To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is  responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS. 

Land use or land cover (LU/LC) mapping serves as a kind of basic information for land resource study. Detecting and analyzing the quantitative changes along the earth’s surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, economic and industrial growth. The LU/LC pattern in Madurai City, Tamil Nadu, has undergone a significant change over the past two decades due to accelerated urbanization. In this study, LU/LC change dynamics were investigated by the combined use of satellite remote sensing and geographical information system. To understand the LU/LC change in Madurai City, different land use categories and their spatial as well as temporal variability have been studied over a period of seven years (1999-2006), by analyzing Landsat images for the years 1999 and 2006 respectively with the help of ArcGIS 9.3 and ERDAS Imagine 9.1 software. This results show that geospatial technology is able to effectively capture the spatio-temporal trend of the landscape patterns associated with urbanization in this region.

This paper qualitatively analyzes the connotation of woodland welfare and the changes of woodland welfare that may be caused by the transfer of the right to use, and interprets the welfare improvement caused by the transfer of the right to use of woodland in the ideal state by using the relevant theories and models of microeconomics. Based on the prospect theory and psychological account theory of behavioral economics, this paper analyzes the reasons why the transfer of forestland use right has not been carried out on a large scale in China.

Based on Landsat–7ETM + images of 2007 and 2012 and Landsat–8 images of 2018, this study took Fuyang City, Anhui Province (Yingzhou District, Yingdong District, Yingquan District) as the research object, and made a quantitative analysis of land use/cover change in Fuyang City from 2007 to 2018 with the Environment for Visualizing Images (ENVI) software. According to the data of land use types in three phases, the article analyzes the development trend of various land use types and the main reasons for the changes of land use, which provides a certain basis for the urban planning and environmental construction of Fuyang City. The results show that with the rapid economic development and continuous improvement of the urbanization level in Fuyang City during 11 years, the area of various land types in the study area has changed greatly. The area of construction land area changed by 448.27 km², with an increase of 543.57%; the area of arable land changed by 597.52 km², with a decrease of 34.74%; the area of bare land changed by 26.00 km², with a decrease of 80.68%. The changes were closely related to the rapid economic and social development in the study area. Under the influence of environmental protection policies and environmental awareness, the area of forest land changed by 85.00 km², with an increase of 97.58%; the water area changed by 84.35 km², with an increase of 201.39%.

Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.

The present study assessed the potential of sediment loading in Beteni, Lauruk, Andheri, and Harpan sub-watersheds of Phewa Lake and estimated the sediment yield in the year 2020. Morphometry, land use/land cover, geology, climate, and human and development factors of the sub-watersheds were studied to assess the potential of sediment loading in the sub-watersheds. SRTM DEM was used for the computation of morphometric parameters and land use/land cover maps were prepared by using Landsat imagery. Geology, rainfall data, census data, and road maps were collected from various secondary sources. The sediment yields of the four sub-watersheds in the year 2020 were estimated by measuring the sediment volume deposited in the sediment retention ponds at the outlet of each sub-watershed. Results indicated that Beteni had the highest potential for sediment loading, while Harpan had the lowest. Likewise, the sediment yields for Beteni, Lauruk, Andheri, and Harpan sub-watersheds in 2020 were estimated at 1,420.67 m³/km²/year, 2,280.14 m³/km²/year, 1,666.77 m³/km²/year, and 766.42 m³/km²/year, respectively. To reduce sedimentation in Phewa Lake, it is recommended to regularly maintain siltation dams and construct check dams along the drainage slopes, alongside other soil conservation measures and appropriate land use practices in the upstream areas of the sub-watersheds.