Monitoring Land Use Dynamics in Chanthaburi Province of Thailand Using Digital Remotely Sensed Images

A comprehensive method of image classification was developed for monitoring land use dynamics in Chanthaburi Province of Tailand. RS (Remote Sensing), GIS (Geographical Information System), GPS (Global Positioning System) and ancillary data were combined by the method which adopts the main idea of classifying images by steps from decision tree method and the hybridized supervised and unsupervised classification. An integration of automatic image interpretation, ancillary materials and expert knowledge was realized. Two subscenes of Landsat 5 Thematic Mapper (TM) images of bands 3, 4 and 5 obtained on December 15, 1992, and January 17, 1999, were used for image processing and spatial data analysis in the study. The overall accuracy of the results of classification reached 90%, which was verified by field check.Results showed that shrimp farm land, urban and traffic land, barren land, bush and agricultural developing area increased in area, mangrove, paddy field, swamp and marsh land, orchard and plantation, and tropical grass land decreased, and the forest land kept almost stable. Ecological analysis on the land use changes showed that more attentions should be paid on the effect of land development on ecological environment in the future land planning and management.

WISE EXPLOITATION OF NEWLY GROWING LAND RESOURCES——An Assessment on Land-use Change of Chongming Island Using GIS

Chongming Island, the third largest island in China and the largest alluvialisland in the world, is situated in the north of Shanghai Municipality at the mouth of theChangjiang (Yangtze) River. Along the fertile and prosperous sea coast there are a total area ofover 120 x 10~3ha, with a population of 735 000, accruing some 500ha of new tidal land resourcescome from silt, sand and mud carried by the Changjiang River every year, extending about 140m peryear. This dynamic process of alluvial growth has run for some 1500 years. Mudflat on ChongmingIsland at the mouth of the Changjiang River is a resting ground for migratory birds and host morethan a hundred species, including rare cranes and geese. But the local people keep reclaiming thetidal land for economic development. Obviously, it is crucial to have a well-concerted plan forfuture exploitation. In this study, we attempted to investigate the status changes of land use andwild life habitats on Chongming Island in recent 10 years, and then analyzed different humanactivities and their effects on wild life habitats using satellite image data (1990, 1997 and 2000)as well as field survey. Based on the analysis, this study explored the relationships between islandgrowth and land use/cover change (LUCC), predicted what the habitat would be like in the future andtried to find more effective use of this new growing resource. At last, this study provided somepreliminary management plans for Chongming Island that will coordinate the development of localeconomies and the conservation of wild life and their habitats.

Assessing Spatio-Temporal Land Cover Changes in Dhund River Basin, Eastern Rajasthan (India), Using Multi-Temporal Landsat Data

Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Land cover identification, delineation and mapping is important for planning activities, resource management and global monitoring studies while baseline mapping and subsequent monitoring is done by application of land use to get timely information about quantity of land that has been used. The present study has been carried out in Dhund river watershed of Jaipur, Rajasthan which covers an area of about 1828 sq∙km. The minimum and maximum elevation of the area is found to be 214 m and 603 m respectively. Land use and land cover changes of three decades from 1991 to 2021 have been interpreted by using remotes sensing and GIS techniques. ArcGIS software (Arc map 10.2), SOI topographic map, Cartosat-1 DEM and satellite data of Landsat 5 and Landsat 8 have been used for interpretation of eleven classes. The study shows an increase in cultivated land, settlement, waterbody, open forest, plantation and mining due to urbanization because of increasing demands of food, shelter and water while a decrease in dense forest, river, open scrub, wasteland and uncultivated land has also been marked due to destruction of aforementioned by anthropogenic activities such as industrialization resulting in environmental degradation that leads to air, soil and water pollution.

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.

Study of Urban Sprawl and Its Impact on Vegetation, Land Surface Temperature and Air Pollution Using Remote Sensing and GIS in Kathmandu Valley from 2015 to 2020

The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative influence on the environment. Recently, Kathmandu was named as the most polluted city in Asia. Urban sprawl has had a negative influence on Kathmandu’s residents in several ways. The state of urban sprawl and the effects it has had on the Kathmandu Valley have been examined using land sat imagery. In this study, IDW was used in GIS to analyze the pollution status using data of PM 2.5 and PM 10 obtained from various monitoring sites. A supervised classification was used to create a LULC map of Kathmandu for the years 2015, 2018, and 2020. To assess the state of the vegetation and determine whether the Kathmandu Valley is being affected by urban heat, NDVI and Land sat temperature calculations were also made. The study’s results were obtained using remote sensing and GIS technology. The built-up area in Kathmandu Valley has grown by 20% over the past five years, impacting land use patterns and deteriorating vegetation cover. Due to the rise of built-up area, which is a good heat absorber, the temperature in the Kathmandu Valley is rising along with the degradation of the vegetation cover. The pollution in the Kathmandu Valley is at its worst, and residents are compelled to breathe air that is significantly more polluted than the prescribed limit.

Simulation of urban affordable housing land-use evolution based on CA-MAS model

In order to simulate the evolution of affordable housing land use a dynamic model that combines cellular automata CA and a multi-agent system MAS is established.This paper aims to utilize the approach of decision factors on site selection of affordable housing through a literature review to construct a hierarchy model of those factors identifying the weight of each factor by an analytic hierarchy process AHP .Based on those weight factors the CA-MAS model is designed. Nanjing city is taken as an example to verify the feasibility of the model.The results show that the CA-MAS model is pragmatic and effective in simulating evolution of affordable housing land use which also promotes the fundamental understanding and perception of the development of affordable housing and urbanization.

Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models

This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000,respectively,by combining spatial land allocation simulation using the CLUE-S model,and numerical land demand prediction using the Markov model.The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices.Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development:1) urban development following existing trends;and 2) under a strict farmland control.The simulations suggested that under either mode,urbanized areas would expand at the expense of land for other uses.This expansion was predicted to dominate the land-use conversions between 2005 and 2015,and was expected to be accompanied by an extensive loss of farmland.The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County,Changping District and Fangshan District.Also,the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Beijing,suggesting a need for priority monitoring and protection.

Quantifying Land Use Change in Zhejiang Coastal Region,China Using Multi-Temporal Landsat TM/ETM+Images

Landsat Thematic Mapper （TM） and Enhanced Thematic Mapper Plus （ETM＋） images in 1985, 1986, 1993, 1994 and 2001 were used to quantify the land use and land cover changes （LUCC） in the Zhejiang coastal region with a stratified unsupervised classification technique in conjunction with visual interpretation and to attempt an identification of the socioeconomic driving forces. In level I an overall accurate classification was achieved using a modified Anderson＇s Ⅰ/Ⅱ/Ⅲ-level classification scheme. The overall accuracy of the land use classification at Anderson level Ⅰ were 89.7% （1985）, 91.6% （1993）, and 90.4% （2001）. The most rapid land use change was a dramatic increase in urban or built-up areas, which quadrupled from 1985 to 2001. Over 90% of this newly expanded built-up area was originally paddy fields or other croplands. In different parts of the Zhejiang coastal region, urban land expansion was spatially uneven. Temporally, land use development did not stabilized, and the two study periods of time （1985-1993 and 1993-2001） had different transition styles. Socioeconomic factors, such as gross domestic product, total population, and financial expenditure, were all highly correlated with the expansion of urban or built-up areas. Based on the degree of urban sprawl and socioeconomic factors, cities and towns were further divided into six subgroups, which may help decision makers improve land use for the region.

AN ASSESSMENT OF LAND USE CHANGES IN FUQING COUNTYOF CHINA USING REMOTE SENSING TECHNOLOGY

Fuqing County of southeast China has witnessed significant land use changes during the last decade. Re mote sensing technology using multitemporal Landsat TM images was used to characterize land use types and to monitor land use changes in the county. Two TM scenes from 1991 and 1996 were used to cover the county and a five-year time period. Digital image processing was carried out for the remotely sensed data to produce classified images. The images were further processed using GIS software to generate GIS databases so that the data could be further spatially analyzed taking the advantages of the software. Land use change areas were determined by using the change detection technique. The comparison of the two classified TM images using the above technologies reveals that during the five study years, a large area of arable lands in the county has been lost and deforestation has taken place largely because of the dramatic in crease in built-up land and orchard. The conclusive statistical information is useful to understand the processes, causes and impacts of the land use changes in the county. The major driving force to the land use changes in the county ap peared to be the rapid economic development. The decision makers of the county have to pay more attention to the land use changes for the county’s sustainable development.

Urban Land Use Change Detection Using Multisensor Satellite Images

Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

Analyzing Land-use Change in Farming-pastoral Transitional Region Using Autologistic Model and Household Survey Approach

Land change science （LCS） strives to understand and model land-use change, which will further advance the understanding of multiple issues in the socio-ecological systems. Based on GIS/RS techniques, autologistic model, and household survey method, this study investigated major land use changes and their causes from 1978 to 2008 in Uxin Banner （county-level）, Inner Mongolia in China and then developed an understanding of the relationships between household livelihood and land-use pattern. Results showed that cultivated land increased from 1988 to 2000, and leveled offafter 2000. Built-up land increased stably for the period 1978 2008. The change of grassland and bare land differed among the three periods. From 1978 to 1988, grassland increased by 23.3%, and bare land decreased by 20.48%. From 1988 to 2000, bare land expanded by 1.7%, but grassland declined by 1.3%. From 2000 to 2008, an increase in grassland area by 1.8% was observed, but a decrease in bare land area by 9.0% was witnessed. The autologistic models performed better than logistic models as indicated by lower Akaike Information Criterion （AIC） values. Factors associated with human activities significantly correlated with the change of cultivated land, forest land, grassland, and built-up land. The produce prices and extensive cultivated land use are major issues in the farming area. This study suggests that completing land circulation systems and maintaining the stability of price are effective solutions. By contrast, reclamation and overgrazing are major concerns in the pastoral areas. Implementing environmental policies effectively, transferring population out of rural pastoral areas, and developing modem animal husbandry are effective ways to address these issues.

Study on Soil Gleization in Subtropic Region of China Using LBV Transformed Landsat Images

This paper describes a new type of transformed Landsat images (LBV images) and their application in discriminating soil gleization in subtropic region of China. LBV transformation was worked out by the present author for extracting useful information from original landsat images. Using this method three black and white images, L image, B image and V image, were computer generated from original bands of a Landsat scene, which covers a.large area of 34 528 km2 in Hubei and Hunan provinces in south China. Then a color composite was produced by these three images. This kind of black-and-white and color images contained rich and definite geographic information. By a field work, the relationship between the colors on the composite and the land use/cover categories on the ground was established. 37 composite colors and 70 ground feature categories can be discriminated altogether. Finally, 17 land use/cover categories and 10 subregions suffering from soil gleization were determined, and the gleization area for the study area was estimated to be 731.3 km2.

Selection of optimal land uses for the reclamation of surface mines by using evolutionary algorithms

A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure.

Assessment of ecological quality in Northwest China(2000-2020)using the Google Earth Engine platform:Climate factors and land use/land cover contribute to ecological quality

The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals(UN SDGs).The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover,and the changes in ecological quality in this arid region over the last two decades are not well understood.This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level.In this study,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)products to generate remote sensing ecological index(RSEI)on the Google Earth Engine(GEE)platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades(from 2000 to 2020).We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality.We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products.Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%.The ecological quality in Xinjiang has increased significantly over the last two decades,with the northern part of this region having a better ecological quality than the southern part.The areas with slightly improved ecological quality accounted for 31.26%of the total land area of Xinjiang,whereas only 3.55%of the land area was classified as having a slightly worsen(3.16%)or worsen(0.39%)ecological quality.The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature,precipitation,closed shrublands,grasslands and savannas were the top five environmental factors affecting the changes in RSEI.Environmental factors were allocated different weights for different RSEI categories.In general,the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial.Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

A Modified Self-adaptive Method for Mapping Annual 30-m Land Use/Land Cover Using Google Earth Engine:A Case Study of Yangtze River Delta

Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.However,obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities,training data,and workflow design.Using the Google Earth Engine(GEE),which provides a catalog of multi-source data and a cloud-based environment,we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery.Our major contribution is a hybrid approach that includes three major components:1)a high-quality training dataset derived from multi-source LULC products,filtered by k-means clustering analysis;2)a yearly 39-band stack feature space,utilizing all available Landsat data and DEM data;and 3)a self-adaptive Random Forest(RF)method,introduced for LULC classification.Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33%in the entire Delta.The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability.In addition,as the proposed workflow is based on open source data and the GEE cloud platform,it can be used anywhere by anyone in the world.

Impacts of land use/cover change on water balance by using the SWAT model in a typical loess hilly watershed of China

Land use/cover change(LUCC)plays a key role in altering surface hydrology and water balance,finally affect-ing the security and availability of water resources.However,mechanisms underlying LUCC determination of water-balance processes at the basin scale remain unclear.In this study,the Soil and Water Assessment Tool(SWAT)model and partial least squares regression were used to detect the effects of LUCC on hydrology and water components in the Zuli River Basin(ZRB),a typical watershed of the Yellow River Basin.In general,three recommended coefficients(R^(2)and E ns greater than 0.5,and P bias less than 20%)indicated that the output results of the SWAT model were reliable and that the model was effective for the ZRB.Then,several key findings were obtained.First,LUCC in the ZRB was characterized by a significant increase in forest(21.61%)and settlement(23.52%)and a slight reduction in cropland(-1.35%),resulting in a 4.93%increase in evapotranspiration and a clear decline in surface runoffand water yield by 15.68%and 2.95%at the whole basin scale,respectively.Second,at the sub-basin scale,surface runoffand water yield increased by 14.26%-36.15%and 5.13%-15.55%,respectively,mainly due to settlement increases.Last,partial least squares regression indicated that urbanization was the most significant contributor to runoffchange,and evapotranspiration change was mainly driven by forest expansion.These conclusions are significant for understanding the relationship between LUCC and water balance,which can provide meaningful information for managing water resources and the long-term sustainability of such watersheds.

Evaluating the impacts of land use and land cover changes on surface air temperature using the WRF-mosaic approach

Satellite-derived land surface data in 1980 and 2010 were used to represent land use and land cover（LULC） changes caused by the rapid economic development and human activities that have occurred over the past few decades in East Asia and China. The effects of LULC changes on the radiation budget and 2-m surface air temperature（SAT） were explored for the period using the Weather Research and Forecasting（WRF） model. The mosaic approach, which considers the N-most abundant land use types within a model grid cell（here, N = 3） and precisely describes the subgridscale LULC changes, was adopted in the integrations. The impacts of LULC changes based on two 36-year integrations showed that SAT generally decreased, with the sole exception being over eastern China, resulting in decreased SAT in China（-0.062 °C） and East Asian land areas（EAL,-0.061 °C）. The LULC changes induced changes in albedo, which influenced the radiation budget. The radiative forcings at the top of the atmosphere were-0.56 W m-2 across the whole of China, and-0.50 W m-2 over EAL. Meanwhile, the altered roughness length mainly influenced near-surface wind speeds, large-scale and upward moisture fluxes, latent heat fluxes, and cloud fractions at different altitudes. Though the impacts caused by the LULC changes were generally smaller at regional scales, the values at local scales were much stronger.

Using the Lund-Potsdam-Jena Model to Understand the Different Responses of Three Woody Plants to Land Use in China

In this study, the approach of conditional nonlinear optimal perturbation related to initial perturbation （CNOP-I） was employed to investigate the maximum variations in plant amount for three main woody plants （a temperate broadleaved evergreen, a temperate broadleaved summergreen, and a boreal needleleaved evergreen） in China. The investigation was conducted within a certain range of land use intensity using a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model （LPJ DGVM）. CNOP-I represents a class of deforestation and can be considered a type of land use with respect to the initial perturbation. When deforestation denoted by the CNOP-I has the same intensity for all three plants, the variation in plant amount of the boreal needleleaved evergreen in northern China is greater than the variation in plant amount of both the temperate broadleaved evergreen and temperate broadleaved summergreen in southern China. As deforestation intensity increases, the plant amount variation in the three woody plant functional types carbon changes, in a nonlinear fashion. The impact of land use on plant functional types is minor because the interaction between climate condition and land use is not considered in the LPJ model. Finally, the different impacts of deforestation on net primary production of the three plant functional types were analyzed by modeling gross primary production and autotrophic respiration. Our results suggest that the CNOP-I approach is a useful tool for exploring the nonlinear and different responses of terrestrial ecosystems to land use.