scholarly journals The Process-Mode-Driving Force of Cropland Expansion in Arid Regions of China Based on the Land Use Remote Sensing Monitoring Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2949
Author(s):  
Tianyi Cai ◽  
Xinhuan Zhang ◽  
Fuqiang Xia ◽  
Zhiping Zhang ◽  
Jingjing Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Water Resources ◽  
Spatial Heterogeneity ◽  
Driving Force ◽  
Arid Regions ◽  
Center Of Gravity ◽  
Rapid Expansion ◽  
Monitoring Data ◽  
Remote Sensing Monitoring

The center of gravity of China’s new cropland has shifted from Northeast China to the Xinjiang oasis areas where the ecological environment is relatively fragile. However, we currently face a lack of a comprehensive review of the cropland expansion in oasis areas of Xinjiang, which is importantly associated with the sustainable use of cropland, social stability and oasis ecological security. In this study, the land use remote sensing monitoring data in 1990, 2000, 2010 and 2018 were used to comprehensively analyze the process characteristics, different modes and driving mechanisms of the cropland expansion in Xinjiang, as well as its spatial heterogeneity at the oasis area level. The results revealed that cropland in Xinjiang continued to expand from 5803 thousand hectares in 1990 to 8939 thousand hectares in 2018 and experienced three stages of expansion: steady expansion, rapid expansion, and slow expansion. The center of gravity of cropland showed the characteristic of shifting to the South. Edge expansion and encroachment on grassland were the dominant spatial pattern mode and land use conversion mode of Xinjiang’s cropland expansion, respectively. The expansion of cropland in Xinjiang was affected by multiple factors. Irrigation conditions played a dominant role. Topography indirectly affected cropland expansion by affecting the suitability of agricultural production and development. Population growth and farmers’ income were important driving forces. There was significant spatial heterogeneity in the intensity, mode and driving force of cropland expansion among different oasis areas in Xinjiang. The spatial shift of China’s new cropland has occupied a large amount of water resources and ecological land in Xinjiang and exacerbated the vulnerability of the ecosystem in arid regions. The key to sustainable management of cropland in Xinjiang in the future lies in maintaining an appropriate scale of cropland and promoting the coordinated development of cropland, population, water resources and industry.

scholarly journals Land Use/Land Cover Mapping of Sambar Watershed by Using Remote Sensing and GIS

2021 ◽  
Vol 10 (9) ◽  
pp. 578-585
Author(s):  
B. Varpe Shriniwas D. Payal Sandip
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing And Gis ◽  
Rapid Expansion ◽  
Water Bodies ◽  
Land Cover Mapping ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Barren Land

In the present study, an effort has been made to study in detail of Land Use/Land Cover Mapping for Sambar watershed by using Remote Sensing and GIS technique was carried out during the year of 2020-2021 in Parbhani district. In this research the Remote Sensing and Geographical Information system technique was used for identifying the land use/land cover classes with the help of ArcGIS 10.8 software. The Sambar watershed is located in 19º35ʹ78.78˝ N and 76º87ʹ88.44˝ E in the Parbhani district of Marathwada region in Maharashtra. It is covered a total area 97.01 km2. The land use/land cover map and its classes were identified by the Supervised Classification Method in ArcGIS 10.8 software by using the Landsat 8 satellite image. Total six classes are identified namely as Agricultural area, Forest area, Urban area, Barren land, Water bodies and Fallow land. The Agricultural lands are well distributed throughout the watershed area and it covers 4135 ha. (43 per cent). Forest occupies 502 ha area and sharing about 5 per cent of the total land use land cover of the study area. The Urban land occupies 390 ha. area (4 per cent) and there was a rapid expansion of settlement area. Barren land occupies 3392 ha. area (35 per cent). A water bodies occupy 630 ha. area (6 per cent) and the Fallow land occupies 650 ha (7 per cent) but well-developed dendritic drainage pattern and good water availability is in the Sambar watershed.

scholarly journals Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

2018 ◽  
Vol 10 (8) ◽  
pp. 2878 ◽  
Author(s):  
Xiaoli Hu ◽  
Xin Li ◽  
Ling Lu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Sustainable Development ◽  
Environmental Policy ◽  
Water Resources ◽  
Cellular Automata ◽  
Policy Change ◽  
Arid Regions ◽  
Artificial Neural

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

scholarly journals Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

2019 ◽  
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Heterogeneity ◽  
Urban Ecology ◽  
Vegetation Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Ecological Index ◽  
Sensing Data

The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

scholarly journals Modeling of Runoff in the Arid Regions Using Remote Sensing and Geographic Information System (GIS)

2020 ◽  
Vol 15 (5) ◽  
pp. 691-700
Author(s):  
Ahmed Shahadha Muneer ◽  
Khamis Naba Sayl ◽  
Ammar Hatem Kamel
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Water Resources ◽  
Water Resources Management ◽  
Surface Runoff ◽  
Arid Regions ◽  
Western Desert ◽  
Resources Management ◽  
Good Agreement

One of the most important challenges in the field of engineering hydrology and water resources management, especially in arid regions such as the Iraqi Western Desert, is the process of predicting and quantifying the surface runoff. The limited available data about rainfall, runoff, soil properties, evaporation, and the lack of metrological stations make the process of predicting and calculating surface runoff a very difficult task. Modern technology can help with the purpose of compensating for the shortage of data and providing the information necessary to estimate the runoff and develop the system of water resources management in the region. The present study develops a model to determine the infiltration of soil from spectral reflectance using Artificial Neural Networks (ANN) integrated with a geographic information system (GIS) and remote sensing (RS). Field infiltration measurements for 105 soil samples in the Al-Ratga catchment area in the Iraqi western desert are achieved. The performance of the developed model was assessed both qualitatively and quantitatively (effective runoff depth) by comparing the results of actual and estimated basic infiltration rate values for each sample. The results refer to a good agreement between estimated and measured infiltration (R2=0.768). The developed model predicts the runoff depending on the water balance equation and the results refer to good agreement with the SCS-CN model that is one of the most widely used in this region.

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