Light Detection And Ranging Elevation Research Articles

Generalization of digital elevation models for military passability maps development

Abstract. This paper presents the methodology of the digital elevation model (DEM) generalization which aims to reduce the generation time of the military passability maps. The generalization is based on the local standard deviations of slopes on the analyzed area. It assumes that in areas, where the diversification of relief is low, the elevation points should be reduced, which may significantly increase the efficiency of the process of the military passability maps development. The generalization was performed on the LIDAR (Light Detection and Ranging) elevation data, which can be obtained with no charges from the Polish Head Office of Geodesy and Cartography. The data are in 1 m resolution and in an ASCII grid file, which contains all coordinates (X, Y and H) of elevation points. The conducted research shows that the modification of parameters used in the generalization process (shape and size of primary fields, number of classes and standard deviation threshold) influences the degree of generalization of obtained results. What is more, the generalized DEMs allowed to reduce the processing time of the passability maps by about 3.5 times compared to the non-generalized model. The parameters used for generalization can be adjusted by the user to individual needs which makes the tool universal.

Soil and satellite remote sensing variables importance using machine learning to predict cotton yield

Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R²), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield.

Spatially estimating flooding depths from damage reports

It is important that a sustainable community better prepare for and design mitigation processes for major flooding events, particularly as the climate is non-stationary. In recent years, there have been major storm events in the USA with record amounts of rainfall that some refer to as stalled storms. These stalled storms frequently result in flooding of urban areas which are not subject to riverine or storm surge flooding. This research focuses on using flood damage reports in conjunction with contour maps, geographical information systems, Light Detection and Ranging (LiDAR) data, photographs, and spatial averaging to develop total (high) flood elevation data sets for two neighborhoods for Tropical Storm Imelda in Beaumont, Texas. When various sources of data such as insurance or Federal Emergency Management Agency damage reports, updated LiDAR elevation sets, and coordination data are readily available, this may be an economical method of estimating maximum flood elevations. High-water marks are frequently collected by various agencies as soon as possible after a flooding event, but these data sets might provide even additional information and validation many months or years post an event.

Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest – Case study from Central Europe

Contemporary airborne Light Detection And Ranging (LiDAR) elevation data provide a high-resolution input to quantitative studies within various branches of geomorphological research. Availability of such data played an especially significant role in the recent developments of geomorphometric studies focused on extremely rugged terrains, such as dissected sandstone plateaus. These are the areas of very complex topography, featuring structurally-controlled landforms such as tabular hills, canyons, cleft-and-valley systems, numerous high and near-vertical rock walls, isolated tors and dense boulder slope covers. In this paper we focus on issues of LiDAR data handling for geomorphometric research in Central European sandstone areas in Czechia, Poland and Germany, where the extremely rugged terrains are usually located in the forest and, thus, usability of LiDAR DEM (Digital Elevation Model) products for morphometric studies can be constrained. The impact of forest density on local decrease of ground point density may be considerable, adversely affecting the quality of DEMs, which become unsuitable to represent properly terrain features. Therefore, the role of DEM pre-processing in geomorphometric workflow is emphasized. Using the original point cloud data one can provide a much more detailed terrain representation that includes numerous convex landforms (tors, boulders) which are erroneously filtered-out from ready-to-use DEMs. Results of such procedure are shown explicitly in the form of Elevation Difference maps, as well as on examples of selected morphometric indices such as Topographic Wetness Index, Terrain Ruggedness Index and Morphometric Protection Index. The awareness of qualities of particular LiDAR datasets should be considered crucial while assessing LiDAR data applicability for morphometric studies on rugged erosional landscapes, especially if the study areas are transboundary.

Scalability and performance tradeoffs in quantifying relationships between elevation and tidal wetland plant communities

Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.

Geometry-Based Assessment of Levee Stability and Overtopping Using Airborne LiDAR Altimetry: A Case Study in the Pearl River Delta, Southern China

Levees are normally the last barrier for defending flood water and storm surges in low-lying coastal cities. Levees in a large delta plain were usually constructed in different time and criteria and have been changing with age as well. Fast and quantitative assessment of levee stability is critical but faces many challenges. This study designs a scoring approach to quickly assess levee stability and overtopping threats with geometric parameters from airborne Light Detection and Ranging (LiDAR). An automated procedure is developed to extract levees geometric parameters from 0.5 m grid LiDAR elevation, such as crown height, width and landside slope. The surveyed levee is seated in the Hengmen waterway in the Pearl River Delta, Southern China. Results show that the stability index using the assessment scores is higher than and superior to the common qualified rates adopted in previous studies. The qualified rate is defined as the count percentage that each parameter meets the designed criteria, while the assessment score proposed in this study assigns different credits to those below/above the designed criteria. The continuous crown heights provide detailed information on levee overtopping threats. The crown heights of levee A and B are above the designed elevation and the flood stage (4.5 m) in a 200-year return period. The crown heights of levee C, D and E are generally lower than 4.5 m and vary in a large range on different sections. The middle section of levee E for the harbor and dock area has front elevation slightly below the flood stage (3.54 m) in a 20-year return period. Moreover, the high precision LiDAR altimetry data reveal various morphological modifications in all levees, such as natural subsidence and artificial modifications, which greatly reduce levees safety and are severe threats to the community. The procedures and assessment approach developed in this study can be easily applied for levees fast assessment in the entire Pearl River Delta and somewhere else, thus offer a suitable mitigation suggestion ahead of levee failure or overtopping.

Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis

This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographic and remotely sensed coastal datasets, as demonstrated here using open-access UK Environment Agency datasets. Two core methodologies, used to generate the novel reference dataset, are presented. Firstly, we establish a robust approach to extracting shorelines from vertical aerial photography, validated against LiDAR (Light Detection and Ranging) and coastal topography surveys. Secondly, we present a standard methodology for quantifying sediment volume change from spatially continuous LiDAR elevation datasets. As coastal systems are monitored at greater spatial resolution and temporal frequency there is an unprecedented opportunity to determine how and why coastal systems have changed in the past with a view to informing future forecasting. With revelation of trends that suggest increasing coastal risk, coastal change research is needed to inform the management and protection of coasts.

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

Paleohydraulic Reconstruction of Modern Large Floods at Subcritical Speed in a Confined Valley: Proof of Concept

The present study aims to show the accuracy of paleoflood reconstruction techniques based on two-dimensional (2D) hydraulic modelling of a large flood. Using this reconstruction technique, we determined trends in flood stages over time in a regulated river. A stretch of the Guadalquivir River (Southern Spain) was selected as the study site. High-resolution orthophotos and LiDAR (Light Detection and Ranging) elevations were acquired just after modern floods. They were used for the identification and location of stage indicators. In addition, water gradients were estimated from gauging records, documentary information and paleostage indicators (PSIs) in two situations: (i) pre-vegetation encroachment; and (ii) post-vegetation encroachment due to upstream impoundment. Standard two-dimensional, flow depth-averaged equations over fixed beds were used in the hydraulic modelling. In a first stage, long records of instrumental data at gauging stations and documentary evidence of flood levels served to calibrate the input parameters of the hydraulic model. In a second stage, paleoflood signatures within sedimentary and botanical sequences served to verify the flood stages in the numerical simulations not only at the river reach where instrumental data exist but also in the downstream river reach. Interestingly, the rating curve obtained from the combined use of documentary information and imagery was nearly as accurate as gauging measurements. The thoughtful comparison of 2D modelled hydraulic variables against inferred values from PSIs and instrumental data supports the paleoflood reconstruction method over fixed beds. Vegetation encroachment provoked 10% deeper floods at the water discharge of 2000 m3 · s−1, which implied an increase of Manning’s roughness coefficient from 0.04 to 0.055 s · m−1/3 in less than 15 years.

Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data

Erosion of permafrost coasts has received increasing scientific attention since 1990s because of rapid land loss and the mobilisation potential of old organic carbon. The majority of permafrost coastal erosion studies are limited to time periods from a few years to decades. Most of these studies emphasize the spatial variability of coastal erosion, but the intensity of inter-annual variations, including intermediate coastal aggradation, remains poorly documented. We used repeat airborne Light Detection And Ranging (LiDAR) elevation data from 2012 and 2013 with 1m horizontal resolution to study coastal erosion and accompanying mass-wasting processes in the hinterland. Study sites were selected to include different morphologies along the coast of the Yukon Coastal Plain and on Herschel Island. We studied elevation and volume changes and coastline movement and compared the results between geomorphic units. Results showed simple uniform coastal erosion from low coasts (up to 10m height) and a highly diverse erosion pattern along coasts with higher backshore elevation. This variability was particularly pronounced in the case of active retrogressive thaw slumps, which can decrease coastal erosion or even cause temporary progradation by sediment release. Most of the extremes were recorded in study sites with active slumping (e.g. 22m of coastline retreat and 42m of coastline progradation). Coastline progradation also resulted from the accumulation of slope collapse material. These occasional events can significantly affect the coastline position on a specific date and can affect coastal retreat rates as estimated in long term by coastline digitalisation from air photos and satellite imagery. These deficiencies can be overcome by short-term airborne LiDAR measurements, which provide detailed and high-resolution information about quickly changing elevations in coastal areas.

Upscaling of methane exchange in a boreal forest using soil chamber measurements and high-resolution LiDAR elevation data

Forest soils are generally considered to be net sinks of methane (CH4), but CH4 fluxes vary spatially depending on soil conditions. Measuring CH4 exchange with chambers, which are commonly used for this purpose, might not result in representative fluxes at site scale. Appropriate methods for upscaling CH4 fluxes from point measurements to site scale are therefore needed. At the boreal forest research site, Norunda, chamber measurements of soils and vegetation indicate that the site is a net sink of CH4, while tower gradient measurements indicate that the site is a net source of CH4. We investigated the discrepancy between chamber and tower gradient measurements by upscaling soil CH4 exchange to a 100ha area based on an empirical model derived from chamber measurements of CH4 exchange and measurements of soil moisture, soil temperature and water table depth. A digital elevation model (DEM) derived from high-resolution airborne Light Detection and Ranging (LiDAR) data was used to generate gridded water table depth and soil moisture data of the study area as input data for the upscaling. Despite the simplistic approach, modeled fluxes were significantly correlated to four out of five chambers with R>0.68. The upscaling resulted in a net soil sink of CH4 of −10μmolm−2h−1, averaged over the entire study area and time period (June–September, 2010). Our findings suggest that additional contributions from CH4 soil sources outside the upscaling study area and possibly CH4 emissions from vegetation could explain the net emissions measured by tower gradient measurements.

Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama

Landsat and Shuttle Radar Topography Mission (SRTM) imagery have recently been used to identify broad-scale floristic units in Neotropical rain forests, corresponding to geological formations and their edaphic properties. Little is known about the structural and functional variation between these floristic units, however, and Landsat and SRTM data lack the spectral and spatial resolution needed to provide this information. Imaging spectroscopy and LiDAR (Light Detection and Ranging) have been used to measure canopy structure and function in a variety of ecosystems, but the ability of these technologies to measure differences between compositionally-distinct but otherwise uniform tropical forest types remains unknown. We combined 16 tree inventories from central Panama with imaging spectroscopy and LiDAR elevation data from the Carnegie Airborne Observatory to test our ability to identify patterns in plant species composition, and to measure the spectral and structural differences between adjacent closed-canopy tropical forest types. We found that variations in spectroscopic imagery and LiDAR data were strong predictors of spatial turnover in plant species composition. We also found that these compositional, chemical, and structural patterns corresponded to underlying geological formations and their geomorphological properties. We conclude that imaging spectroscopy and LiDAR data can be used to interpret patterns identified in lower resolution sensors, to provide new information on forest function and structure, and to identify underlying determinants of these patterns.

A NEW FEATURE DESCRIPTOR FOR LIDAR IMAGE MATCHING

Abstract. LIght Detection And Ranging (LiDAR) data is available in a point cloud corresponding to long overlapping strips on the ground. The percentage of overlap in these LiDAR strips varies between 10 and 30. The strips are unregistered with respect to each other. Any further interpretation or study of the whole area requires registration of these strips with respect to each other. This process is called strip-adjustment. Traditionally, LiDAR point clouds are matched and strip-adjusted using techniques based on iterative closest point (ICP) or modifications of the same, which as the name suggests, runs over multiple iterations. Iterative algorithms, however, are time consuming and this paper offers a solution to the problem. In this paper, point correspondences are found on overlapping strips so that they can be registered with each other. We introduce a new method for point matching on LiDAR data. Our algorithm combines the power of LiDAR elevation data with a keypoint detector and descriptor that is based on the Scale Invariant Feature Transform (SIFT) method. The keypoint detector finds interesting keypoints in the LiDAR intensity image from the SIFT keypoints; a unique signature of each keypoint is then obtained by examining a patch surrounding that point in the elevation image. Histograms of subdivisions of the patch are set as the keypoint descriptor. Once all the keypoints and descriptors are obtained for two overlapping patches, correspondences are found using the nearest neighbor distance ratio method.

Revisiting the Timing and Events Leading to and Causing the Johnstown Flood of 1889

The Johnstown Flood of May 31, 1889, was responsible for more recorded deaths than any other disaster in the United States until the Galveston hurricane of 1900.1 An important difference between the two is that the Johnstown flood was not a natural disaster. Although the Johnstown region was in the midst of a particularly wet spring and the former boroughs that now form the city of Johnstown were already experiencing low-level flood-ing on May 31, the ultimate reason for the high death toll was the catastrophic failure of the South Fork Dam, located fourteen miles upstream from the outskirts of Johnstown on the South Fork of the Little Conemaugh River (see fig. 1). The millions of tons of water released by the failure of the dam caused devastation along the Little Conemaugh River drainage. As the water moved downstream it was temporarily impounded by debris dams behind two Pennsylvania Railroad bridges (Viaduct and Bridge no. 6), which caused "reformation of the lake" at these points. When Bridge no. 6 failed, the rejuvenated flood wave sped toward Johnstown. Most structures

Using airborne LiDAR and USGS DEM data for assessing rock glaciers and glaciers

Varying topographic and geologic conditions affect the location of rock glaciers. Despite being found worldwide, rock glaciers are often confused with glacier counterparts or other periglacial landforms. Light detection and ranging (LiDAR) data, because of its accuracy and resolution, may help the assessment of topographic variables needed to form rock glaciers or help reveal unique characteristics to enhance regional, automatic mapping. The objectives of this paper are to compare the elevation, slope, aspect, hillshade, and curvature for 1m LiDAR and 10m US Geological Survey (USGS) Digital Elevation Models (DEMs) from the Andrews and Taylor Glaciers with the Taylor Rock Glacier in Colorado. The utility of these data sources will be assessed for landform discrimination and to evaluate the uncertainty between the DEMs. According to the LiDAR data, the Taylor Rock Glacier exists at a lower elevation and has a gentler slope compared to the glaciers. Each landform has steep areas from which snow and debris are delivered. The Andrews Glacier has the most northern aspect, which helps maintain it through snow accumulation and reduced insolation. Glaciers exhibit a concave mean curvature, whereas the Taylor Rock Glacier has a convex mean curvature. The fine resolution of the LiDAR data clearly identifies some distinct characteristics. On the Taylor Rock Glacier, ridges, furrows, and a pronounced front slope were easily identifiable on the LiDAR DEM, whereas crevasses, the boundary between snow and debris covered surfaces, and a lateral moraine were detectable near the Andrews Glacier. The accuracy assessment revealed that at a common 10m resolution, the USGS DEM estimated a maximum elevation about 150m greater compared to the LiDAR data in areas of rugged topography surrounding the landforms. A comparison of root mean squared errors (RMSE) between the LiDAR and USGS DEMs showed that the Taylor Rock Glacier has the lowest RMSE for the elevation and the curvature variables. As a result, readily available USGS DEMs may better for analysis to characterize the topographic setting of landforms at the regional scale. At the fine scale, however, the micro-topography of rock glaciers is illuminated much more clearly on the LiDAR data, making it an ideal, yet costly source, for feature extraction.

Association of elevation error with surface type, vegetation class and data origin in discrete-returns airborne LiDAR

Airborne LiDAR (light detection and ranging) data are now commonly regarded as the most accurate source of elevation data for medium-scale topographical modelling applications. However, quoted LiDAR elevation error may not necessarily represent the actual errors occurring across all surfaces, potentially impacting the reliability of derived predictions in Geographical Information Systems (GIS). The extent to which LiDAR elevation error varies in association with land cover, vegetation class and LiDAR data source is quantified relative to dual-frequency global positioning system survey data captured in a 400-ha area in Ireland, where four separate classes of LiDAR point data overlap. Quoted elevation errors are found to correspond closely with the minimum requirement recommended by the American Society of Photogrammetry and Remote Sensing for the definition of 95% error in urban areas only. Global elevation errors are found to be up to 5 times the quoted error, and errors within vegetation areas are found to be even larger, with errors in individual vegetation classes reaching up to 15 times the quoted error. Furthermore, a strong skew is noted in vegetated areas within all the LiDAR data sets tested, pushing errors in some cases to more than 25 times the quoted error. The skew observed suggests that an assumption of a normal error distribution is inappropriate in vegetated areas. The physical parameters that were found to affect elevation error most fundamentally were canopy depth, canopy density and granularity. Other factors observed to affect the degree to which actual errors deviate from quoted error included the primary use for which the data were acquired and the processing applied by data suppliers to meet these requirements.

High‐Resolution Mapping of Wet Terrain within Discontinuous Permafrost using LiDAR Intensity

ABSTRACTSurface hydrology is an important aspect of northern environments on account of the thermal influence of water on permafrost. In this study, we demonstrate the ability of light detection and ranging (LiDAR) to map wet terrain within an area of discontinuous permafrost adjacent to the Northwest Territories Highway 3, located west of Yellowknife, Canada. Wet terrain was identified from LiDAR intensity measurements beneath forest canopies and across vegetated surfaces, including peatlands, fens, flooded black spruce and birch forests, and terrain adjacent to the highway embankment. Surface water pathways representing hydrological connections between water bodies and wet terrain were also identified at locations otherwise indiscernible from optical imagery. Statistical separability between terrain types, and thus the ability to map them, was improved by integrating LiDAR all‐return and bare‐earth intensity with colour orthophotos. The average classification accuracy for wet terrain was 93 per cent. These results indicate that LiDAR intensity can be used for local‐scale mapping of wet terrain, as required by northern engineers and scientists. Future integration of LiDAR intensity and elevation measurements may be used to assess changes in surface hydrological conditions impacting permafrost. Copyright © Her Majesty the Queen in Right of Canada 2012.

The use of medium point density LiDAR elevation data to determine plant community types in Baltic coastal wetlands

This study investigates the use of medium point density LiDAR (light detection and ranging) elevation data for identifying and locating plant community types in Baltic coastal wetlands. Ground-surveyed micro-topography data obtained using real-time kinematic differential GPS were used to analyse and correct the accuracy of LiDAR sourced elevation values in Matlab R2010a. Following the application of elevation corrections, 3 interpolation techniques were applied: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and the Delaunay Triangulated Irregular Network (TIN) interpolation. Analysis showed that the TIN method produced a more robust model that adequately represented the micro-topographical variation across the wetland. The TIN elevation interpolation was then used to characterise the extent and distribution of the plant communities using known elevation preferences in one coastal wetland site, Tahu. The plant community model developed from the TIN elevation interpolation was then further ground-truthed in two separate Baltic coastal wetland sites, Matsalu and Kudani, in order to test model validity.The results showed that the accuracy of the LiDAR data varied dependent upon plant community type. In 5 of the 6 studied community types, the LiDAR data gave a mean elevation difference of 0.177m (0.016SD) above the dGPS elevations. With dGPS elevation corrections, the LiDAR data produced an accurate model of the location of the different plant community types, Kappa coefficient (κ) 0.63, at the initial site, Tahu. The model was found to be very accurate in one of the subsequent ground-truthed sites, κ 0.81, with a similar management history, but less accurate, κ 0.53, in a wetland more exposed to wave action and with a longer history of vegetation management.The inclusion of dGPS ground survey data was shown to considerably improve the accuracy of the LiDAR elevation data to facilitate the identification of plant community types on the basis of elevation. The approach developed in this study of Baltic coastal wetlands may provide a transferable method for rapidly and accurately mapping plant communities in other open environments.

Salt marsh connectivity and freshwater versus saltwater inflow: multiple methods including tidal gauges, water isotopes, and LIDAR elevation models1This article is derived from a special session entitled “A New Hydrology: Inflow Effects on Ecosystem Form and Functioning” that took place at the February 2011 ASLO Aquatic Sciences conference in San Juan, Puerto Rico.

The hydrologic connectivity between different components of a coastal marsh is a key wetland structural element and is essential in determining habitat value. Salt marshes are often thought of as being hydrologically connected via tidal creeks, though water bodies within a salt marsh may also be semipermanently disconnected. At a salt marsh in Matagorda, Texas, USA, there are many “isolated” ponds, located at varying elevations. Our objective was to quantify the hydrologic connectivity of spatially isolated ponds at this site. We sampled water for salinity and stable isotopes (δ2H, δ18O) to determine the relative contribution of tidal water and rainfall within each pond. We also quantified the water level at which each pond floods its banks and connects to other ponds, using a light detection and ranging (LIDAR)-based elevation model. We found that pond connectivity was driven by multiple factors, of which wind and rainfall were the most important. Salinity and isotopic values between any two sampled ponds were correlated with the water level elevation at which the ponds connected. We conclude that the connectivity of the network, and the similarity of water samples within it, profoundly changes when specific water level thresholds are exceeded by both fresh water and salt water.

A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications

An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for site-specific management practices in cotton. A bi-variate Gaussian probability density distribution is modified to predict an improper probability distribution that also incorporates categorical variables associated with quadrant direction from the population means for the NDVI and elevation data layers. Water availability, influenced by slope and relative changes in elevation (as captured by the elevation data layer), affects crop phenology (as captured by the NDVI data layer). Thus, this fusion procedure results in a map potentially describing the joint effects of NDVI and elevation on cotton growth in a spatial and temporal way.