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.     

CARTOGRAPHIC RESEARCH ON ENVIRONMENTAL PROBLEMS ASSOCIATED WITH THE AGROINDUSTRIAL COMPLEX

The authors describe how cartographic research methods can be integrated with geographic information systems and automated mapping systems for the purpose of monitoring and analyzing negative impacts of agricultural and food processing activities on the environment. The paper also outlines basic research trends in ecological-geographical mapping and the content and types of cartographic products derived from mapping environmental impacts of the agroindustrial complex. A final section focuses on principles for the mapping of specific aspects and/or consequences of agroindustrial activity, e.g., soil erosion, agricultural chemical concentrations, water conservation issues. Translated by Edward Torrey, Alexandria, VA 22308 from: Geografiya i prirodnyye resursy, 1991, No. 3, pp. 5–11.     

THE CENTROGRAPHIC METHOD OF STUDYING SPATIAL STRUCTURE

The authors focus on modern applications of the centrographic method of geographic research and mapping—both in terms of how centrography may aid in the cartographic representation of certain distributions and in terms of how cartographic devices (e.g., centrograms) may be used in the more effective identification of centers. A brief history of the method's development in Russia and the USSR is followed by examples of the use of centrograms in simultaneously comparing shifts (a) of distinct but related phenomena and (b) of the same phenomenon but at different scale levels. A variety of centrograms are presented and analyzed. Translated by Jay K. Mitchell, PlanEcon, Inc., Washington, DC 20005 from: Izvestiya Akademii Nauk SSSR, seriya geograficheskaya, 1990, No. 2, pp. 92–105.     

Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena

Studies on volunteered geographic information (VGI) have focused on examining its validity to reveal geographic phenomena in relatively recent periods. Empirical evaluation of the validity of VGI to reveal geographic phenomena in historical periods (e.g., decades ago) is lacking, although such evaluation is desirable for assessing the possibility of broadening the temporal scope of VGI applications. This article presents an evaluation of the validity of VGI to reveal historical geographic phenomena through a citizen data‐based habitat suitability mapping case study. Citizen data (i.e., sightings) of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) were elicited from local residents through three‐dimensional (3D) geovisualization interviews in Yunnan, China. The validity of the elicited sightings to reveal the historical R. bieti distribution was evaluated through habitat suitability mapping using the citizen data in historical periods. The results of controlled experiments demonstrated that suitability maps predicted using the historical citizen data had a consistent spatial pattern (correlation above 0.60) that reflects the R. bieti distribution (Boyce index around 0.90) in areas free of significant environmental change across historical periods. This in turn suggests that citizen data have validity for mapping historical geographic phenomena. It provides supporting empirical evidence for potentially broadening the temporal scope of VGI applications.     

MAPPING THE SOILS OF THE MAJOR NATURAL ZONES OF THE USSR FROM REMOTE SENSING IMAGERY

The authors describe the results of their experience in the use of remote sensing imagery to map the distribution of soils within the major natural zones (e.g., wooded steppe, taiga, semidesert) of the USSR. Considerable attention is devoted to instrumental-visual methods of interpretation before the focus shifts to automated interpretation and methods of map compilation, particularly general mapping at intermediate and large scales. One section assesses the relative sizes of mesorelief forms and fields in various natural zones in an effort to determine at what image scales the soil types indicated by these features will be identifiable. Translated by Edward Torrey, Alexandria, VA 22308 from: G. V. Dobrovol'skiy and V. L. Andronikov, eds., Aerokosmicheskiye metody v pochvovedenii i ikh ispol'zovaniye v sel'skom khozyaystve: sbornik nauchnykh trudov [Remote Sensing Methods in Soil Science and Their Utilization in Agriculture: A Collection of Scientific Works]. Moscow: Nauka, 1990, pp. 22-34.     

DEVELOPMENT OF QUANTITATIVE METHODS OF STUDYING THE EARTH FROM SPACE

The paper surveys the development of “space geography”–a field of knowledge applying the methods of remote sensing, the physical sciences, and mathematics in the solution of geographic problems. Early advances featured methodological improvements (use of quantitative methods in image processing), whereas future research must focus upon perfecting our knowledge of: (a) relationships underlying the use of these methods, i.e., between environmental parameters and reflectance values, (b) methods of effectively combining different types of imagery, and image products and field work, in research, and (c) appropriate quantitative indices for feature recognition. Examples of the operationalization of such concerns are demonstrated for land-use and soil mapping projects. Translated from: Izvestiya, AN SSSR, seriya geograficheskaya, 1985, No. 5, pp. 110–116.     

Land-use and vegetation-cover mapping of an indigenous land area in the state of Mato Grosso (Brazil) based on spectral linear mixing model,segmentation and region classification

A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna).     

LANDSCAPE-FUNCTIONAL MAPS IN THE STUDY OF URBAN GEOCHEMICAL ANOMALIES

A method is proposed for the use of large-scale “landscape-functional” maps (depicting interrelationships between landscape structure, land use, and environmental impacts) in the identification and explanation of geochemical anomalies within urbanized areas. Landscape-functional complexes or basic elementary landscapes located within different zones of human activity, serve as the basic units of mapping. An excerpt from a landscape-functional map of an unnamed mining-metallurgical center in a semi-arid mountain basin environment is provided as an illustration of how such maps reveal spatial associations between trace-element concentrations in soil, snow, and stream bottom sediments; location in respect to major pollution sources (proximity, upslope or downslope position, leeward or windward orientation); and frequency and intensity of trace-element transport (mass movements, stream and overland flow, prevailing winds). Translated from: Vestnik Moskovskogo Universitete, geografiya, 1986, No. 5, pp. 88-94.     

Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data

Directly mapping impervious surface area (ISA) at national and global scales using nighttime light data is a challenge due to the complexity of land surface components and the impacts of unbalanced economic conditions. Previous research mainly used the coarse spatial resolution Defense Meteorological Satellite Program’s Operational Linescan System (DMSP OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data for ISA mapping; the improved spatial resolution and data quality in the Suomi National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS DNB) and in Proba-V data provide a new opportunity to accurately map ISA distribution at the national scale, which has not been explored yet. This research aimed to develop a new index – modified impervious surface index (MISI) – based on VIIRS DNB and Proba-V data to improve ISA estimation and to compare the results with those from the combination of VIIRS DNB and MODIS NDVI data. Landsat data were used to develop ISA data for the typical sites for use as reference data. Regression analysis was used to establish the ISA estimation model in which the dependent variable was from the Landsat data and the independent variable was from the MISI, as well as the previously used Large-scale Impervious Surface Index (LISI). The results indicate that the major error is from the very small or very large proportion of ISA in a unit; improvement of spatial resolution through use of higher spatial resolution nighttime light data (e.g., VIIRS DNB) or NDVI (e.g., Proba-V NDVI) data is an effective approach to improve ISA estimation. Although different indices for the combination of nighttime light and NDVI data have been used, the MISI is especially valuable for reducing the estimation errors for the regions with a small or large ISA proportion.     

Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.     

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.     

Soil Resource Database at Village Level for Developmental Planning

A study was carried out in village Mohammadabad, Nalgonda district, Andhra Pradesh to demonstrate the potential of high-resolution remote sensing data in generating soil resource database at village level that would facilitate natural resource developmental planning. For this study, detail soil resource database was generated by interpreting IRS Resourcesat-1 LISS-IV + Cartosat-1 merged data and by undertaking soil survey and soil analysis. This database was integrated with cadastral map and was analysed for preparation of an action plan for the village that included recommendation on suitable crops, soil and water conservation measures and distribution of soil health card to farmers. The study indicates that the LISS-IV + Cartosat-1 merged satellite data helps in mapping soils at phase level and in delineation of more number of pure soil mapping units i.e. consociations at 1:10.000 scale. The plot-wise resource potential and constraints reveals that significant inter and intra-field soil variability exists. Soil fertility assessment indicates that the soils of the village are low to medium in fertility (available NPK Content) and micronutrients like copper, manganese and iron content is above the critical level for optimum growth of crops while zinc content is less than the critical level.     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

Room semantics inference using random forest and relational graph convolutional networks: A case study of research building

Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79).     

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.     

PROBLEMS AND PROSPECTS OF THE COMPLEX ECOLOGICAL-GEOGRAPHIC MAPPING OF SIBERIA

A review of basic theoretical principles of “ecological” mapping and types of maps of the environment sets the stage for a more detailed treatment of ecological-geographic maps—maps portraying a wide range of nature-society and nature-nature relationships—as they are used in support of economic development and environmental monitoring in Siberia. Particular attention is devoted to the compilation of map series, which include ecological-geographical maps, for the Lake Baykal area, BAM service zone, West Siberia, and specific territorial production complexes (e.g., Kansk-Achinsk TPC). Translated from: Geografiya i prirodnyye resursy, 1987, No. 3, pp. 10-18.     

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.     

Detecting urban ecological land-cover structure using remotely sensed imagery: A multi-area study focusing on metropolitan inner cities

With increasing attention being paid to sustainable urban development and human habitation improvement, urban ecological land cover (UELC), i.e., surface water and green space, has played an important role of the highly compact inner urban regions. In this study, we developed an efficient approach for UELC mapping by coupling Sentinel-2 multi-spectral imagery and Google Earth high-resolution imagery. In contrast with the conventional single-source and multi-source imagery-based classification methods, the proposed method respectively achieved the highest overall accuracies of 91.50% and 94.05% in the UELC mapping for two test sites (i.e. Shanghai and Seoul). The proposed method is used for urban surface mapping among six world-class cities. For an in-depth analysis of the landscape structures for inner urban regions, seven landscape metrics are introduced for the quantification of the UELC structure based on the obtained high-precision UELC maps. The result shows that London appears to have the best UELC-induced ecological quality, that is, with high percentage of landscape, area-weighted mean fractal dimension, edge density, Shannon’s evenness index values and a low contagion index value, while Tokyo is exactly the opposite. Several common characteristics found through the statistical analysis are: 1) all the inner-city regions have small UELC coverage (< 50%) and low shape complexity; 2) green space generally contributes more to urban eco-environment than the urban surface water; and 3) all cities show high landscape consistency in the inner urban region.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

MICROPHOTOMETRIC ANALYSIS OF SOIL MOISTURE CONTENT FROM REMOTE SENSING IMAGERY

A method is outlined for the determination of soil moisture content from remote sensing imagery, with consideration of its rapid fluctuation based on meteorological events and other factors (e.g., humus content and character of vegetation). The present paper is devoted to soil moisture detection in the visible, reflected-infrared, and thermal-infrared portions of the electromagnetic spectrum in irrigated portions of the Kura-Araks lowland in Soviet Transcaucasia, based on optical density measurements from image negatives, for the most part. Translated by Edward Torrey, Alexandria, VA 22308 from: G. V. Dobrovol'skiy and V. L. Andronikov, eds., Aerokosmicheskiye metody v pochvo-vedenii i ikh ispol'zovaniye v sel'skom khozyaystve: sbornik nauchnykh trudov [Remote Sensing Methods in Soil Science and Their Utilization in Agriculture: A Collection of Scientific Works]. Moscow: Nauka, 1990, pp. 183–189.