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

Otgonbayar, Munkhdulam; Atzberger, Clement; Chambers, Jonathan; Amarsaikhan, D.; Böck, Sebastian; Tsogtbayar, Jargaltulga

doi:10.4236/gep.2017.59017

Cited by 24 publications

(25 citation statements)

References 32 publications

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“…Therefore, there is a relatively strong interaction between soil type and vegetation distribution. Related studies have also shown that when precipitation is the dominant factor, soil type has an important impact on vegetation growth and rainwater reuse [6,56].…”

Section: Driving Force Of Vegetation Distribution and Changesmentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen's slope, Mann-Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.With the continuous development of earth observation system technology and the continuous enrichment of statistical models, the research content and methods of the relationships between vegetation, climate change, and human activities have become increasingly diverse. The normalized density vegetation index (NDVI), derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of vegetation growth status and spatial distribution density of vegetation, and is linearly related to vegetation distribution density [6,7]. A large number of ecological studies have been carried out related to vegetation change and its influencing factors using NDVI and statistical models. Temperature and precipitation, as important factors in the natural environment, are often used as research priorities to assess their impact on vegetation. However, the dominant factors contributing to NDVI in different regions vary considerably. The variety in vegetation and its connection to climate change have been investigated by Du et al. [5] and Zheng et al. [8] in China. These studies identified precipitation as the key climatic factor governing variation in NDVI. However, Guo et al. [9] found that temperature was the dominant factor on vegetation (NDVI) growth ...

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Section: Driving Force Of Vegetation Distribution and Changesmentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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“…Erdenee et al [5] have used Landsat TM and ETM data in the detection of changes cropland over Tsagaannuur, Selenge provinces from 1989 and 2000. Otgonbayar et al [41] investigated to prepare a cropland suitability map of Mongolia using Landsat and MODIS (MOD13, MOD15, and MOD17). Furthermore, Enkhjargal et al [42] used MODIS and SPOT time-series remotely sensed data from 2000-2013 to estimate long-term soil moisture content in agricultural regions of Mongolia.…”

Section: Introductionmentioning

confidence: 99%

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Tuvdendorj

Zeng

et al. 2019

Remote Sensing

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In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

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“…Therefore, the yield per hectare of two farms in the same geographical area appears to be different. The evaluation criteria of Munkhdulam (2017) were followed since it was difficult to develop criteria for this sub-topic [20].…”

Section: Pmasmentioning

confidence: 99%

Assessment of Eco-geomorphological potential of Mongolia

Bumtsend

Munkhtur

Erdenedalai

2020

Proc. Mong. Acad. Sci.

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The eco-geomorphological analysis includes the impact assessment of relief under various ecosystem conditions in as much relief has various ecological roles, both direct and indirect. Rising elevation above sea level is mostly influenced by climate indirectly leading to reduced air pressure, oxygen deficiency, reduction of air temperature, excessive solar radiation, and creation of strong wind. The depth of relief dissection of the bumpy surface of mountainous areas created by floods and mudflows, and the depth of the bumpy surface increases energy consumption and poses risks during mountain climbing, and also has negative economic implications if economic activities are undertaken in such terrain. On the other hand, mountainous landscapes have a specific impact on human well-being and also have considerable potential for promoting tourism. Although, in the steppe environment, relief dissection increases the unique features of the landscape and increases the potential of tourism in other respects, however, it is assessed negatively to a large extent. The nature of corelationship between and the interdependence of the terrestrial surface and population, terrestrial surface and livestock, terrestrial surface and agriculture, which are significant in the study into the inter-relationship between environment and human society, was assessed and determined , and the relevant conclusion was drawn.

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

Cited by 24 publications

References 32 publications

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Assessment of Eco-geomorphological potential of Mongolia

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