served by an existing park

Sample Size based on Binomial Probability

P: Expected percent accuracy of the entire map q = 100 - p E: allowable error Z = 2 for the standard normal deviate of 1.96 for the 95% confidence interval If expected percent accuracy is 85% and an allowable error is 5%, the total number of points necessary for a reliable results is: If allowable error is 10%

Analytical Methodologies for Spatial Modeling

PPDAC: Problem; Plan; Data; Analysis; and Conclusions

Factors Affecting Classification Accuracy

Parcel size and shape • Number of classes • Spectral-radiometric "contrast" with surrounding pixels • Adequacy of training data • Quality of reference data

temporal autocorrelation

Pattern of values recorded and graphed might show that rainfall, or commodity prices, exhibits regularity over time.

Conditional statement

Performs a conditional if/else evaluation on each of the input cells of an input raster.

UAVs

Phantom, Sensefly, Mavic Air 2

Vector to Raster Conversion

Point, line, or polygon features can be converted to a raster dataset. Cell size impacts the results of the analysis

Topology rules for points

Points in one feature class must be coincident with points in another feature class.

Discrete objects

Points, lines, polygons (represents objects as well-defined boundaries)

Airworthiness

Pre-flight inspection • Mechanical condition • Software version • Control software feedback • Changes since last flight • Battery health • Range check • Logbook (notate most of the list above) Quick flight test

Inexact interpolators

Predict a value that is different from the measured value at the sample point.

Exact interpolators

Prediction at a sample point is identical to what is measured at that point.

Thiessen Polygons

Predictions at every unsampled point are provided by the nearest data point using the proximity polygons. Any location within a thiessen polygon is closer to its associated point than to any other point. Simple, does not produce a continuous field of estimates. There are abrupt jumps at the edges.

N-Line Striping

Presence of systematic noticeable lines in the image as a result of a maladjusted detector

PPDAC- Problem Phase

Problem definition • Clear definition of problem/project and (client) expectations is paramount • Desk research/literature review • Identification of specific spatial issues: Spatial (and statistical) scale factors Data availability and quality Spatial grouping issues

Spatial interpolation

Process of estimating the value of a continuous attribute at places where the attribute is not exactly measured. Underlying principle is Toblers law

basemap

Project item that is often displayed under the content to provide a geographic context to the maps operational layers

Scene

Project item used to display and work with geographic data in 3D.

Map

Project item used to display and work with geographic data in two dimensions.

Nominal scale

Provides descriptive information about and object, no implied order/size (vegetation type, city name)

Small Multispectral Systems

Purpose • Quantify light reflectance response in multiple discrete bands • Includes bands outside of the visible spectrum • Commonly NIR (700 - 900nm) • Quantify relationships with spectral indices •TetraCam Micro-MCA6 -Weight: 530 g (1.16 lbs) -Spectral range: 380 - 1000 nm •Bandwidth: 10 nm •6 Bands are user configurable -Shutter: Global shutter -Sensor: 1280 x 1024 (1.3 mp) -FOV @ 122 m: 84 x 67 m -GSD @ 122 m: 6.6 cm -Cost : $13,495 MicaSense RedEdge • Weight: 150g (0.33 lbs.) • Spectral Range: 460 - 850 nm • 5 Bands: 480, 560, 670,720 & 830 nm • Bandwidth: ~20 nm • Sensor: 1280 x 960 (1.2 mp) • Shutter: Global shutter • FOV @ 120 m: 102 m x 77 m • GSD @ 120 m: 8 cm Cost: $ 5,900

Digital Image Analysis

Quality of remote sensing data varies greatly. Variety of factors impact quality of remotely sensed images such as sensors, hazy atmosphere, .... Image preprocessing refers to a sets of operations that are typically taken before an image is further manipulated and information is derived from the data: • Classification • Change detection

Geostatistical Interpolation method

Quantify the spatial autocorrelation among measured points and account for the spatial configuration of the sample points around the prediction location

QQ Plot

Quantile-quantile is explanatory tool to assess the similarity between distribution of one numeric variable and a normal distribution, or between distribution of two numeric variables 1) order data values and compute cumulative distribution values as (I-0.5)/n for the other ordered value out of n (gives proportion) 2) produce CDF graph by plotting the ordered data versus the cumulative distribution values 3) do the same process for standard normal distribution 4) pair data values corresponding to specific quantiles and plot in QQ.

Building simple queries

Queries are among most common operations of DBMS, built to select a subset of records based on the values of specified attributes

Radiance

Radiance (L) is the radiant flux per unit solid angle leaving an extended source in a given direction per unit projected source area in that direction and is measured in watts per meter squared per steradian (W m-2 sr -1 ). We are only interested in the radiant flux in certain wavelengths (L) leaving the projected source area (A) within a certain direction () and solid angle ():

Radiometric Error Caused by the Sensor:

Radiometric error can be introduced by the sensor system when the detectors do not function properly or are improperly calibrated. Common radiometric errors are: • Random bad pixels (shot noise) • Line-start/stop problems • Line or column drop-outs • Line or column striping

Radiometric Preprocessing

Radiometric preprocessing influences the brightness values of an image to: • Correct for sensor malfunctions • Adjust the values to compensate for atmospheric degradation

Reflectance

Reflectance is the process whereby radiation "bounces off" an object like a cloud or the terrain. Actually, the process is more complicated, involving re-radiation of photons in unison by atoms or molecules in a layer one-half wavelength deep Typical spectral reflectance curves for urban-suburban phenomena in the region 0.4 - 0.9 µm.

Regression Models

Regression analysis is the term used to describe a family of methods that seek to model the relationship between one (or more) dependent or response variables and a number of independent or predictor variables You may want to understand why people are persistently dying young in certain regions of the country or what factors contribute to higher than expected rates of diabetes. Analysis of 911 emergency call data showing call hot spots (red), call cold spots (blue), and locations of the fire/police units responsible for responding (green crosses) Regression analysis allows you to model, examine, and explore spatial relationships and can help explain the factors behind observed spatial patterns. By modeling spatial relationships, regression analysis can also be used for prediction. One (or more) dependent (response) variables One or more independent (predictor) variables Linear regression is linear in coefficients: Typical assumptions: • Data are independent random samples from an underlying population • Model is valid and meaningful (in form and statistical) • Errors are iid: identical and Independently distributed • Error distribution is normal

Remote sensing and gis

Relationship of the GISciences to Mathematics, Physical, Biological, and Social Sciences

Key remote sensing concepts

Remote sensing is a tool or technique similar to mathematics. 1. Using sensors to measure the amount of electromagnetic radiation (EMR) exiting an object or geographic area from a distance. 2. Extracting valuable information from the data using mathematically and statistically based algorithms. 3. It functions in harmony with other spatial data-collection techniques or tools of the mapping sciences, including cartography and geographic information systems (GIS)

Heat Maps

Represent point features as a surface of relative density. High intensity=red Low intensity=blue

Layer

Representation of spatial data in a map or scene.

Error in Predictions

Residual measures the difference between a data point and the corresponding model estimate. Residuals can be positive or negative, sum of residuals is not a good measure of overall error.

Scale

Results obtained at one scale do not necessarily apply at other scales. Scale is always very important

Factors affecting reliability of experimental variograms

Sample size, sampling interval and spatial scale, lag interval, statistical distribution, anisotropy, trend

Scale

Scale can be expressed as: • Verbal Scale: e.g. 1-in. on the air photo equals 2,000-ft. in the real world • Representative Fraction (RF) (dimensionless): e.g. 1/2,000 or 1:24,000 • Graphic Scale: Large scale photos: small value in the denominator of the reference fraction Typically: RF 1:20,000 Medium scale photos: Typically: RF between 1:20,000 and 1:100,000 Small scale photos: Large denominator in RF Typically: RF 1:100,000

Scattering

Scatter differs from reflection in that the direction associated with scattering is unpredictable, whereas the direction of reflection is predictable. There are three types of scattering: • Rayleigh • Mie • Non-selective Type of scattering is a function of: • the wavelength of the incident radiant energy, and • the size of the gas molecule, dust particle, and/or water vapor droplet encountered

Topology

Science and mathematics of relationships used to validate geometries of feature datasets. How point, line, and polygon features share geometry. Define and enforce data integrity rules: no gaps should exist between polygons, no overlapping features. Support sophisticated editing tools Construct features from unconstructed geometry, create polygons from lines.

What do environmental scientists do?

Scientists formulate hypotheses and then attempt to accept or reject them in a systematic, unbiased fashion by collecting data. 1) Data may be directly collected in the field (in situ data collection) time-consuming, expensive, and inaccurate process 2) Data may be obtained at some remote distance from the subject matter (remote sensing

Select

Select layer by attributes: selects features based on attribute values. Select layer by location: selects features based on their relationship to other features (within or outside of feature class)

Advantages to Remote Sensing

Selected reasons why remote sensing of the environment is important: • Aerial perspective at global, national/regional, and local scales; • Historical imagery can document change which can help us to understand the human and/or physical processes at work; • Obtain knowledge beyond our human visual perception (day/night; in inclement weather); • Information extraction: 3-dimensional terrain characteristics land-use/land-cover biophysical properties

Sensors

Sensors can be used to obtain • specific information about an object (diameter of a tree crown) • geographic extent of a phenomenon ( boundary of a forest) The Electromagnetic energy reflected, emitted, or back-scattered from an object or geographic area is used as a surrogate for the actual property under investigation

radial basis function

Series of exact interpolator techniques: thin-plate spline, spline w tension, completely regularized spline, multiquadric function, inverse mulitquadric. Produce smooth surfaces from large number. Inappropriate when large changes in the surface occur within short distances.

What can we compare SfM to?

SfMotion is not the only way to create a georeferenced point cloud like the one on the previous slide. The other option is using LiDAR. LiDAR laser scans an area of interest to create a 3D point cloud. Two example platforms for LiDAR are shown

Standard deviation classification

Shows how much a features attribute value varies from the mean

Color

Sir Isaac Newton discovered that white light could be dispersed into its spectral components by passing it through a prism

Site Specific Accuracy

Site-specific accuracy is based on the detailed assessment of agreement between the map and reference data at specific locations.

Database Management System (DBMS)

Software application designed to organize the efficient and effective storage and access data

Clustered

Some locations more likely than others. Can be produced by first-order and second-order. Attractive

Random Bad Pixels

Sometimes a detector does not record spectral data for an individual pixel. When this occurs randomly, it is called a bad pixel. You can use a threshold algorithm to find bad pixels. Use neighborhood functions to estimate the value of a missing pixel.

Shadow

Sometimes it can provide information about the object height

Non-uniformity of space

Space is not uniform. Particular patterns in data is simply a result of where people live and work.

Critical issues in spatial analysis

Spatial autocorrelation, modifiable areal unit problem, scale, ecological fallacy, non-uniformity of space, edge issues

Modifiable Areal Unit Problem (MAUP)

Spatial data are often aggregates Aggregation units are often defined arbitrarily and result may depend on specific geographic unit: province or county; county or city.

Spatial Filtering

Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is based on the number changes in brightness values over a given area A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries.

Spatial Regression Models

Spatial regression methods are similar, but take explicit account of the spatial structure of data. • Positive spatial autocorrelation is the norm, hence dependence between samples exists • Datasets often non-Normal >> transformations may be required (Log, Box-Cox, Logistic) • Samples are often clustered >> spatial declustering may be required • Spatial coordinates (x,y) may form part of the modelling process Spatial data exhibits two properties that make it difficult (but not impossible) to meet the assumptions and requirements of traditional (nonspatial) statistical methods, like OLS regression: • Geographic features are often spatially autocorrelated. This creates an overcount type of bias for traditional (nonspatial) regression methods. • Processes behave often differently in different parts of the study area. This characteristic of spatial data can be referred to as regional variation or non-stationarity. True spatial regression methods were developed to robustly manage these two characteristics of spatial data.

Cardinality of the relationship between tables

Specifies the number of objects in the origin class that can relate to a number of objects in the destination class.

Constructing Variogram

Squared height difference= semivariance.

tone and color

Stand of pine (evergreen) surrounded by hardwoods (Black and white Infrared Image) Vegetation is dark, fallow is bright, turbid water is gray

Ecological fallacy

Statistical relationship observed at one level of aggregation is held at more detailed level. Crime rate data shows strong relationship between income and crime rate, but doesn't mean that lower income individuals are likely to commit a crime.

Matching features

Step 1 Match corresponding features and measure distances between them on the camera image plane d, d' The Scale-Invariant Feature Transform is key to matching corresponding features despite varying distances

Find camera locations

Step 2 When we have the matching locations of multiple points on two or more photos, there is usually just one mathematical solution for where the photos were taken. Therefore, we can calculate individual camera positions (x, y, z), (x', y', z'), orientations i, i', focal lengths f, f', and relative positions of corresponding features b, h, in a single step known as "bundle adjustment."

georectification

Step 4 Georectification means converting the point cloud from an internal, arbitrary coordinate system into a geographical coordinate system. This can be achieved in one of two ways Step 4 Georectification means converting the point cloud from an internal, arbitrary coordinate system into a geographical coordinate system. This can be achieved in one of two ways: • directly, with knowledge of the camera positions and focal lengths • indirectly, by incorporating a few ground control points (GCPs) with known coordinates. Typically these would be surveyed using differential GPS.

File geodatabase

Stored as multiple files in a folder. Each dataset in a single file. Default grow to 1TB, but changed to 4 to 255 TB using configuration keyword. Native structure for ArcGIS File folder with .gdb at the end of its name.

Sampling Design for Accuracy Assessment

Stratified systematic design with sections as sampling units Combinations of systematic and random sampling are often employed

Find the stream network

Stream networks can be delineated from a digital elevation model (DEM) using the output from the Flow Accumulation tool and applying a threshold value

Structure-From-Motion(SfM)

Structure-from-Motion photogrammetry is an emerging technique used to create 3D point clouds with associated color for the area of interest. The key things to notice here are that the camera needs to continually be moving (no two photos taken from the same location) and the photographs need to include the same features, so that features are in multiple photographs. Scientists use this for many applications 3D model are captured by multiple images, not just one

Digital Elevation Model (DEM)

Surfaces are usually modeled with raster datasets. Each cell in the matrix represents a square unit of area and contains a numeric value that is a measurement or estimate of elevation for that location A raster is a matrix of cells, also called pixels, organized in rows and columns

Logistic Regression Model- Example

Survival in the Donner party. In 1846 the Donner and Reed family travelling by covered wagon got stuck in a snow storm in October in the Sierra Nevada. By the next April when they were rescued, 40 of the 87 people had died from starvation and cold stress. Anthropologists considered mortality in the 45 people aged more than 15 years to investigate whether females are better able to withstand harsh conditions than man. Indicating that a 30 year-old male had a 0.33 chance of surviving the winter

Texture

Texture is the characteristic placement and arrangement of repetitions of tone or color in an image. In some images, the scale of the image impact the texture

Hillshade

The Hillshade tool obtains the hypothetical illumination of a surface by determining illumination values for each cell in a raster

5) Assess Model Bias

The Jarque-Bera statistic indicates whether or not the residuals (the observed/known dependent variable values minus the predicted/estimated values) are normally distributed.

4) Assess Stationarity

The Koenker (BP) Statistic (Koenker's studentized Bruesch-Pagan statistic) is a test to determine whether the explanatory variables in the model have a consistent relationship to the dependent variable both in geographic space and in data space. When the model is consistent in geographic space, the spatial processes represented by the explanatory variables behave the same everywhere in the study area (the processes are stationary). When the model is consistent in data space, the variation in the relationship between predicted values and each explanatory variable does not change with changes in explanatory variable magnitudes (there is no heteroscedasticity in the model).

Radiant intensity of the sun

The Sun approximates a 6,000 K blackbody with a dominant wavelength of 0.48 µm. Earth approximates a 300 K blackbody with a dominant wavelength of 9.66 µm. 41% of Sun's energy is in the visible region from 0.4 - 0.7 µm. The other 59% of the energy is in wavelengths shorter than blue light and longer than red light. Remote sensor detectors can be made sensitive to energy in the non-visible regions of the spectrum

Electromagnetic soectrum

The Sun produces a continuous spectrum of energy from gamma rays to radio waves that continually bathe the Earth in energy The visible portion of the spectrum may be measured using wavelength (measured in micrometers or nanometers, i.e., mm or nm) or electron volts (eV). All units are interchangeable

Zonal functions

The Zonal tools allow you to perform analysis where the output is a result of computations performed on all cells that belong to each input zone. Zonal statistics Calculates statistics on values of a raster within the zones of another dataset

GIS & Imagery

The advent of new computing architectures in the cloud along with new advances in GIS software, have provided massive opportunities for learning and understanding.

Wave Model of Electromagnetic Energy

The amplitude of an electromagnetic wave is the height of the wave crest above the undisturbed position. Successive wave crests are numbered 1, 2, 3, and 4. An observer at the position of the clock records the number of crests that pass by in a second. This frequency is measured in cycles per second, or hertz

photogrammetry

The art and science of obtaining reliable measurements by means of aerial photography. • Analog: Data in hardcopy format (9" x 9" photos or positive transparencies) • Digital: Data in digital format (digitized or digital aerial photograph

Aspect

The aspect identifies the compass direction that the downhill slope faces for each location

Overlapping aerial photographs

The change in position of an object from one photograph to the next caused by the aircraft's motion is referred to as stereoscopic x-parallax. You can use this concept to compute exact height of an object

Radiant Flux Density

The concept of radiant flux density for an area on the surface of the Earth. Irradiance is a measure of the amount of radiant flux incident upon a surface per unit area of the surface measured in watts m-2 . Exitance is a measure of the amount of radiant flux leaving a surface per unit area of the surface measured in watts m-2 .

Metadata Styles

The default metadata style supports providing the information used by the ArcGIS platform. • The Federal Geographic Data Committee's (FGDC) Content Standard for Digital Spatial Metadata (CSDGM) is a well-known metadata content standard that has been used in North America and around the world for many years. • The metadata content standard ISO 19115, Geographic information — Metadata and the implementation specification ISO 19139, Geographic information — Metadata — XML schema implementation, are well-known standards that have been used internationally for many years.

Energy of photons

The energy of quanta photons) ranging from gamma rays to radio waves in the electromagnetic spectrum

Modeling the variograms

The experimental variogram consists of semivariances based on samples and they are subject to error. The next step in variography is to fit a smooth curve or surface to the experimental values to capture principal features of the sequence while ignoring the point-to-point erratic fluctuation.

Georeferencing

The first-order polynomial transformation is commonly used to georeference an image. Use a first-order or affine transformation to shift, scale, and rotate a raster dataset. With a minimum of three control points, the mathematical equation used with a first- order transformation can exactly map each raster point to the target location.

Raster file formats

The geodatabase is the native data model in ArcGIS for storing geographic information, including raster datasets, mosaic datasets, and raster catalogs. There are many file formats you can work with that are maintained outside a geodatabase

Rayleigh scattering

The intensity of Rayleigh scattering varies inversely with the fourth power of the wavelength . Rayleigh scattering is responsible for the blue sky

Radiometric Errors Caused by the Atmosphere

The intensity of reflected and emitted radiation to space is influenced by the surface and atmospheric conditions. Thus, satellite measurements contain information about the surface and atmospheric conditions.

Local functions

The local tools are those where the value at each cell location on the output raster is a function of the values from all the inputs at that location. Arithmetic and Boolean are examples of local functions

Ordinary Kriging

The most common type of Kriging methods. • It needs sampling points, their locations and attributes and a variogram • Assumes that the variation is random and spatially dependent. • The underlying random process is intrinsically stationary with constant mean. • Variance depends only on separation in distance and direction between places and not on absolute position. To ensure that the estimate is unbiased the weights are made to sum to 1

Geographic Coordinate System

The most powerful systems of georeferencing for accurate measurement of position and distance between between pairs of locations, based on longitude and latitude, spherical recorded in DMS or DD

Orthomosaic

The orthomosaic is a high-resolution photograph of the area created by associating color with each point in the model. This can be used to view the features on the surface.

Part 107 small Unmanned Aircraft Systems

The part 107 small Unmanned Aircraft Systems (sUAS) course describes the certification and operational requirements to operate sUAS in the National Airspace System (NAS) under Title 14 of the Code of Federal Regulations (14 CFR) part 107, small Unmanned Aircraft Systems • Does not permit flying over people or near people in a manner that puts them at risk • No flying above 400 ft without additional FAA authorization • < 100 mph • < 55 lbs total weight • No flying beyond the line of sight or at night

Dispersed

The presence of one point may make others less likely in its vicinity. Only produced by 2nd-order. Competition

Remote sensing process

The remote sensing data-collection and analysis procedures used for Earth resource applications are often implemented in a systematic fashion referred to as the remote sensing process.

Error Matrix

The standard form for reporting site-specific error is the error matrix (confusion matrix). It identifies overall errors for each category as well as misclassifications (due to confusion between categories) by category. Compilation of an error matrix is required for any serious study of accuracy. It consists of an n × n array, where n represents the number of categories. To construct the error matrix, the analyst must compare two sources of data— the reference samples and the classified image—on a point-by-point basis to determine exactly how each of the validation samples is represented in the classification. Errors of omission are, for example, the assignment of pixels of forest on the ground to non-forest on the map (in other words, an area of "real" forest on the ground has been omitted from the map). Error of commission would be to assign an area of non-forest on the ground to the forest category on the map.

supervised classification

The user selects representative sites for each land cover class in the image. These sites are called training samples. A training sample has location information (point or polygon) and associated land cover class. The image classification algorithm uses the training samples to identify the land cover classes in the entire image

Scale of aerial photographs- flat terrain

There are 2 methods for determining the scale of aerial photographs: 1. Ratio between photo distance and ground distance: f/H = 12"/12000"=1/1000 • Focal Length of the camera (f) • Flying Height of the aircraft (H) 2. Computing distances measured on the air photo (ab) and distances found in the real world (AB)

Sources of electromagnetic energy

Thermonuclear fusion taking place on the surface of the Sun yields a continuous spectrum of electromagnetic energy. The 5770 - 6000 kelvin (K) temperature of this process produces a large amount of relatively short wavelength energy that travels through the vacuum of space at the speed of light. Some of this energy is intercepted by the Earth, where it interacts with the atmosphere and surface materials. The Earth reflects some of the energy directly back out to space or it may absorb the short wavelength energy and then re-emit it at a longer wavelength

External Geometric Error

These errors are caused as a result of random movement of the aircraft. This involves: 1) Altitude change 2) Attitude change due to roll, pitch, yaw

Conceptual Models

These models are static and they describe the present condition of a system. They describe the following information about a system: • Geometry • System stresses • Boundaries • Various components and interactions

Mathematical models

They translate the conceptual model into a series of mathematical equations that represents various reactions and interactions between variables.

Local interpolation methods

Thiessen polygons, inverse distance weighting, spline. Based on: 1) defining a search area or neighborhood around point to be predicted. 2) identifying data points in that neighborhood. 3) choosing a mathematical function that describes variation over the limited data points. 4) evaluate

Variogram model function

Three most popular variograms: • Power function (unbounded) • Spherical (bounded) • Exponential (asymptotically bounded)

Variogram

To characterize spatial autocorrelation across a surface sampled. Accurate estimates of variograms are needed for reliable prediction by kriging.

Complex KDE

To estimate local density by weighting nearby events more heavily than the distant points

3D perception

To obtain this 3D effect from aerial photographs or images, we need to obtain 2 photographs or images of the terrain from two slightly different vintage points

Inverse-distance weighting method

To predict a value for any unmeasured location, uses the measured values surrounding the prediction location. The measured values closest to the prediction location have more influence on the predicted value than those further away. To compute sample mean at unknown location, weights the sampling location based on their proximity to the unknown location in a way that nearer locations are given more weights. Average over observed values weighted by distance. Weights proportional to the inverse of the distance raised to power of p. As distance increases, weights decrease rapidly. The rate at which the weights decrease is dependent on the value of p. Shape of neighborhood restricts how far and where to look for the measured values to be used in prediction. If no directional influence=points equally If directional influence=adjust shape of search neighborhood to ellipse with major axis parallel with direction. Exact interpolator. Predicted values always bounded by min and max. May produce counterintuitive results. Sensitive to clustering and outliers.

Map projections

Transforms a position on earths surface identified by lat and long into Cartesian coordinates, paper is flat and is used as medium, rasters are flat, photographic film is flat, no distortion where surface touches earth

Image to Image Registration

Translation and rotation alignment process by which two images of like geometry and same geographic area are positioned coincident with respect to one another. In this method, a georeferenced image is used as a reference. The problem is that the same geometric errors in the base image will be translated to the new image.

Independent random process (IRP)

Two conditions: 1) any even has equal probability of being in any position on the map or each area of the map has an equal chance of receiving an event. 2) the positioning of an event is independent of the positioning of any other event (independence)

Communication

UAS communication is critical in terms of: • Mission requirements • Safety Colomina and Molina.2014. ISPRS Many communication technologies are used in today's UAS, the most predominant of which in the Mini UAS category is Wi-Fi (usually around 2.4 GHz

UAS History

UAS were born and raised in the military context. Mapping potential was already understood in by the research groups in the late 1970s

Model Calibration

Underestimates peaks • Over-estimates low flows • On average is about correct! To find a parameter set (θ) so that the model most closely simulates the behaviour of the catchment

Ratio scale

Used for numeric items where ratios between two values have definite meaning, derived relative to a fixed zero point on a linear scale.

Spline

Used interpolation method that estimates values using math function that minimizes overall surface curvature, resulting in smooth passing through point exactly. Two conditions: 1) surface must pass exactly through data points. 2) minimum curvature. Cumulative sum of the squares of the second derivative terms of of the surface taken over each point must be minimum.

Global Positioning System (GPS)

Uses known reference points (base stations) on the Earth to provide corrections for unknown points. The base station has a continuously collected known location. When the receiver of the GPS communicates with the base station, your location will be known to cm precision

Mapping the Internet of Things

Utility of real-time data in GIS Today most everything that exists or moves on the planet (and above and below it) is measured in real time. The real-time GIS capabilities of the ArcGIS platform have transformed how information is utilized during any given situation

Anisotropy- variogram experiment

Variation can vary from one direction to another, anisotropic. Anisotropy is such that it could be made isotopic by a simple linear transformation of the spatial coordinates.

P-value

Very small means it is unlikely that the observed spatial pattern is the result of a random process, so reject the null.

PPDAC Process

Views spatial analysis in a broader context • Modeling process is highly iterative • Analysis is just part of the process • Spatial data sourcing and quality • Spatial focus - spatial relationships and spatial dependence • Spatio-temporal focus

Enterprise geodatabase

Virtually unlimited in size and number of users. Based on Oracle, Microsoft, IBM, DB2

Image interpolation

Visual interpretation of images for the purpose of identifying objects and judging their significance

Line Charts

Visualize change over a continuous range(time/distance) Overall trend , multiple trends simultaneously

Histogram

Visually summarize the distribution of a continuous numeric variable by measuring the frequency at which certain values appear in the dataset.

Trend- experimental variogram

We can always calculate the experimental variogram, but it estimates the theoretical variogram y(h) only when random. If there is a trend in the data, the semivariance equation gives a false summary of the random part.

Web Mercator Projection

Web mapping services with requirements: 1) accurate enough to support distance calculations when routing vehicles. 2) fast to compute. 3) conformal so that local scale is the same in all directions.

NDVI

When plants are stressed, the level of the NIR radiation that they reflect immediately drops. A Normalized Difference Vegetation Index (NDVI) provides a graphical way to quantify small changes in multi-spectral image color relationships. These images consist of pixels with values determined by the formula, (NIR -RED)/(NIR +RED) Where healthy vegetation dominates the scene, the NDVI formula approaches (NIR/NIR) or +1 in value. Where there is an absence of NIR-reflecting vegetation, the NDVI value approaches (-RED/+RED) or -1 in value

Geometric Transformation Function

When you've created enough control points, you can transform the raster dataset to the map coordinates of the target data using a mathematical function.

Lag interval- experimental variogram

Where data on a regular grid or at equal intervals on transects the natural step is one interval. Where they are irregularly scattered, the practitioner must choose the step, h, and the limits, w, within which the squares differences are averaged for each step.

1) Assess Model Performance

Your model (your explanatory variables modeled using linear regression) explains approximately 39 percent of the variation in the dependent variable.

Zonal effect

Zones or grouping schemes that one uses for data analysis can also be an issue, even if the units are all the same scale. May use zones with similar size and number of units, but different boundaries.

Nugget Variance

arises from measurement error at h = 0

Tobler's First Law of Geography

everything is related to everything else, but near things are more related than distant things

Automating Your Geoprocessing Work:

f ArcGIS Pro does not include a tool which can answer your specific question, you can build your own custom tool. Toolbox in a folder connection containing several script tools Using geoprocessing tools and other functions as building blocks, you can create simple or complex workflows as models or scripts. Models and scripts help you to automate multi-step processes as well as document processing steps so the workflow can be better understood, examined, and refined.

Which Variogram model?

f your models have the same number of parameters and the ones fitted seem to fit well, choose the model with the smallest residual sum of squares (RSS). You can always diminish the RSS by increasing the number of parameters in the fitted model. To ensure parsimony, we can compute an estimate of the Akaike Information Criterion (AIC)

positive spatial autocorrelation

features that are similar in location are also similar in attributes

Range

is the limit of spatial correlation where the autocorrelation becomes 0

Akaike information criterion (AIC)

n: the number of points on the variogram (16 in this example), p: is the number of model parameters R: is the mean square of the residuals (RMS) When comparing multiple variograms, the first term on the right is constant so we compute The best model is the one with the smallest A.

Vector Data

points, lines, polygons

Variogram model function- exponential

where a is the distance parameter. This function approaches its sill asymptotically, and so it does not have a finite range Experimental variograms computed in four directions: a pH; the solid line is the isotropic exponential model and the dotted lines form the envelope of the fitted anisotropic exponential

Variogram model function - spherical

where c0 is the nugget variance, c is the variance of spatially correlated component and r is the range of spatial dependence.

Mathematical Function

you collect more control points, you can fit a higher order polynomial. The higher the transformation order, the more complex the distortion that can be corrected.

Reasons why photo/image interpretation are powerful scientific tools:

• Aerial/regional perspective • Three-dimensional depth perception • Ability to obtain knowledge beyond human visual perception • Ability to obtain a historical image record to document change

Logical operators

• Boolean And • Boolean Not • Boolean Or • Boolean Xor • Greater than • Greater Than Equal

Manual catchment delineation

• Catchments can be delineated by hand. • Cumbersome, error-prone. • More fun and efficient to design algorithms that do it for us. • Typically, people use software (e.g. ArcGIS) to do that

Sensors

• Electro-optical (Visual) • Multispectral • Hyperspectral • Infrared (Long wave) • LIDAR

EO Sensors

• Emulate human eye • Tuned to visible light spectrum • Image color is intuitive • Many Choices from $300 - $60,000 • Suggestions: • Canon S100 ($300) - 12 mp, large sensor, light body • Sony A5100 ($400 - $700) - 24 mp, large sensor, light body • GoPro Hero 4 ($500) - 12 mp, durable, small • Sony A7 ($1200) - 36 mp, large FOV, lightest full frame • PhaseOne IXU 180 (>$50,000) - 80 mp, aerial survey, 2 lbs. • Application • Surface modeling • Purpose • Estimate biomass • Forest inventory

Site Considerations

• Forecasts and observations • Rapid weather change • Weather and aircraft performance/limitations

Ground Control Points

• GCPs should have high contrast in all images of interest • GCPs should be easily identifiable on the ground and on the image • GCPs should be unchanging over time • GCPs should have a good coverage of an area of interest

Platform Selection

• How large is the area of interest? • What is financially feasible? • How large is the intended camera? • Is the survey collection path accessible or will a UAS need to be used? • What additional components are needed and does the field area support these? (i.e., batteries, specific weather

Technologies for obtaining DEM

• In situ surveying • Photogrammetry • Interferometric Synthetic Aperture Radar (IFSAR) • Light Detection and Ranging (LIDAR)

Limitations of remote sensing

• It is often oversold. Remote sensing is not a panacea that provides all the information needed to conduct physical, biological, or social science research. • Human method-produced error may be introduced as the remote sensing instrument and mission parameters are specified. • Powerful active remote sensor systems that emit their own electromagnetic radiation (e.g., LIDAR, RADAR, SONAR) can be intrusive and affect the phenomenon being investigated. • Remote sensing instruments may become uncalibrated, resulting in uncalibrated remote sensor data. • Remote sensor data may be expensive to collect and analyze. Hopefully, the information extracted from the remote sensor data justifies the expense. • The people who analyze the data are often the most expensive part of the remote sensing system

Useful raster functions

• Local functions • Neighborhood functions • Zonal functions

Elements of image interpolation

• Location • Tone/color • Size • Shape • Texture • Pattern • Shadow • Height/Depth/Volume • Slope/Aspect • Site situation • Association

Benefits of SfM

• Low-cost photogrammetric method for high resolution topographic reconstruction • Suitable for low budget research • Application in remote areas Westoby et al.2012. Geomorphology • It differs fundamentally from conventional photogrammetry, in that the geometry of the scene, camera positions and orientation is solved automatically without the need to specify a priori, a network of targets which have known 3-D positions. • The new generation of image matching algorithms allow for unstructured image acquisition

Equipment List

• Platform: pole, kite, balloon, UAS • Camera • Camera mount: how are you going to attach the camera to the platform? • Targets if georeferencing • GPS system if georeferencing • Scale bars as an alternative to georeferencing • Extra SD cards and batteries for camera • Extra supplies for platform (helium for balloon, batteries for UAS) if necessary

Explanatory Variables:

• Population • Jobs • Education • Distance to urban centers • Renters • Employment status What are the explanatory variables that could explain the observed patterns in 911 calls?

Creating Flow Direction

• Principle: assigns a flow direction code to each cell, based on the steepest downhill slope as defined by the DEM. • 8 possible direction codes indicating the cells towards which the water flows. • Does not work for depressions. These have to be filled beforehand Creates a raster of flow direction from each cell to its downslope neighbor, or neighbors, using: • D8 • Multiple Flow Direction (MFD) • D-Infinity (DINF) methods24

Small Hyperspectral Systems

• Quantify light reflectance • Many narrow (< 10 nm) spectral bands • Visualize phenomena that are not apparent to the human eye • Quantify relationships with spectral indices (Blackburn et al. 1998, Chuvieco 2002). Headwall Hyperspec-Nano • Weight: 680 g (1.5 lbs.) • Various lens focal lengths • Customized FOV and GSD • Spectral Range: 400 - 1000 nm • Bandwidth: ~5 nm • Sensor: Pushbroom • FOV @ 120 m w/8 mm lens: 70m • GSD @ 120 m w/ 8mm lens: 22 cm Cost > $80,000 Applications • Forest health (Pontius et al. 2008) • Mineral detection (Neville et al. 2003) • Image classification (Chen et al. 2011) • Vineyard vigor assessment (Zarco-Tejada 2013) • Vegetation water stress (Zarco-Tejada 2012) • Photosynthetic Productivity (e.g. fluorescence

Aeronautical Decision Making (ADM)

• Remote pilot operator or ground control operator should communicate with visual observers about anticipated safe flight conditions. • All human involvement should be able to make call to return to safe conditions. • Ground control operator should be able to quickly relay reported UAS feedback concerns to remote pilot operator

Thermal Infared

• Sensitive to heat • Long-Wave IR (LWIR) Spectral Range: ~7500 - 13,500 nm • Not in atmospheric window - Isolated to radiant energy emitted from terrestrial objects • Thermographic - Per-pixel heat energy •FLIR TAU-2 / ICI 9640 -$10,000 - $15,000 -Weight 100 - 400g -Thermographic option -LWIR - emitted thermal • FLIR VUE −Weight 100 - 400g −No thermography −Lower SNR −Turnkey −LWIR - emitted thermal −$3000 (640 x 480 version Applications • Vegetation water stress (Zarco-Tejada 2012

Remote sensor resolutions

• Spatial: the size of the field-of-view, e.g. 10 x 10 m • Spectral: the number and size of spectral regions, e.g. blue, red, thermal • Temporal: how often the sensor collects data • Radiometric: sensitivity of detectors to small differences in electromagnetic energy

The physical rules

• Water flows in the direction of the terrain steepest downhill slope. • Streamlines are orthogonal to the elevation contour lines. • Drainage divides are found along the highest points of the terrain. • Streamlines do not flow towards drainage divides, and do not intersect with them.

Four mile creek differencing (sfm-als

•Another application of SfM is looking at geomorphologic change. In 2013, a major flood occurred in Boulder, Colorado. • Prior to the flood, an ALS survey was conducted of the Four Mile Creek area in North Boulder. After the flood, SfM and TLS surveys were conducted. • "Change detection" is when you take a dataset from two different times and subtract

Small Lidar Sensors

•Light Detection and Ranging (LiDAR) sensors -Range finder -Complex systems can produce 100,000+ points/sec •3D modeling of an environment or subject •Topographic mapping -Precise internal timing and calibration reduces ranging uncertainty ASC Peregrine • Flash LiDAR • Weight: 680 g (1.5 lbs) • 250,000 pulses/sec • Various lens focal lengths • Customized FOV and range Cost: ~$50,000 Velodyne VLP-16 Puck Lite / Hi-Res • Rotating LIDAR • Dual return (echo) • Weight: 590 g • Effective range: 100 m • Vert FOV: 30 degrees • Horiz FOV: 360 degree • 300,000 points/sec • Ground sampling density • Depends on velocity and altitude Cost: ~$8,000 Applications • High detail topographic mapping (Glennie 2013) • Forest inventory (Goerndt 2011) • Damage from seismic activity