Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Mapping Energy Expansion: Remote Sensing Insights into Oil and Gas Infrastructure and Land Use Changes in Midland, TX

Rapid expansion in global energy demand driven primarily by oil and gas consumption has spurred significant environmental concerns. This study delves into the intricate relationship between energy development and environmental impacts focusing on Midland County, Texas, a pivotal region within the Permian Basin. Leveraging satellite imagery and Geographic Information Systems (GIS) techniques, the research meticulously examines land use dynamics from 2001 to 2019. The findings illuminate a marked decline in vegetation health and density attributable to the burgeoning oil and gas infrastructure in the area. Moreover, the analysis underscores the emergence of barren lands and the displacement of agricultural areas, indicative of the profound alterations in land cover patterns over the study period. These insights underscore the urgent need for concerted efforts to mitigate the adverse environmental effects of energy expansion, emphasizing the importance of collaborative approaches to foster sustainable land use practices. Additionally, the study explores the socio-economic implications of land use changes, addressing how energy expansion affects local communities and economies. Previous studies have emphasized the need for comprehensive assessments of cumulative environmental impacts, advocating for the implementation of effective mitigation strategies.

Remote Sensing Investigation Method on Land Resources by A Geographic Information System

The human living and developing depend on the land. The remote sensing images in Keerqinhouqi, Inner Mongolia Autonomous Region, China, were processed . Supported by a geographical information system, the image map was formed by th e boundary making and overlaying to the precision-corrected remote sensed image . Finally, the current land use types were also classified and outlined. The area s were also calculated. The error is less than 10 precent, compared with the sur vey. Thus the proceduce is considered accepfable.

LAND RESOURCES SURVEY BY REMOTE SENSING AND ANALYSIS OF LAND CARRYING CAPACITY FOR POPULATION IN TUMEN RIVER REGION

The Tumen river region including Yanji,Tumen, Longjing and Hunchun cities is situated in the east part of Jilin Province.The region is an important economic exploitation area in the province. The total area is 10, 228. 86 km2.There are superior geographical location, rich natural rare, various gemorphological types and less farmaland in the region. The remote sensing technique is adopted in the survey of present landuse. The newest Landsat and CCT data are selected in the survey. Comparing the data obtained from remote sensing survey with the data from land detail investigation we can are that the paddy-field, garden for planting fruits, residential area and factory and mine, traffic land have increased in different extents, especially, the residential area is increased rapidly, but the forest land, grazing land have decreased. The unused land has been used. Land productive potentiality system is a multi-hierarchic comprehensive-complex system of natural economy.Its core is photosynthesis of green vegetation, which is affected by factors such as radiation,temperature, rainfall, soil fertility and management level. According to calculation of productive potentiality, the analysis of carrying capacity for population has ho done and the conclusion is drawn. After 2000,the population growth in the region will be reStricted by lack of are of farmland resources and level of grain production. Existing land and its reserve are can not carry a population more than 150×104.It is estmeted that the grain only depends on transportstion outside to meet the needs of population growth and social development after 2000.

Forest Resources Management Information System for Forest Farms Based on Remote Sensing Images and Web GIS

This study was to estabIish the forest resources management information system for forest farms based on the B/S structural WebGIS with trial forest farm of Hunan Academy of Forestry as the research field, forest resources field survey da-ta, ETM＋ remote sensing data and basic geographical information data as research material through the extraction of forest resource data in the forest farm, require-ment analysis on the system function and the estabIishment of required software and hardware environment, with the alm to realize the management, query, editing, analysis, statistics and other functions of forest resources information to manage the forest resources.

Application of Remote Sensing and Geographic Information System in Land Use and Land Cover Change

The integration and application of remote sensing （RS） and geographic in-formation system （GIS） in the study of the Land Use and Land Cover Change （LUCC） were summarized, as wel as researches on the monitoring dynamic changes in LUCC, driving force and application examples of the integration and the application of RS and GIS in simulation research. The methods and technical ap-proaches of RS and GIS in LUCC research were discussed. Views on the existing problems of the integration and the application of RS and GIS were put forward, and the future developing direction of LUCC technology was forecasted.

Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index

Otindag Sandy Land in China is an important ecological barrier to Beijing;the changes in its ecological quality are major concerns for sustainable development and planning of this area.Based on principal component analysis and path analysis,we first generated a modified remote sensing ecological index(MRSEI)coupled with satellite and ground observational data during 2001–2020 that integrated four local indicators(greenness,wetness,and heatness that reflect vegetation status,water,and heat conditions,respectively,as well as soil erosion).Then,we assessed the ecological quality in Otindag Sandy Land during 2001–2020 based on the MRSEI at different time scales(i.e.,the whole year,growing season,and non-growing season).MRSEI generally increased with an upward rate of 0.006/a during 2001–2020,with clear seasonal and spatial variations.Ecological quality was significantly improved in most regions of Otindag Sandy Land but degraded in the southern part.Regions with ecological degradation expanded to 18.64%of the total area in the non-growing season.The area with the worst grade of MRSEI shrunk by 15.83%of the total area from 2001 to 2020,while the area with the best grade of MRSEI increased by 9.77%of the total area.The temporal heterogeneity of ecological conditions indicated that the improvement process of ecological quality in the growing season may be interrupted or deteriorated in the following non-growing season.The implementation of ecological restoration measures in Otindag Sandy Land should not ignore the seasonal characteristics and spatial heterogeneity of local ecological quality.The results can explore the effectiveness of ecological restoration and provide scientific guides on sustainable development measures for drylands.

A New Fusion Technique of Remote Sensing Images for Land Use/Cover

In China, accelerating industrialization and urbanization followinghigh-speed economic development and population increases have greatly impacted land use/coverchanges, making it imperative to obtain accurate and up to date iufbimation on changes soas toevaluate their environmental effects. The major purpose of this study was to develop a new method tofuse lower spatial resolution multispectral satellite images with higher spatial resolutionpanchromatic ones to assist in land use/cover mapping.An algorithm of a new fusion method known asedge enhancement intensity modulation (EEIM) was proposed to merge two optical image data sets ofdifferent spectral ranges. The results showed that the EEIM image was quite similar in color tolower resolution multispectral images, and the fused product was better able to preserve spectralinformation. Thus, compared to conventional approaches, the spectral distortion of the fused imageswas markedly reduced. Therefore, the EEIM fusion method could be utilized to fuse remote sensingdata from the same or different sensors, including TM images and SPOT5 panchromatic images,providing high quality land use/cover images.

Cultivated Land Changes and Their Driving Forces——A Satellite Remote Sensing Analysis in the Yellow River Delta,China

Taking Kenli County in the Yellow River Delta, China, as the study area and using digital satellite remote sensing techniques, cultivated land use changes and their corresponding driving forces were explored in this study. An interactive interpretation and a manual modification procedure were carried out to acquire cultivated land information. An overlay method based on classification results and a visual change detection method which was supported by land use maps were employed to detect the cultivated land changes. Based on the changes that were revealed and a spatial analysis between cultivated land use and related natural and socio-economic factors, the driving forces for cultivated land use changes in the study area were determined.The results showed a decrease in cultivated land in Kenli County of 5321.8 ha from 1987 to 1998, i.e.,an average annual decrement of 483.8 ha, which occurred mainly in the central paddy field region and the northeast dry land region. Adverse human activities, soil salinization and water deficiencies were the driving forces that caused these cultivated land use changes.

基于Landsat影像的城市土地利用动态监测

土地是自然界不可或缺的资源,土地利用反映了人地关系。随着人口数量的持续增加和社会工业化、城市化的持续推进,如何科学地开发利用宝贵的土地资源越来越受到人们的重视。本文通过2013年和2023年的Landsat影像数据,结合遥感与GIS,对10年间合肥市土地资源进行监测与分析,结果表明,经过10年的城市发展,合肥市耕地面积减少了65707.92 hm 2,建设用地增长了39181.05 hm 2,林地减少了1728.27 hm 2,水域增加了28255.14 hm 2。

Monitoring urban land cover and vegetation change by multi-temporal remote sensing information

In order to analyze changes in human settlement in Xuzhou city during the past 20 years, changes in land cover and vegetation were investigated based on multi-temporal remote sensing Landsat TM images. We developed a hierarchical classifier system that uses different feature inputs for specific classes and conducted a classification post-processing approach to improve its accuracy. From our statistical analysis of changes in urban land cover from 1987 to 2007, we conclude that built-up land areas have obviously increased, while farmland has seen in a continuous loss due to urban growth and human activities. A NDVI difference approach was used to extract information on changes in vegetation. A false change information elimination approach was developed based on prior knowledge and statistical analysis. The areas of vegetation cover have been in continuous decline over the past 20 years, although some measures have been adopted to protect and maintain urban vegetation. Given the stability of underground coal exploitation since 1990s, urban growth has become the major driving force in vegetation loss, which is different from the vegetation change driven by coal exploitation mainly before 1990.

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

Establishing evaluation index system for desertification of Keerqin sandy land with remote sensing data

Keerqin sand land is located in the transitional terrain between the Northeast Plain and Inner Mongolia （42°41′-45°15′N, 118°35′-123°30′ E） in Northeast China and it is seriously affected by desertification. According to the configuration and ecotope of the earths surface, the coverage of vegetation, occupation ratio of bare sandy land and the soil texture were selected as evaluation indexes by using the field investigation data. The evaluation index system of Keerqin sandy desertification was established by using Remote Sensing data. and the occupation ratio of bare sandy land was obtained by mixed spectrum model. This index system is validated by the field investioation data and results indicate that it is suitable for the desertification evaluation of Keerqin.Foundation Item： This study is supported by a grant from the National Natural Science Foundation of China （No. 30371192）

Analysis of Land Use Change and Driving Force of Bole City Based on Remote Sensing Image

[Objectives] The land use change and its influence has been the frontier and hotspot in the research of the surface process of change. The aim of this study was to provide a reasonable scientific basis for the more reasonable use of regional land resources of Bole City by study of land use change and driving force of Bole City.[Methods] Through geometric correction, image mosaic and image registration processing and classification of the remote sensing images of Bole City in 2006, 2011 and 2016, the three images of land use change in land use types （land use change range, dynamic degree and variation degree） were studied, and the natural and social economy in terms of the driving forces of land use change were analyzed.[Results] In the 2006 to 2016 period, cultivated land of Bole City had the land use dynamic growth state, and the average growth rate was 0.26%; and forest land, construction land, water, grassland and unused land showed a decreasing trend, decreased by 0.23%, 0.22%, 0.75%, 3.85% and 1.52%, respectively. In the entire study period, the change of grassland was the biggest, the changes of unused land and water were the second, and the changes of cultivated land, construction land and forest land were lesser.[Conclusions] The main driving factors that effected on land use change of the study area were climate, industrialization, urbanization, social and economic activities, adjustment of agricultural structure and population expansion.

The Massive Expansion and Spatial Transformation of Potentially Contaminated Land Across China in 1990–2020 Observed from Remote Sensing and Big-data

Identifying and monitoring the spatiotemporal patterns of potentially contaminated land(PCL) in China is a key concern of ecological governance. However, the dynamics of PCL’s expansion remain unclear nationwide. Integrating high-resolution remote sensing images, a land-use/cover change database, crawler data from websites, and other multisource data, we produced a new dataset of China’s PCL in 1990, 2000, 2010, and 2020 using data fusion technology. Then we analyzed the spatiotemporal patterns of China’s PCL from 1990 to 2020. Our study shows that the acquired vector dataset of China’s PCL is of high quality and reliability, with an overall accuracy of 93.21%. The area of China’s PCL has kept growing for the past 30 years, and the growth rate was especially rapid during2000–2010, 2.32 and 6.13 times as rapid as that during 1990–2000 and 2010–2020, respectively. PCL has also been trending toward higher aggregation over markedly enlarged areas and has transferred progressively from north and southeast of China to northwest and southwest of China and Qinghai-Tibet Plateau. The patterns of China’s PCL have been driven by the joint factors of policies, mineral resources, economy, and others, among which policies and the economy have contributed more prominently to the long-term transition.Our study promotes the access to high-quality spatial data of PCL to facilitate environmental governance of mine wastes, pollution and land management.

Application of Remote Sensing and GIS for Modeling and Assessment of Land Use/Cover Change in Amman/Jordan

Modeling and assessment of land use/cover and its impacts play a crucial role in land use planning and formulation of sustainable land use policies. In this study, remote sensing data were used within geographic information system (GIS) to map and predict land use/cover changes near Amman, where half of Jordan’s population is living. Images of Landsat TM, ETM+ and OLI were processed and visually interpreted to derive land use/cover for the years 1983, 1989, 1994, 1998, 2003 and 2013. The output maps were analyzed by using GIS and cross-tabulated to quantify land use/cover changes for the different periods. The main changes that altered the character of land use/cover in the area were the expansion of urban areas and the recession of forests, agricultural areas (after 1998) and rangelands. The Markov chain was used to predict future land use/cover, based on the historical changes during 1983-2013. Results showed that prediction of land use/cover would depend on the time interval of the multi-temporal satellite imagery from which the probability of change was derived. The error of prediction was in the range of 2%-5%, with more accurate prediction for urbanization and less accurate prediction for agricultural areas. The trends of land use/cover change showed that urban areas would expand at the expense of agricultural land and would form 33% of the study area (50 km×60 km) by year 2043. The impact of these land use/cover changes would be the increased water demand and wastewater generation in the future.

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km2 and sparse vegetation (farmland and grassland) with a change of 53.97 km2, while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.

Monitoring Land Cover Change Using Remote Sensing (RS) and Geographical Information System (GIS): A Case of Golden Pride and Geita Gold Mines, Tanzania

This study monitored land cover change in the mining sites of Golden Pride Gold Mine (GPGM) and Geita Gold Mine (GGM), Tanzania. The satellite data for land cover classification for the years 1997, 2010 and 2017 were obtained from the United States Geologic Survey Departments (USGS) online database and were analyzed using Arc GIS 10 software. Supervised classification composed of seven classes namely forest, bushland, agriculture, water, bare soil, urban area and grassland, was designed for this study, in order to classify Landsat images into thematic maps. In addition, future land cover changes for the year 2027 were simulated using a Cellular Automata (CA)-Markov model after validating the model using the Land Cover for the year 2017. The results from the LULC analysis showed that forest was the most dominant land cover type in 1997 at GPGM and GGM covering 510 ha (52.1%) and 9833 ha (49.7%) respectively. In 2017, the forest area decreased and the bushland replaced forest to be the most dominant land cover type covering 219 ha (22.4%) for GPGM and 8878 ha (44.9%) for GGM. Based on the CA-Markov model, a predicted land cover map for 2027 was dominated by forest covering 340 ha (34.7%) and 8639 ha (43.7%) for GPGM and GGM respectively. An overall accuracy and kappa coefficient for GPGM were 74.7% and 70.2% respectively and for GGM were 71.4% and 66.1% respectively. Thus, land cover changes resulting from mining activities involve reduction of forest land hence endangers biodiversity. GIS and remote sensing technologies are potential to detect the trend of changes and predict future land cover. The findings are crucial as it provides basis for land use planning and intensifies monitoring programs in the mining areas of Tanzania.