scholarly journals Response of Land Use Change to the Grain for Green Program and Its Driving Forces in the Loess Hilly-Gully Region

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Xiao Zhang ◽  
Yuanjie Deng ◽  
Mengyang Hou ◽  
Shunbo Yao
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Population Density ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Driving Forces ◽  
Cultivated Land ◽  
Forest Land ◽  
Land Area ◽  
Grain For Green

Implementation of the Grain for Green program (GGP) intensifies land use/cover change (LUCC) in the loess hilly-gully region. Clarifying the response of LUCC to the GGP and its driving forces are basic premises to implement the GGP more effectively for alleviating soil erosion in this region. This study analyzed the spatio-temporal characteristics of conversion of cultivated land to forest land and grassland in two study periods of 2000–2010 and 2010–2018. The transition matrix model and the dynamic degree model were utilized to explore changes among cultivated land, forest land, and grassland based on the remote sensing (RS) and monitoring data of land use in 2000, 2010, and 2018. Secondly, further detection on driving forces of increase of forest land and grassland was conducted through the logistic regression model. Fourteen driving factors were selected: the GGP, elevation, slope, population density, GDP per land area, distance to city, distance to residential area, etc. The results revealed that: (1) Area of cultivated land was mainly transferred to forest land and grassland during two study periods. The conversion of cultivated land to forest land and grassland occupied 21.48% and 68.01% of outward-transferring area of cultivated land from 2000 to 2010, and accounted for 13.26% and 74.3% from 2010 to 2018; (2) From the results of the logistic regression model, elevation, the GGP, annual mean temperature, slope III (6–15°), and GDP per land area were the main driving forces from 2000 to 2010. Moreover, the most prominent driving forces were the GGP, elevation, rural population density, slope III (6–15°), and soil pH from 2010 to 2018. The findings of this study can help us better understand the conversion of cultivated land to forest land and grassland under the GGP and provide a scientific basis to facilitate sustainable development of land resources in the study area.

scholarly journals Application of Geospatial Techniques and Logistic Regression Model for Urban Growth Analysis in Limbe, Cameroon

2020 ◽  
Vol 4 (1) ◽  
pp. 138-151
Author(s):  
E. A. Adzandeh ◽  
D. Alaigba ◽  
C. N. Nkemasong
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Land Cover ◽  
Regression Model ◽  
Urban Growth ◽  
Land Use Planning ◽  
Logistic Regression Model ◽  
Transition Probability ◽  
Thematic Mapper ◽  
Geospatial Techniques

Little is known about the nature of ecosystem loss, rampant changes in land use and land cover (LULC) and urban growth taking place in Limbe. The aim of this study is to analyze urban growth in Limbe, Cameroon from 1986-2019 using geospatial techniques and Logistic Regression Model (LRM). Landsat Thematic Mapper (1986), Enhanced Thematic Mapper+ (2002) and Operational Land Imagery/Thermal Infrared Sensor (2019) were utilized in this study. The images were classified into land cover classes using supervised image classification algorithm in ENVI software. The classification output was subjected to LRM application to evaluate urban growth. Image difference of urban growth between 1986 and 2019 was calculated as dependent variable and the independent variables were produced by calculating the Euclidean distance and Buffer of built-up, waterbody, road and farmland as driving factor for urban growth. Future urban growth was determined for 2035 using the Land Change Modeler in IDRISI Selva. Classification overall accuracy for the three date were not less than 99%. LRM results show a good fit with relative operation characteristic of 0.8344 and Pseudo R2 of 0.21. Analysis of LULC shows that built-up increased from 3.5% (1986) to 17.6% (2019). An urban land expansion rate of about 23% was observed for 2035. Transition probability matrix revealed high probability (0.6345) of build-up to remaining build-up by 2035, while the probability for it changing to waterbody, bare land, farm land and vegetation are 0.1099, 0.0459, 0.1939 and 0.1221, respectively. This study successfully demonstrates the application of geo-spatial techniques and LRM for land use/land cover change detection and in understanding the urban growth dynamics. It also identifies the potential areas of future urban growth, which can help land use policy planners for making optimum decisions of land use planning and investment.

scholarly journals Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur)

2020 ◽  
Author(s):  
Mahnaz Naemitabar ◽  
Mohammad Ali Zangeneh Asadi ◽  
Abolghasem Amirahmadi ◽  
Leila Goli Mokhtari
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Drainage Basin ◽  
Infiltration Rate ◽  
Drainage Density ◽  
Topographic Wetness Index ◽  
Model Case ◽  
Scs Cn

Spatial evaluation of flood-prone areas at the drainage basins is one of the basic strategies in the field of flood risk management. The present study aims to investigate the efficiency of the CN logistic and hydrological regression model for predicting and zoning floods. In the first stage, 13 runoff parameters, hydrologic soil groups (HSGs), slope, lithology, drainage density (DD), land curvature, elevation, distance to waterways/rivers, topographic wetness index (TWI), stream power index (SPI), rainfall, land use, and NDVI were employed. In the SCS-CN model of the drainage basin, the infiltration rate (S) and runoff amount (Q) were determined. The weights of the used layers were weighted by the AHP. Also, a flood zoning map of the drainage basin with different 5, 15, 25, and 50 year return periods was drawn by applying the weights of the layers. To ensure the accuracy of the zoning map with the logistic regression model, the ROC curve, and the area below the curve were used. The results showed that for the prediction rate, the AUC is 0.81%, indicating that the model has acceptable accuracy. The most important factors affecting flood are geological index; distance to waterways/rivers; and NDVI in the logistic regression model, and slope, DD, rainfall, and land use in the SCS-CN model respectively. 30 to 46% of the drainage basin area during 5 to 50 year periods has moderate flood potential, and 28 to 34% has high potential.

scholarly journals REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 721-726
Author(s):  
P. Myagmartseren ◽  
D. Ganpurev ◽  
I. Myagmarjav ◽  
G. Byambakhuu ◽  
G. Dabuxile
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Urban Area ◽  
Urban Growth ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Landsat Tm ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI & TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

A Spatial Logistic Regression Model for Simulating Land Use Patterns: A Case Study of the Shiraz Metropolitan Area of Iran

2010 ◽  
pp. 27-42 ◽  
Author(s):  
Amin Tayyebi ◽  
Mahmoud Reza Delavar ◽  
Mohammad Javad Yazdanpanah ◽  
Bryan Christopher Pijanowski ◽  
Sara Saeedi ◽  
...  
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Metropolitan Area ◽  
Logistic Regression Model ◽  
Land Use Patterns ◽  
Use Patterns
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