Spatiotemporal characteristics and driving mechanisms of land use/land cover(LULC)changes in the Jinghe River Basin,China

Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.

Spatiotemporal evolution and future simulation of land use/land cover in the Turpan-Hami Basin,China

The Turpan-Hami(Tuha)Basin in Xinjiang Uygur Autonomous Region of China,holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative,necessitating a holistic understanding of the spatiotemporal evolution of land use/land cover(LULC)to foster sustainable planning that is tailored to the region's unique resource endowments.However,existing LULC classification methods demonstrate inadequate accuracy,hindering effective regional planning.In this study,we established a two-level LULC classification system(8 primary types and 22 secondary types)for the Tuha Basin.By employing Landsat 5/7/8 imagery at 5-a intervals,we developed the LULC dataset of the Tuha Basin from 1990 to 2020,conducted the accuracy assessment and spatiotemporal evolution analysis,and simulated the future LULC under various scenarios via the Markov-Future Land Use Simulation(Markov-FLUS)model.The results revealed that the average overall accuracy values of our LULC dataset were 0.917 and 0.864 for the primary types and secondary types,respectively.Compared with the seven mainstream LULC products(GlobeLand30,Global 30-meter Land Cover with Fine Classification System(GLC_FCS30),Finer Resolution Observation and Monitoring of Global Land Cover PLUS(FROM_GLC PLUS),ESA Global Land Cover(ESA_LC),Esri Land Cover(ESRI_LC),China Multi-Period Land Use Land Cover Change Remote Sensing Monitoring Dataset(CNLUCC),and China Annual Land Cover Dataset(CLCD))in 2020,our LULC data exhibited dramatically elevated overall accuracy and provided more precise delineations for land features,thereby yielding high-quality data backups for land resource analyses within the basin.In 2020,unused land(78.0%of the study area)and grassland(18.6%)were the dominant LULC types of the basin;although cropland and construction land constituted less than 1.0%of the total area,they played a vital role in arid land development and primarily situated within oases that form the urban cores of the cities of Turpan and Hami.Between 1990 and 2020,cropland and construction land exhibited a rapid expansion,and the total area of water body decreased yet resurging after 2015 due to an increase in areas of reservoir and pond.In future scenario simulations,significant increases in areas of construction land and cropland are anticipated under the business-as-usual scenario,whereas the wetland area will decrease,suggesting the need for ecological attention under this development pathway.In contrast,the economic development scenario underscores the fast-paced expansion of construction land,primarily from the conversion of unused land,highlighting the significant developmental potential of unused land with a slowing increase in cropland.Special attention should thus be directed toward ecological and cropland protection during development.This study provides data supports and policy recommendations for the sustainable development goals of Tuha Basin and other similar arid areas.

Dynamics of Land Use/Land Cover Considering Ecosystem Services for a Dense-Population Watershed Based on a Hybrid Dual-Subject Agent and Cellular Automaton Modeling Approach

Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions,leading to potential conflicts among natural and human-related issues as well as among stakeholders.This study introduced economic standards for farmers.A hybrid approach(CA-ABM)of cellular automaton(CA)and an agent-based model(ABM)was developed to effectively deal with social and land-use synergic issues to examine human–environment interactions and projections of land-use conversions for a humid basin in south China.Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change.The major modules of the CA-ABM are initialization,migration,assets,land suitability,and land-use change decisions.Empirical estimates of the factors influencing the urban land-use conversion probability were captured using parameters based on a spatial logistic regression(SLR)model.Simultaneously,multicriteria evaluation(MCE)and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion.An agent-based CA-SLR-MCE-Markov(ABCSMM)land-use conversion model was proposed to explore the impacts of policies on land-use conversion.This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services.The results demonstrated land-use simulations under multi-policy scenarios,revealing the usefulness of the model for normative research on land-use management.

Riparian habitat quality as an indicator of land use/land cover effects on riverine water quality

Riparian land use/land cover(LULC)plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients.Nevertheless,establishing a direct relationship between water quality and LULC is challenging due to the multi-indicator nature of both factors.Water quality encompasses a multitude of physical,chemical,and biological parameters,while LULC represents a diverse array of land use types.Riparian habitat quality(RHQ)serves as an indicator of LULC.Yet,it remains to be seen whether RHQ can act as a proxy of LULC for assessing the impact of LULC on riverine water quality.This study examines the interplay between RHQ,LULC and water quality,and develops a comprehensive indicator to predict water quality.We measured several water quality parameters,including pH(potential of hydrogen),TN(total nitrogen),TP(total phosphorus),T_(water)(water temperature),DO(dissolved oxygen),and EC(electrical conductivity)of the Yue and Jinshui Rivers draining to the Han River during 2016,2017 and 2018.The water quality index(WQI)was further calculated.RHQ is assessed by the InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)model.Our study found noticeable seasonal differences in water quality,with a higher WQI observed in the dry season.The RHQ was strongly correlated with LULC compositions.RHQ positively correlated with WQI,and DO concentration and vegetation land were negatively correlated with T_(water),TN,TP,EC,cropland,and construction land.These correlations were stronger in the rainy season.Human-dominated land,such as construction land and cropland,significantly contributed to water quality degradation,whereas vegetation promoted water quality.Regression models showed that the RHQ explained variations in WQI better than LULC types.Our study concludes that RHQ is a new and comprehensive indicator for predicting the dynamics of riverine water quality.

Spatiotemporal dynamics of land use/land cover(LULC)changes and its impact on land surface temperature:A case study in New Town Kolkata,eastern India

Rapid urbanization creates complexity,results in dynamic changes in land and environment,and influences the land surface temperature(LST)in fast-developing cities.In this study,we examined the impact of land use/land cover(LULC)changes on LST and determined the intensity of urban heat island(UHI)in New Town Kolkata(a smart city),eastern India,from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands.This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis(Getis–Ord Gi^(*) method)to examine the impact of LULC changes on urban thermal environment.This study noticed that the area percentage of built-up land increased rapidly from 21.91%to 45.63%during 1991–2021,with a maximum positive change in built-up land and a maximum negative change in sparse vegetation.The mean temperature significantly increased during the study period(1991–2021),from 16.31℃to 22.48℃in winter,29.18℃to 34.61℃in summer,and 19.18℃to 27.11℃in autumn.The result showed that impervious surfaces contribute to higher LST,whereas vegetation helps decrease it.Poor ecological status has been found in built-up land,and excellent ecological status has been found in vegetation and water body.The hot spot and cold spot areas shifted their locations every decade due to random LULC changes.Even after New Town Kolkata became a smart city,high LST has been observed.Overall,this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment,and appropriate planning is needed to reduce LST.This study can help policy-makers create sustainable smart cities.

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.

Modelling Land Use/Land Cover Change of River Rwizi Catchment, South-Western Uganda Using GIS and Markov Chain Model

Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.

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.

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.

Land Use/Land Cover and Forest Canopy Density Monitoring of Wafi-Golpu Project Area, Papua New Guinea

This study aims to examine the use of Remote Sensing and Geographical Information System (GIS) technology in land use/land cover mapping to aide sustainable planning and development in the Wafi-Golpu project area. At the same time, this study examines an existing method of Forest Canopy Density (FCD) model to estimate forest canopy density of the proposed deforestation site, which is known as the Advanced Exploration Feasibility Study Activities (AEFSA) area within the Wafi-Golpu Project site. The FCD model calculates the forest canopy density using the three (3) indices of vegetation, soil and shadow from the Landsat-8 Operational Land Imager (OLI) satellite image of year 2013. In this study an attempt has been made to monitor the forest loss or degradation during deforestation in a natural forest stand of the Wafi-Golpu project area using forest FCD mapping and monitoring model and the findings of the study will assist the project planners and developers with their work on forest rehabilitation and reforestation for the purposes of sustainable forest management. The result of the work shows that a considerable amount of forest loss will be undertaken during the AEFSA deforestation exercise and also the findings show that a reliable land use/land cover map will greatly assist sustainable development in a resource project development period.

An Appraisal of Land Use/Land Cover Change Scenario of Tummalapalle, Cuddapah Region, India—A Remote Sensing and GIS Perspective

The study was aimed at appraising the changing land use/land cover scenario of Tummalapalle region in Cuddapah district of Andhra Pradesh using Remote sensing data and GIS technology. The region is considered as it has rich uranium reserves and is experiencing a remarkable expansion in recent times. The land use/land cover change analysis was carried out using IRS P6 LISS-III and LANDSAT-8 OLI multitemporal data pertaining to the years 2006 and 2016. The image classification resulted in five major land use/land cover classes namely built-up, agricultural, forest, wasteland and water bodies. The study noticed that the areas under built-up and agricultural classes are found increased from 0.94 km2 (0.84%) to 2.75 km2 (2.44%) and 61.68 km2 (54.84%) to 63.91 km2 (56.82%), respectively during 2006-2016. Area under forest, wasteland and water bodies are found decreased considerably from 3.09 km2 (2.75%) to 0.86 km2 (0.76%), 43.71 km2 (38.56%) to 42.60 km2 (37.88%) and 3.05 km2 (2.71%) to 2.35 km2 (2.09%), respectively. The study recommends development of industrial based economy by optimally utilizing the existing land resource (scrub and wasteland classes) and simultaneously extending the agricultural practices to other possible areas to make them more productive.

Impact of land use/land cover types on surface humidity in northern China in the early 21^(st)century

In the context of global change,it is essential to promote the rational development and utilization of land resources,improve the quality of regional ecological environment,and promote the harmonious development of human and nature for the regional sustainability.We identified land use/land cover types in northern China from 2001 to 2018 with ENVI images and ArcGIS software.Meteorological data were selected from 292 stations in northern China,the potential evapotranspiration was calculated with the Penman-Monteith formula,and reanalysis humidity and observed humidity data were obtained.The reanalysis minus observation(RMO,i.e.,the difference between reanalysis humidity and observed humidity)can effectively characterize the impact of different land use/land cover types(forestland,grassland,cultivated land,construction land,water body and unused land)on surface humidity in northern China in the early 21^(st) century.The results showed that from 2001 to 2018,the area of forestland expanded(increasing by approximately 1.80×10^(4) km^(2)),while that of unused land reduced(decreasing by approximately 5.15×10^(4) km^(2)),and the regional ecological environment was improved.Consequently,land surface in most areas of northern China tended to be wetter.The contributions of land use/land cover types to surface humidity changes were related to the quality of the regional ecological environment.The contributions of the six land use/land cover types to surface humidity were the highest in northeastern region of northern China,with a better ecological environment,and the lowest in northwestern region,with a fragile ecological environment.Surface humidity was closely related to the variation in regional vegetation coverage;when the regional vegetation coverage with positive(negative)contributions expanded(reduced),the land surface became wetter.The positive contributions of forestland and water body to surface humidity were the greatest.Unused land and construction land were associated with the most serious negative contributions to surface humidity.Affected by the regional distribution pattern of vegetation,surface humidity in different seasons decreased from east to west in northern China.The seasonal variation in surface humidity was closely related to the growth of vegetation:surface humidity was the highest in summer,followed by autumn and spring,and the lowest in winter.According to the results,surface humidity is expected to increase in northeastern region of northern China,decrease in northern region,and likely increase in northwestern region.

Land use/land cover change responses to ecological water conveyance in the lower reaches of Tarim River,China

The Tarim River is the longest inland river in China and is considered as an important river to protect the oasis economy and environment of the Tarim Basin.However,excessive exploitation and over-utilization of natural resources,particularly water resources,have triggered a series of ecological and environmental problems,such as the reduction in the volume of water in the main river,deterioration of water quality,drying up of downstream rivers,degradation of vegetation,and land desertification.In this study,the land use/land cover change(LUCC)responses to ecological water conveyance in the lower reaches of the Tarim River were investigated using ENVI(Environment for Visualizing Images)and GIS(Geographic Information System)data analysis software for the period of 1990-2018.Multi-temporal remote sensing images and ecological water conveyance data from 1990 to 2018 were used.The results indicate that LUCC covered an area of 2644.34 km^(2) during this period,accounting for 15.79%of the total study area.From 1990 to 2018,wetland,farmland,forestland,and artificial surfaces increased by 533.42 km^(2)(216.77%),446.68 km^(2)(123.66%),284.55 km^(2)(5.67%),and 57.51 km^(2)(217.96%),respectively,whereas areas covered by grassland and other land use/land cover types,such as Gobi,bare soil,and deserts,decreased by 103.34 km2(14.31%)and 1218.83 km2(11.75%),respectively.Vegetation area decreased first and then increased,with the order of 2010<2000<1990<2018.LUCC in the overflow and stagnant areas in the lower reaches of the Tarim River was mainly characterized by fragmentation,irregularity,and complexity.By analyzing the LUCC responses to 19 rounds of ecological water conveyance in the lower reaches of the Tarim River from 2000 to the end of 2018,we proposed guidelines for the rational development and utilization of water and soil resources and formulation of strategies for the sustainable development of the lower reaches of the Tarim River.This study provides scientific guidance for optimal scheduling of water resources in the region.

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

Land Use/Land Cover (LULC) Dynamics in a Semi-Arid Watershed in Eastern Rajasthan, India Using Geospatial Tools

Land use/land cover (LULC) change analysis has become a unique approach in determining the extent of degradation of natural resources within a given period of time. Remote sensing and GIS techniques have proved to be efficient tools for mapping and analyzing LULC changes over the last few decades. LULC change analysis has been carried out in Ruparel watershed which is situated in Alwar district, Eastern Rajasthan, India, based on visual image interpretation and change detection analysis of multi-temporal satellite data pertaining to IRS-P6 LISS III data of 2004 (Path-Row 95:52), IRS-P6 LISS III of 2014 (Path-Row 95:52) and IRS-R2A LISS III data of 2021. Visual image interpretation led to the delineation of 13 LULC classes using ArcGIS 10.5 software and include categories such as cultivated land, fallow land dense forest, open forest, degraded forest, open scrub, gullied/ravenous land, settlement/built-up land, River/waterbody, dry waterbody/dry river, plantation, barren/rocky/stony waste, and stone quarry. Results of the analysis depict significant LULC changes that have taken place in the area from 2004 to 2021. LULC categories such as cultivated land and settlement/built-up land have reported major changes in terms of their increase with 56.42 km2 (4.63%) and 31.9 km2 (2.63%) respectively primarily because of an increase in population. Likewise, the dense forest has reported a decrease of 33.78 km2 (2.78%) in its area and has been converted into degraded forest i.e., 32.04 km2 (2.64%) and open forest 2.85 km2 (0.24%) due to increased human exploitation of forest resources and mining activities taking place within the forested area. The study area needs the immediate attention of policymakers and stakeholders as the study area being part of the National Capital Region (NCR) will see excessive in-migration of the population in coming years which will further deplete the precious resources in the area.

Prioritization of Sub-Watersheds in Ruparel Watershed, Rajasthan Based on Morphometric and Land Use/Land Cover Analysis Using Remote Sensing and GIS

Watershed prioritization is considered as the most significant aspect in watershed resource management and development program. The present work attempts to prioritize seventeen sub-watersheds in Ruparel watershed of Alwar district of Rajasthan, India. For prioritization of sub-watersheds, morphometric and land use/land cover (LULC) analysis were performed using remote sensing and GIS. Base map of the study area has been derived from SOI toposheet on 1:50,000 scale whereas LULC mapping was done using IRS P6 LISS III data. Standard methods for drainage morphometry have been followed for computing morphometric parameters such as linear and shape for seventeen sub-watersheds and allotted ranks based on their relationship with erodibility and a compound value has been calculated for final ranking. Five main LULC categories were computed and were assigned priority ranks and subsequently a compound parameter was determined for final ranking. Integration of both morphometric and LULC results reveal that SBW5, SBW7, SBW12 and SBW16 are the common sub-watersheds that fall under high priority, SBW3 falls under Medium category and SBW11 comes under low priority. The results of the analysis can be used to identify the sub-watersheds which need immediate restoration and will eventually help in watershed resource management for sustainable development.

Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS

Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.

Impacts of Regional-Scale Land Use/Land Cover Change on Diurnal Temperature Range

The NCAR Community Atmosphere Model(CAM4.0)was used to investigate the climate efects of land use/land cover change(LUCC).Two simulations,one with potential land cover without significant human intervention and the other with current land use,were conducted.Results show that the impacts of LUCC on diurnal temperature range(DTR)are more significant than on mean surface air temperature.The global average annual DTR change due to LUCC is–0.1℃,which is three times as large as the mean temperature change.LUCC influences regional DTR as simulated by the model.In the mid-latitudes,LUCC leads to a decrease in DTR,which is mainly caused by the reduction in daily maximum temperature.However,there are some diferences in the low latitudes.The reduction in DTR in East Asia is mainly the result of the decrease in daily maximum temperature,while in India,the decrease in DTR is due to the increase in daily minimum temperature.In general,the LUCC significantly controls the DTR change through the changes in canopy evaporation and transpiration.

Spatial features of land use/land cover change in the United States

With the classifi cation data covering American land-use/land-cover (LUCC) with 30 m resolu tion from the project of National Land Cover Data (NLCD), we normalize d them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and featur es of American LUCC as well as the influence of population and altit ude on the land-use grade in light of methods of sampling analysis a nd correlation study. Based on the analysis, we concluded that forestr y and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9. 54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the sp atial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of la nd-use grade is not significanct.