SOIL-GEOBOTANICAL REGIONALIZATION FROM REMOTE SENSING IMAGERY

A multiscalar approach to mapping soil-vegetation regions from remote sensing imagery is outlined, using the West Siberian Lowland as a study area. At an initial stage small-scale space imagery is used to identify extensive soil-vegetation regions which extend across nearly the entire Lowland. Subsequent analysis focuses on identification and mapping of increasingly smaller units. The dominant criteria used for image interpretation and regionalization vary at each particular level of analysis, changing from vegetation structure and density at the smallest scales to soil “hydromorphism” (waterlogging) and topographic affiliations for intermediate- and large-scale units. Hydromorphic indicators are stressed as most important overall. Translated from: Distantsionnyye issledovaniya rel'yefa Sibiri, A. L. Yanshin and V. N. Sharapov, eds. Novosibirsk: Nauka, 1985, pp. 51-58.     

MAPS OF COMBUSTIBLE FOREST MATERIALS

An article devoted to applied forest-fire mapping outlines principles for the compilation of maps depicting “raw materials” for such fires. Various types and densities of vegetation cover are classified in terms of combustibility, i.e., according to the intensity of burning expected once they are fully exposed to flames. These maps are used in conjunction with weather data and forecasts to predict and combat the spread of fire across an area. Particular attention is devoted to identification and mapping of “basic conductors” of combustion–layers of forest litter and mossypeaty vegetation along which a forest fire normally spreads. Translated from: Geografiya i prirodnyye resursy, 1987, No. 3, pp. 138-144.     

REMOTE SENSING IN THE MORPHOMETRIC ANALYSIS OF CHANNELS AND FLOODPLAINS

The first of two papers devoted to the analysis and mapping of river channels and floodplains describes Soviet work in the photogrammetric and cartometric analysis of floodplain morphology based on remote sensing imagery. The emphasis of the present paper is on the creation of digital terrain models for the automated measurement and mapping of floodplain features. Considerable attention is focused upon analysis of indicators of channel and floodplain dynamics (channel deposition, bankside erosion, meanders) appearing on aerial photographs. The results of channel analyses based on aerial photographic and field methods (determinations of channel width, depth, etc.) are compared for a test site. Translated from: Geomorfologiya, 1986, No. 4, pp. 51-57.     

Use of WorldView-2 time series to establish a wetland monitoring program for potential offsite impacts of mine site rehabilitation

The Ramsar-listed wetlands of the Magela Creek floodplain, situated in the World Heritage Kakadu National Park, in northern Australia are recognised for their biodiversity and cultural values. The floodplain is also a downstream receiving environment for Ranger uranium mine, which is entering closure and rehabilitation phases. Vegetation on the floodplain is spatially and temporally variable which is related to the hydrology of the region, primarily the extent and level of inundation and available soil moisture. Time-series mapping of the floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics to assist with determining whether change is natural or a result of the potential impacts of mine closure activities such as increased suspended sediment moving downstream. The research described here used geographic object-based image analysis (GEOBIA) to classify the upper Magela Creek floodplain vegetation from WorldView-2 imagery captured over four years (2010–2013) and ancillary data including a canopy height model. A step-wise rule set was used to implement a decision tree classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010, May 2011, June 2012 and June 2013 with overall accuracies of over 80% for each map. Most of the error appears to be associated with confusion between vegetation classes that are spectrally similar such as the classes dominated by grasses. Object-based change detection was then applied to the maps to analyse change between dates. Results indicate that change between dates was detected for large areas of the floodplain. Most of the change is associated with the amount of surface water present, indicating that although imagery was captured at the same time of year, the imagery represents different stages of the seasonal cycle of the floodplain.     

Procedures for woody vegetation surveys in the Kazgail rural council area,Kordofan, Sudan

Efforts to reforest parts of the Kordofan Province of Sudan are receiving support from international development agencies. These efforts include planning and implementing reforestation activities that require the collection of natural resources and socioeconomic data, and the preparation of base maps. A combination of remote sensing, geographic information system and global positioning systems procedures are used in this study to meet these requirements.

Remote sensing techniques were used to provide base maps and to guide the compilation of vegetation resources maps. These techniques provided a rapid and efficient method for documenting available resources. Pocket‐sized global positioning system units were used to establish the location of field data collected for mapping and resource analysis. A microcomputer data management system tabulated and displayed the field data. The resulting system for data analysis, management, and planning has been adopted for the mapping and inventory of the Gum Belt of Sudan.     

Visual versus digital analysis for vegetation mapping: Some examples on central Spain

Digital processing of Landsat images has been considered the most appropriate interpretation method for vegetation mapping. However, digital processing presents several difficulties: (i) it demands significant inversions, with respect both the images and the equipment; (ii) it presents problems to discriminate heterogeneous categories, and (iii) it requires much more training effort.

Visual analysis, on the other hand, is less demanding both in economic investments and training. Therefore, it is a fruitful alternative to digital mapping, especially when it is applied to small and medium scale inventories. A consistent methodology for visual interpretation of vegetation categories is presented in this paper. Benefits and disadvantages of this procedure are analyzed, as well as keys‐for visual identification of land cover categories. A TM Quarter of scene on Central Spain is presented as an example of this method. Two false‐color images from different seasons were interpreted at 1: 250,000 scale. Fourteen land cover categories were identified, yielding 83.03% of final accuracy.     

REMOTE SENSING FROM SPACE: A REGIONAL STUDY APPLICATION

This article outlines the contributions of remote sensing to a comprehensive mapping program and resource inventory now underway in the Kalmyk ASSR, a semi-arid area in the southern USSR. Specific uses include: identification of potential oil- and gas-bearing formations for geological exploration, pinpointing of sites experiencing rangeland deterioration and loss of vegetation cover, identification of areas containing possible reserves of groundwater, and agricultural land use monitoring and inventory. Translated from: Ekonomicheskaya gazeta, No. 27, July 1985, p. 17.     

A LANDSCAPE-GEOCHEMICAL WORLD MAP AS A BASIS FOR ENVIRONMENTAL-GEOCHEMICAL MAPPING AND FORECASTING

The author outlines a procedure for the compilation of a global-scale map of landscapes as defined by their associated geochemical conditions. Major methodological issues are addressed, such as the selection of the appropriate taxonomic units (size of geochemical regions) for mapping, identification of specific geochemical criteria for regional differentiation, and use of color and pattern in map generation to ensure maximum discrimination and readability. Translated by Edward Torrey, Alexandria, VA 22308 from: Geografiya i prirodnyye resursy, 1993, No. 1, pp. 5-10.     

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.     

Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS,Landsat 7 ETM+, SRTM and multiple Random Forests

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape.The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation.In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit.Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively.In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.     

Soil resource assessment and mapping using remote sensing and GIS

In this study digital image processing for physiographic analysis and soil resource mapping of Solani watershed was carried out using satellite remote sensing data and GIS. Digital image processing of satellite data facilitated in accurately delineating and identifying various soil mapping units. The physiography of the study area is mainly influenced by denudational and colluvial processes in the upper part and by sedimentation processes in the lower part. Topography of the land and nature of parent material along with the time factor seemed to have played a vital role in the genesis of soils. Majority of the mapping units are Typic Haplustepts with Entisols and Inceptisols being the major soil orders. The soils of the Siwalik hills experiences severe erosion, which prevents the maturation of soil profile. The present study demonstrated that satellite remote sensing and GIS is a valuable tool for physiographic analysis and soil resource mapping.     

A novel approach to optimize hierarchical vegetation mapping from hyper-temporal NDVI imagery,demonstrated at national level for Namibia

This paper presents a novel methodological approach to countrywide vegetation mapping. We used green vegetation biomass over the year as captured by coarse resolution hyper-temporal NDVI satellite-imagery, to generate vegetation mapping units at the biome, ecoregion and at the next lower hierarchical level for Namibia, excluding the Zambezi Region. Our method was based on a time series of 15 years of SPOT-VGT-MVC images each representing a specific 10-day period (dekad). The ISODATA unsupervised clustering technique was used to separately create 2–100 NDVI-cluster maps. The optimal number of temporal NDVI-clusters to represent the information on vegetation contained in the imagery was established by divergence separability statistics of all generated NDVI-clusters. The selected map consisted of legend of 81 cluster-specific temporal NDVI-profiles covering each a 15-year period of averaged NDVI data representing all pixels classified to that cluster. Then, by legend-entry using the dekad-medians of all 15 annual repeats, we produced generalized legend-entries without year-specific anomalies for each cluster. Subsequently, a hierarchical cluster analysis of these temporal NDVI-profiles was used to produce a dendrogram that generated grouping options for the 81 legend-entries. Maps with cluster-groups of 8 and 4 legend-entries resulted. The 81-cluster map and its 65 legend-entries vector version have no equivalent in published vegetation maps. The 8 cluster-group map broadly corresponds with published ecoregion level maps and the 4 cluster-group map with the published biome maps in their number of legend units. The published vegetation maps varied considerably from our NDVI-profile maps in the location of mapping unit boundaries. The agreement index between our map and published biome maps ranges from 70−93. For the ecoregion level, the agreement index is much lower, namely 51−75. Our methodological approach showed a considerably higher discretionary power for hierarchical levels and the number of vegetation mapping units than the approaches applied to previously published maps. We recommended an approach to transform our three hyper-temporal NDVI-profiles based legend-entries into more specific vegetation units. This might be accomplished by re-analysis of available, spatially-comprehensive plant species occurrence data.     

An automated algorithm for mapping building impervious areas from airborne LiDAR point-cloud data for flood hydrology

Buildings, as impervious surfaces, are an important component of total impervious surface areas that drive urban stormwater response to intense rainfall events. Most stormwater models that use percent impervious area (PIA) are spatially lumped models and do not require precise locations of building roofs, as in other applications of building maps, but do require accurate estimates of total impervious areas within the geographic units of observation (e.g. city blocks or sub-watershed units). Two-dimensional mapping of buildings from aerial imagery requires laborious efforts from image analysts or elaborate image analysis techniques using high spatial resolution imagery. Moreover, large uncertainties exist where tall, dense vegetation obscures the structures. Analyzing LiDAR point-cloud data, however, can distinguish buildings from vegetation canopy and facilitate the mapping of buildings. This paper presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes and can be used with standard GIS software to identify building roofs under tall, thick canopy. Accuracy assessment methods are presented that can optimize model performance for modeling BIA within the geographic units of observation for hydrologic applications. The Building Extraction from LiDAR Last Returns (BELLR) model, a 2.5D rule-based GIS model, uses a non-spatial, local vertical difference filter (VDF) on LiDAR point-cloud data to automatically identify and map building footprints. The model includes an absolute difference in elevation (AdE) parameter in the VDF that compares the difference between mean and modal elevations of last-returns in each cell.

The BELLR model is calibrated for an extensive inner-city, highly urbanized small watershed in Columbia, South Carolina, USA that is covered by tall, thick vegetation canopy that obscures many buildings. The calibration of BELLR used a set of building locations compiled by photo-analysts, and validation used independent building reference data. The model is applied to two residential neighborhoods, one of which is a residential area within the primary watershed and the other is a younger suburban neighborhood with a less-well developed tree canopy used as a validation site. Performance results indicate that the BELLR model is highly sensitive to concavity in the lasboundary tool of LAStools® and those settings are highly site specific. The model is also sensitive to cell size and the AdE threshold values. However, properly calibrated the BIA for the two residential sites could be estimated within 1% error for optimized experiments.

To examine results in a hydrologic application, the BELLR estimated BIAs were tested using two different types of hydrologic models to compare BELLR results with results using the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness data. The BELLR BIA values provide more accurate results than the use of the 2011 NLCD PIA data in both models. The VDF developed in this study to map buildings could be applied to LiDAR point-cloud filtering algorithms for feature extraction in machine learning or mapping other planar surfaces in more broad-based land-cover classifications.     

MAPPING ANIMAL HABITATS

The authors propose a series of general methodological guidelines for animal habitat mapping. These include the use of “summary” indicators (of aggregate effects of environmental conditions on animal populations) as indices for mapping, the combination of laboratory and field work at all but the smallest scales of mapping, and explicit recognition (based on extensive studies of ecological characteristics and life cycles of species being mapped) that habitat boundaries in many cases vary dramatically from vegetation and landscape-geological boundaries. Habitat maps of two study areas are compared with vegetation and landscape maps at identical scales to demonstrate these differences. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1985, No. 3, pp. 95-101.     

USE OF THE CARTOGRAPHIC METHOD IN THE STUDY OF CHANNEL PROCESSES

A second paper on floodplain and channel mapping describes how the “cartographic method” (the use of maps to derive information about the world not readily available through other means of investigation) is applied through the use of special maps depicting the sizes and types of river channels, the character of floodplains and of channel deformation, and the general conditions responsible for channel evolution. The uses, strengths, and limitations of channel maps at small, intermediate, and large scales are discussed, as well as methods of cartographic presentation that have proven most effective. Translated from: Geografiya i prirodnyye resursy, 1986, No. 3, pp. 99-108.     

Airborne LiDAR point cloud in mapping of fluvial forms: a case study of a Hungarian floodplain

The aim of this paper was to analyze the ground and low vegetation points of a Light Detection and Ranging (LiDAR) point cloud from the aspect of the generated digital terrain model (DTM). We determined the height difference between the surveyed surface and the DTM and the level of interspersion of ground and low vegetation points in a floodplain. Finally, we performed a supervised classification with topographic (elevation, slope and aspect) variables and an Normalized Difference Vegetation Index (NDVI) layer to identify swales and point bars as floodplain forms. Cross sections of field surveys provided reference data to express the magnitude of the bias on the DTM caused by the vegetation, and we proved that the bias can reach the 60% of the relative height and depth of the floodplain forms (mean error was 0.15 ± 0.12 m). A landscape metric, the Aggregation Index, provided an appropriate tool to analyze and quantify the interspersion of the ground and vegetation points: indicating a high level of interspersion of the classified points, i.e. proved that vegetation points where the last echoes reflected from the vegetation became ground points. Floodplain classification performed best with the common use of DTM, slope, aspect and NDVI coverages, with 71% overall accuracy.     

Detecting and mapping levels of Gonipterus scutellatus-induced vegetation defoliation and leaf area index using spatially optimized vegetation indices

Gonipterus scutellatus outbreaks may severely defoliate Eucalyptus plantations growing in South Africa. Therefore, detecting and mapping the severity and extent of G. scutellatus defoliation is essential for the deployment of suppressive measures. In this study, we tested the utility of spatially optimized vegetation indices and an artificial neural network in detecting and mapping G. scutellatus-induced vegetation defoliation, using both visual estimates of percentage defoliation and optical leaf area index (LAI) measures. We tested both field methods to determine which of the two were more superior in detecting vegetation defoliation using optimized vegetation indices. These indices were computed from a WorldView-2 pan-sharpened image, which is characterized with a 0.5-m spatial resolution and eight spectral bands. The indices were resampled to spatial resolutions that best represented levels of G. scutellatus-induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with an R² of 0.83 and an RMSE of 1.55 (2.97% of the mean measured defoliation), based on an independent test data-set. Similarly, LAI subjected to defoliation was detected with an R² of 0.80 and an RMSE of 0.03 (0.06% of the mean measured LAI), based on an independent test data-set. Therefore, the results indicate that the cheaper less-complicated visual percentage estimates of defoliation was the more superior model of the two. A sensitivity analysis revealed that NDRE, MCARI2 and ARI ranked as the top three most influential indices in developing both percentage defoliation and LAI models. Furthermore, we compared the optimized model with a model developed using the original image spatial resolution. The results indicated that the optimized model performed better than the original 0.5-m spatial resolution model. Overall, the study showed that vegetation indices optimized to specific spatial resolutions can effectively detect and map levels of G. scutellatus-induced defoliation and LAI subjected to defoliation.