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Estimation of bioclimatic variables of Mongolia derived from remote sensing data

Frontiers of Earth Science 2022年第2期16卷 323-339页

作者： munkhdulam otgonbayar Clement ATZBERGER Erdenesukh SUMIYA Sainbayar DALANTAI Jonathan CHAMBERS Institute of Geography and Geoecology Mongolian Academy of Sciences(MAS)Ulaanbaatar 15170Mongolia Institute of Geomatics University of Natural Resources and Life Sciences(BOKU)Vienna 1190Austria School of Arts and Sciences National University of Mongolia(NUM)Ulaanbaatar 14201Mongolia Cooperazione Internazionale Milan 5020151Italy

Global maps of bioclimatic variables currently exist only at very coarse spatial resolution(***).For ecological studies requiring higher resolved information,this spatial resolution is often *** aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation(EO)data at a higher spatial resolution of 1 *** analysis used two different satellite time series data sets:land surface temperature(LST)from Moderate Resolution Imaging Spectroradiometer(MODIS),and precipitation(P)from Climate Hazards Group Infrared Precipitation with Stations(CHIRPS).Monthly maximum,mean,and minimum air temperature were estimated from Terra MODIS satellite(collection 6)LST time series product using the random forest(RF)regression *** total precipitation data were obtained from CHIRPS version *** on this primary data,spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated,representing the period *** tested the relationship between estimated bioclimatic variables(SatClim)and WorldClim bioclimatic variables version 2.0(WorldClim)using determination coefficient(R^(2)),root mean square error(RMSE),and normalized root mean square error(nRMSE)and found overall good *** the set of 19 WorldClim bioclimatic variables,17 were estimated with a coefficient of determination(R^(2))higher than 0.7 and normalized RMSE(nRMSE)lower than 8%,confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to *** the two bioclimatic variables related to temperature extremes(i.e.,annual mean diurnal range and isothermality)were modeled with only moderate accuracy(R^(2) of about 0.4 with nRMSE of about 11%).Generally,precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic *** overall success of the modeling was attributed to the f

关键词： bioclimatic variables MODIS land surface temperature CHIRPS precipitation

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Land Suitability Evaluation for Agricultural Cropland in Mongolia Using the Spatial MCDM Method and AHP Based GIS

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Journal of Geoscience and Environment Protection 2017年第9期5卷 238-263页

作者： munkhdulam otgonbayar Clement Atzberger Jonathan Chambers D. Amarsaikhan Sebastian Bock Jargaltulga Tsogtbayar Institute of Geography-Geoecology Mongolian Academy of SciencesUlaanbaatarMongolia Institute of Surveying Remote Sensing and Land InformationDepartment of Landscape Spatial and Infrastructure ScienceUniversity of Natural Resources and Life SciencesViennaAustria Eng-Geo Tech LLC UlaanbaatarMongolia

The purpose of this study was to prepare a cropland suitability map of Mongolia based on comprehensive landscape principles, including topography, soil properties, vegetation, climate and socio-economic factors. The primary goal was to create a more accurate map to estimate vegetation criteria (above ground biomass AGB), soil organic matter, soil texture, and the hydrothermal coefficient using Landsat 8 satellite imagery. The analysis used Landsat 8 imagery from the 2016 summer season with a resolution of 30 meters, time series MODIS vegetation products (MOD13, MOD15, MOD17) averaged over 16 days from June to August 2000-2016, an SRTM DEM with a resolution of 30 meters, and a field survey of measured biomass and soil data. In total, 6 main factors were classified and quality evaluation criteria were developed for 17 criteria, each with 5 levels. In this research the spatial MCDM (multi-criteria decision-making) method and AHP based GIS were applied. This was developed for each criteria layer’s value by multiplying parameters for each factor obtained from the pair comparison matrix by the weight addition, and by the suitable evaluation of several criteria factors affecting cropland. General accuracy was 88%, while PLS and RF regressions were 82.3% and 92.8%, respectively.

关键词： Land Suitability MCDM Boolean and Fuzzy Analysis AHP RF and PLS Regression

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