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

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.

Crop phenotyping studies with application to crop monitoring

1.Introduction Crop yield must urgently be sustainably increased to accommodate a rising global population and anticipated climate change in the coming decades,in the face of plant stresses and limited resources[1].Conventional crop breeding is limited by phenotypic selection and breeding efficiency.

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

Background:Species Distribution Modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive species.However,attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices(S2-RIs)and biophysical variables,in particular,were limited.Hence,this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin,Ethiopia.Thirty-five variables were computed from Sentinel-2B level-2A,and out of the variables,twelve significant variables were selected using Variable Inflation Factor(VIF).A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software“sdm”package.The performance of the models was evaluated using sensitivity,specificity,True Skill Statistics(TSS),kappa coefficient,area under the curve(AUC),and correlation.Results:Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction.Among the tested models,Random Forest(RF)performed better with 93%TSS and 99%AUC followed by Boosted Regression Trees(BRT),ensemble,Generalized Additive Model(GAM),Support Vector Machine(SVM),and Generalized Linear Model(GLM)in decreasing order.The relative influence of vegetation indices was the highest followed by soil indices,biophysical variables,and water indices in decreasing order.According to RF prediction,16.14%(1553.5 km^(2))of the study area was invaded by the alien species.Conclusions:Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution.Besides,the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.

Modeling grass yields in Qinghai Province,China,based on MODIS NDVI data--an empirical comparison

Qinghai Province is one of the four largest pastoral regions in China.Timely monitoring of grass growth and accurate estimation of grass yields are essential for its ecological protection and sustainable development.To estimate grass yields in Qinghai,we used the normalized difference vegetation index(NDVI)time-series data derived from the Moderate-resolution Imaging Spectroradiometer(MODIS)and a pre-existing grassland type map.We developed five estimation approaches to quantify the overall accuracy by combining four data pre-processing techniques(original,Savitzky-Golay(SG),Asymmetry Gaussian(AG)and Double Logistic(DL)),three metrics derived from NDVI time series(VImax,VIseason and VImean)and four fitting functions(linear,second-degree polynomial,power function,and exponential function).The five approaches were investigated in terms of overall accuracy based on 556 ground survey samples in 2016.After assessment and evaluation,we applied the best estimation model in each approach to map the fresh grass yields over the entire Qinghai Province in 2016.Results indicated that:1)For sample estimation,the crossvalidated overall accuracies increased with the increasing flexibility in the chosen fitting variables,and the best estimation accuracy was obtained by the so called“fully flexible model”with R2 of 0.57 and RMSE of 1140 kg/ha.2)Exponential models generally outperformed linear and power models.3)Although overall similar,strong local discrepancies were identified between the grass yield maps derived from the five approaches.In particular,the two most flexible modeling approaches were too sensitive to errors in the pre-existing grassland type map.This led to locally strong overestimations in the modeled grass yields.

Estimation of bioclimatic variables of Mongolia derived from remote sensing data

Global maps of bioclimatic variables currently exist only at very coarse spatial resolution(e.g.WorldClim).For ecological studies requiring higher resolved information,this spatial resolution is often insufficient.The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation(EO)data at a higher spatial resolution of 1 km.The 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 model.Monthly total precipitation data were obtained from CHIRPS version 2.0.Based on this primary data,spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated,representing the period 2002-2017.We 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 agreement.Among 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 WorldClim.Only 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 variables.The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables,especially at high altitudes and high latitudes.As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution,we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications,including species distribution modeling.