The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Diff...The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.展开更多
Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.M...Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.Machine learn-ing methods for land use and land cover(LULC)classification are vital for monitoring environmental changes.Remote sensing advancements increase the potential for classifying land cover,which requires assessing algorithm ac-curacy and efficiency for fragile environments.This research identifies the best algorithms for LULC monitoring and developing adaptive methods for sensi-tive ecosystems.Landsat-9 imagery from January to April 2023 facilitated land use class identification.Preprocessing in the Google Earth Engine applied spec-tral indices such as the NDVI,NDWI,BSI,and NDBI.Supervised classification uses random forest(RF),support vector machine(SVM),classification and re-gression trees(CARTs),gradient boosting trees(GBTs),and naïve Bayes.An accuracy assessment was used to determine the optimal classifiers for future land use analyses.The evaluation revealed that the RF model achieved 84.4%accuracy with a 0.85 weighted F1 score,indicating its effectiveness for complex LULC data.In contrast,the GBT and CART methods yielded moderate F1 scores(0.77 and 0.68),indicating the presence of overclassification and class imbalance issues.The SVM and naïve Bayes methods were less accurate,ren-dering them unsuitable for LULC tasks.RF is optimal for monitoring and plan-ning land use in dynamic arid areas.Future research should explore hybrid methods and diversify training sites to improve performance.展开更多
Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead ...Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead to uncertainties or inaccuracies in assessing their impacts.Further investigation of differences between fixed and transient LULCC simulations is needed.Here,we employ the Community Earth System Model(CESM)to analyze contrasting responses of mean and extreme near-surface air temperature to historical land cover change.Our results show that forest cover in Europe generally follows a linear upward trend,while East Asia experiences deforestation processes during the historical period.It is found that temperature changes do not exhibit similar seasonal variation and have regional dependence,with Europe showing more pronounced seasonal variability.It is also demonstrated that using fixed land cover simulations exaggerates the temperature responses,leading to an overestimation of temperatures.In Europe,the overestimation of mean and extreme near-surface air temperature is approximately 0.2℃ and 0.3℃,respectively.However,the overestimation is about 0.1℃ in East Asia.Besides,we further disentangle the local and nonlocal effects in the temperature changes and show that nonlocal atmospheric feedbacks dominate the temperature responses in Europe,while local and nonlocal effects exhibit similar temperature variations in East Asia.Further efforts to explore the nonlocal effects of realistic land cover change could help enhance our understanding of climatic effects of land cover change at midlatitudes.展开更多
Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact ...Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.展开更多
The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and clim...The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and climate variability.Understanding the spatiotemporal dynamics of water yield and its driving factors is essential for sustainable water resource management in this ecologically sensitive region.This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model to quantify the spatiotemporal patterns of water yield in the LRB(dividing into six sub-basins from east to west:East Liaohe River Basin(ELRB),Taizi River Basin(TRB),Middle Liaohe River Basin(MLRB),West Liaohe River Basin(WLRB),Xinkai River Basin(XRB),and Wulijimuren River Basin(WRB))from 1993 to 2022,with a focus on the impacts of climate change and land use cover change(LUCC).Results revealed that the LRB had an average annual precipitation of 483.15 mm,with an average annual water yield of 247.54 mm,both showing significant upward trend over the 30-a period.Spatially,water yield demonstrated significant heterogeneity,with higher values in southeastern sub-basins and lower values in northwestern sub-basins.The TRB exhibited the highest water yield due to abundant precipitation and favorable topography,while the WRB recorded the lowest water yield owing to arid conditions and sparse vegetation.Precipitation played a significant role in shaping the annual fluctuations and total volume of water yield,with its variability exerting substantially greater impacts than actual evapotranspiration(AET)and LUCC.However,LUCC,particularly cultivated land expansion and grassland reduction,significantly reshaped the spatial distribution of water yield by modifying surface runoff and infiltration patterns.This study provides critical insights into the spatiotemporal dynamics of water yield in the LRB,emphasizing the synergistic effects of climate change and land use change,which are pivotal for optimizing water resource management and advancing regional ecological conservation.展开更多
Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different cro...Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.展开更多
Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the ...Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the operation of such farming from 16/17th century till 1945,many changes in land use/land cover and landscape at all occurred,which are generally evaluated positively.Turbulent events including political,economic and social changes and the displacement of the German-speaking population associated with them in the mid-20th century rapidly ended this development,causing significant landscape changes,such as the abandonment of agricultural land and succession,afforestation,expansion of the alpine tree line,reduction of diversity.The aim of our study is to evaluate changes of land cover(forests,dwarf pine,grasslands,other areas)from 1936/1946 till 2021,secondary succession and driving forces of change for selected meadow enclaves in the Krkonose Mountains and the Hruby Jeseník Mountains after the decline of mountain chalet farming since the middle of 20th century.We used remote sensing methods(aerial imagery)and field research(dendrochronology and comparative photography)to detect the land use/land cover changes in the selected study areas in the Krkono?e Mountains and the Hruby Jeseník Mountains.We documented the process of the succession,which occurred almost immediately after the end of farming,peaking about 10–20 years later,with an earlier start in the Hruby Jeseník Mountains.The succession led to the significant change of land use/land cover and these processes were similar in both mountain ranges.The largest changes were a decrease in grasslands by 62%–64%and an increase in forest area by 33%–40%for both study areas.The abandonment of land is the main consequence of a crucial political driving forces(displacement of German-speaking population)in the Krkono?e Mountains and the Hruby Jeseník Mountains.展开更多
Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality ...Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality development of ecological environment.The carbon sequestration capacity within the mountain-desert-oasis system(MDOS),a unique landscape pattern,exhibits significant gradient characteristics,and its carbon sink potential can be substantially improved through multi-scale spatial optimization.This study employed the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model to estimate carbon storage and sequestration(CSS)in the Gansu section of Heihe River Basin,China,a representative MDOS,based on land use/land cover(LULC)data from 1990 to 2020.The Patch-level Land Use Simulation(PLUS)model was coupled to simulate LULC and estimate carrying CSS under natural development(ND),ecological protection(EP),water constraint(WC),and economic development(ED)scenarios for 2035.Furthermore,the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits,exploring the guiding significance of different scenarios for pattern optimization.The results showed that CSS spatial distribution is closely correlated with LULC pattern,and CSS is expected to improve in the future.CSS showed an overall increase across subsystems during 1990–2020,but varied across LULC types.CSS of construction land in all subsystems exhibited an increasing trend,while CSS of unused land showed a decreasing trend,with specific changes of 1.68×103 and 3.43×105 t,respectively.Regional CSS dynamics were mainly driven by conversions among unused land,cultivated land,and grassland.The CSS pattern of MDOS was divided into carbon sink functional region(CSFR),low carbon conservation region(LCCR),low carbon economic region(LCER),and economic development region(EDR).Water resources coordination served as the basis of pattern optimization,while the four dimensions—ecological carbon sink,low-carbon maintenance,agricultural carbon reduction and sink enhancement,and urban carbon emission reduction—framed the optimization framework.ND,EP,WC,and ED scenarios provided guidance as the basic reference,optimal benefit,"dual carbon"baseline,and upper development limit,respectively.Additionally,the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems,demonstrating the applicability of these sub-partitions.These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.展开更多
The Kulpawn River Basin(KRB)plays a critical role in supporting rural livelihoods through agriculture,water supply,and biodiversity conservation.However,between 1995 and 2023,significant land use and land cover(LULC)c...The Kulpawn River Basin(KRB)plays a critical role in supporting rural livelihoods through agriculture,water supply,and biodiversity conservation.However,between 1995 and 2023,significant land use and land cover(LULC)changes have been observed,affecting ecosystem services(ESs).This study evaluated the ecosystem service values(ESVs)associated with LULC changes.The random forest algorithm was applied to extract LULC information from Landsat images for 1995,2005,2015,and 2023.The benefit transfer method was employed to estimate the ESVs over the study period.Questionnaires were also used to assess the views of respondents on the drivers of the ES changes in the basin.The results showed that agricultural lands expanded by 14.14%,built-up areas by 15.17%,and light savannah forest by 8.73%,while dense savannah forest and water bodies declined by 25.71%and 20.00%,respectively.The total estimated ESV was 410.09×10^(8),362.92×10^(8),335.30×10^(8),and 319.28×10^(8) USD/(hm^(2)·a)in 1995,2005,2015,and 2023,respectively,indicating that the total ESV declined from 410.09×10^(8) USD/(hm^(2)·a)in 1995 to 319.28×10^(8) USD/(hm^(2)·a)in 2023.The study concludes that the reduction in ESVs is due to the LULC changes resulting from agricultural activities,expansion of built-up areas,population sprawl,and artisanal mining activities.Hence,there is an urgent need to develop programs and strategies to mitigate and curtail the degradation of LULC and ESVs in the basin.These findings reveal a growing ecological vulnerability,threatening water security and rural livelihoods.The study offers valuable insights to guide sustainable land use planning and ecosystem conservation strategies.展开更多
Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage va...Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage variations in terrestrial ecosystems.Therefore,evaluating the impacts of LUCC on carbon storage is crucial for achieving strategic goals such as the China’s dual carbon goals(including carbon peaking and carbon neutrality).This study focuses on the Aral Irrigation Area in Xinjiang Uygur Autonomous Region,China,to assess the impacts of LUCC on regional carbon storage and their spatiotemporal dynamics.A comprehensive LUCC database from 2000 to 2020 was developed using Landsat satellite imagery and the random forest classification algorithm.The integrated valuation of ecosystem services and trade-offs(InVEST)model was applied to quantify carbon storage and analyze its response to LUCC.Additionally,future LUCC patterns for 2030 were projected under multiple development scenarios using the patch-generating land use simulation(PLUS)model.These future LUCC scenarios were integrated with the InVEST model to simulate carbon storage trends under different land management pathways.Between 2000 and 2020,the dominant land use types in the study area were cropland(area proportion of 35.52%),unused land(34.80%),and orchard land(12.19%).The conversion of unused land and orchard land significantly expanded the area of cropland,which increased by 115,742.55 hm^(2).During this period,total carbon storage and carbon density increased by 7.87×10^(6) Mg C and 20.19 Mg C/hm^(2),respectively.The primary driver of this increase was the conversion of unused land into cropland,accounting for 49.28%of the total carbon storage gain.Carbon storage was notably lower along the northeastern and southeastern edges.By 2030,the projected carbon storage is expected to increase by 0.99×10^(6),1.55×10^(6),and 1.71×10^(6) Mg C under the natural development,cropland protection,and ecological conservation scenarios,respectively.In contrast,under the urban development scenario,carbon storage is projected to decline by 0.40×10^(6) Mg C.In line with China’s dual carbon goals,the ecological conservation scenario emerges as the most effective strategy for enhancing carbon storage.Accordingly,strict enforcement of the cropland red line is recommended.This study provides a valuable scientific foundation for regional ecosystem restoration and sustainable development in arid regions.展开更多
Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this stud...Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.展开更多
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, seve...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.展开更多
The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data...The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain.The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF?2 multispectral data.The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance,and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911.Therefore,considering the LULC classification performance and data characteristics,GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring.Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.展开更多
The paramo,plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon.This research was carried out in the province of Tungurahua,specifically the Que...The paramo,plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon.This research was carried out in the province of Tungurahua,specifically the Quero district.The aim is to develop a classification of the land use land cover(LULC)in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance,for which three different approaches were applied:Pixel-Based Image Analysis(PBIA),Geographic Object-Based Image Analysis(GEOBIA),and a Deep Neural Network(DNN).Various parameters were used,such as the Normalized Difference Vegetation Index(NDVI),the Bare Soil Index(BSI),texture,altitude,and slope.Seven classes were used:paramo,pasture,crops,herbaceous vegetation,urban,shrubrainland,and forestry plantations.The data was obtained with the help of onsite technical experts,using geo-referencing and reference maps.Among the models used the highest-ranked was DNN with an overall precision of 87.43%,while for the paramo class specifically,GEOBIA reached a precision of 95%.展开更多
Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human...Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human and environmental pressures remain poorly understood.In this study,we analyzed the spatiotemporal evolution of NEP in the Horqin Sandy Land,China from 2000 to 2020,and observed the variation in NEP across different land use types.We further identified and quantified the effects of human activities,topographical features,climatic conditions,and soil properties on NEP through the application of structural equation modeling(SEM)and boosted regression trees(BRT).The results showed that the multi-year average NEP ranged from–137.79 to 461.96 g C/m^(2) in the Horqin Sandy Land,with 88.21%of the area showing a significant increasing trend.Among different land use types,forestland exhibited the highest NEP values,followed by cropland,grassland,impervious land,and unused land.The NEP in carbon sink areas was primarily regulated by potential evapotranspiration(negatively correlated)and precipitation(positively correlated).Slope was identified as the most significant positive determinant in carbon source areas.Forestland exhibited climate–topography interactions driving NEP,whereas cropland and grassland relied on temperature;unused land and impervious land were susceptible to land use/cover change and human footprint.This study has significant implications for maintaining the carbon sink function and promoting ecological engineering programs that aim to enhance the capacity of terrestrial carbon sinks in the semi-arid agro-pastoral ecotone.展开更多
Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classif...Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.展开更多
Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic...Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic aperture radar (SAR) imagery offers new opportunities for land cover classification in frequently cloud covered environments. In this study, we investigated the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying strip of Douala, Cameroon when compared with Landsat enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major LULC classes in the region included water, settlement, bare ground, dark mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other vegetation and palms. Textural variables including mean, correlation, contrast and entropy were derived from the Sentinel-1 C band. Various conventional image processing techniques and the support vector machine (SVM) algorithm were applied. Only four land cover classes (settlement, water, mangroves and other vegetation and rubber) could be calibrated and validated using SAR imagery due to speckles. The Sentinel-1 only classification yielded a lower overall classification accuracy (67.65% when compared to all Landsat bands (88.7%)). The integrated Sentinel-1 and Landsat data showed no significant differences in overall accuracy assessment (88.71% and 88.59%, respectively). The three best spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall accuracy assessment (91.96%). in the study. These results demonstrate a lower potential of Sentinel-1 for land cover classification in the Douala estuary when compared with cloud free Landsat images. However, comparable results were obtained when only broad classes were considered.展开更多
This paper carries out quantitative analysis on the land use/cover （LU/C） change of 13anjin Binhai New Area in recent 10 years through using land use transition matrix from the three-stage LU/C classification maps o...This paper carries out quantitative analysis on the land use/cover （LU/C） change of 13anjin Binhai New Area in recent 10 years through using land use transition matrix from the three-stage LU/C classification maps of 2000, 2005 and 2010 drafted by means of the National Land Classification System of China based on Landsat TM satellite remote sensing image and the Tianjin Binhai New Area 1：50 000 relief maps. On this basis, the impact of such driving factors as the economy and population on LU/C is further analyzed. The results show that the area of the building land in Binhai New Area has increased significantly over the ten years, and the greenland, wetland, and shoals of high ecological value have been dramatically transformed into the building land and unused land for the development and construction, and the change is more significant in the later five years.展开更多
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 d...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.展开更多
基金supported by the United Kingdom(UK)Darwin Initiative(28-003).
文摘The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.
文摘Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.Machine learn-ing methods for land use and land cover(LULC)classification are vital for monitoring environmental changes.Remote sensing advancements increase the potential for classifying land cover,which requires assessing algorithm ac-curacy and efficiency for fragile environments.This research identifies the best algorithms for LULC monitoring and developing adaptive methods for sensi-tive ecosystems.Landsat-9 imagery from January to April 2023 facilitated land use class identification.Preprocessing in the Google Earth Engine applied spec-tral indices such as the NDVI,NDWI,BSI,and NDBI.Supervised classification uses random forest(RF),support vector machine(SVM),classification and re-gression trees(CARTs),gradient boosting trees(GBTs),and naïve Bayes.An accuracy assessment was used to determine the optimal classifiers for future land use analyses.The evaluation revealed that the RF model achieved 84.4%accuracy with a 0.85 weighted F1 score,indicating its effectiveness for complex LULC data.In contrast,the GBT and CART methods yielded moderate F1 scores(0.77 and 0.68),indicating the presence of overclassification and class imbalance issues.The SVM and naïve Bayes methods were less accurate,ren-dering them unsuitable for LULC tasks.RF is optimal for monitoring and plan-ning land use in dynamic arid areas.Future research should explore hybrid methods and diversify training sites to improve performance.
基金supported by the National Key R&D Program of China(Grant No.2022YFF0801601).
文摘Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead to uncertainties or inaccuracies in assessing their impacts.Further investigation of differences between fixed and transient LULCC simulations is needed.Here,we employ the Community Earth System Model(CESM)to analyze contrasting responses of mean and extreme near-surface air temperature to historical land cover change.Our results show that forest cover in Europe generally follows a linear upward trend,while East Asia experiences deforestation processes during the historical period.It is found that temperature changes do not exhibit similar seasonal variation and have regional dependence,with Europe showing more pronounced seasonal variability.It is also demonstrated that using fixed land cover simulations exaggerates the temperature responses,leading to an overestimation of temperatures.In Europe,the overestimation of mean and extreme near-surface air temperature is approximately 0.2℃ and 0.3℃,respectively.However,the overestimation is about 0.1℃ in East Asia.Besides,we further disentangle the local and nonlocal effects in the temperature changes and show that nonlocal atmospheric feedbacks dominate the temperature responses in Europe,while local and nonlocal effects exhibit similar temperature variations in East Asia.Further efforts to explore the nonlocal effects of realistic land cover change could help enhance our understanding of climatic effects of land cover change at midlatitudes.
文摘Human well-being and livelihoods depend on natural ecosystem services(ESs).Following the increment of population,ESs have been deteriorated over time.Ultimately,land use/land cover(LULC)changes have a profound impact on the change of ecosystem.The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values(ESVs)in the upper Gilgel Abbay watershed,Ethiopia.Changes in LULC types were studied using three Landsat images representing 1986,2003,and 2021.The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System(ERDAS)Imagine 2014.We classified ESs in this study into four categories(including provisioning,regulating,supporting,and cultural services)based on global ES classification scheme.The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs.Five LULC types were identified in this study,including cultivated land,forest,shrubland,grassland,and water body.The result revealed that the area of cultivated land accounted for 64.50%,71.50%,and 61.50%of the total area in 1986,2003,and 2021,respectively.The percentage of the total area covered by forest was 9.50%,5.90%,and 14.80%in 1986,2003,and 2021,respectively.Result revealed that the total ESV decreased from 7.42×10^(7) to 6.44×10^(7) USD between 1986 and 2003.This is due to the expansion of cultivated land at the expense of forest and shrubland.However,the total ESV increased from 6.44×10^(7) to 7.76×10^(7) USD during 2003-2021,because of the increment of forest and shrubland.The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003.Nevertheless,the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021.Therefore,the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.
基金funded by the Liaoning Provincial Social Science Planning Fund(L22AYJ010).
文摘The Liaohe River Basin(LRB)in Northeast China,a critical agricultural and industrial zone,has faced escalating water resource pressures in recent decades due to rapid urbanization,intensified land use changes,and climate variability.Understanding the spatiotemporal dynamics of water yield and its driving factors is essential for sustainable water resource management in this ecologically sensitive region.This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model to quantify the spatiotemporal patterns of water yield in the LRB(dividing into six sub-basins from east to west:East Liaohe River Basin(ELRB),Taizi River Basin(TRB),Middle Liaohe River Basin(MLRB),West Liaohe River Basin(WLRB),Xinkai River Basin(XRB),and Wulijimuren River Basin(WRB))from 1993 to 2022,with a focus on the impacts of climate change and land use cover change(LUCC).Results revealed that the LRB had an average annual precipitation of 483.15 mm,with an average annual water yield of 247.54 mm,both showing significant upward trend over the 30-a period.Spatially,water yield demonstrated significant heterogeneity,with higher values in southeastern sub-basins and lower values in northwestern sub-basins.The TRB exhibited the highest water yield due to abundant precipitation and favorable topography,while the WRB recorded the lowest water yield owing to arid conditions and sparse vegetation.Precipitation played a significant role in shaping the annual fluctuations and total volume of water yield,with its variability exerting substantially greater impacts than actual evapotranspiration(AET)and LUCC.However,LUCC,particularly cultivated land expansion and grassland reduction,significantly reshaped the spatial distribution of water yield by modifying surface runoff and infiltration patterns.This study provides critical insights into the spatiotemporal dynamics of water yield in the LRB,emphasizing the synergistic effects of climate change and land use change,which are pivotal for optimizing water resource management and advancing regional ecological conservation.
基金supported by the Deanship of Research and Graduate Studies at the King Khalid University(RGP2/287/46)the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R733)+1 种基金the Princess Nourah bint Abdulrahman University Research Supporting Project(RSPD2025R787)the King Saud University,Saudi Arabia.
文摘Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.
基金funded by the European Commission,CINEA Horizon Europe project no.101081307“Towards Sustainable Land-Use in the Context of Climate Change and Biodiversity in Europe(Europe-LAND)”。
文摘Chalet farming,as a specific type of agricultural landscape management,has been established in many European mountain ranges,including the Krkono?e Mountains and the Hruby Jeseník Mountains in Czechia.During the operation of such farming from 16/17th century till 1945,many changes in land use/land cover and landscape at all occurred,which are generally evaluated positively.Turbulent events including political,economic and social changes and the displacement of the German-speaking population associated with them in the mid-20th century rapidly ended this development,causing significant landscape changes,such as the abandonment of agricultural land and succession,afforestation,expansion of the alpine tree line,reduction of diversity.The aim of our study is to evaluate changes of land cover(forests,dwarf pine,grasslands,other areas)from 1936/1946 till 2021,secondary succession and driving forces of change for selected meadow enclaves in the Krkonose Mountains and the Hruby Jeseník Mountains after the decline of mountain chalet farming since the middle of 20th century.We used remote sensing methods(aerial imagery)and field research(dendrochronology and comparative photography)to detect the land use/land cover changes in the selected study areas in the Krkono?e Mountains and the Hruby Jeseník Mountains.We documented the process of the succession,which occurred almost immediately after the end of farming,peaking about 10–20 years later,with an earlier start in the Hruby Jeseník Mountains.The succession led to the significant change of land use/land cover and these processes were similar in both mountain ranges.The largest changes were a decrease in grasslands by 62%–64%and an increase in forest area by 33%–40%for both study areas.The abandonment of land is the main consequence of a crucial political driving forces(displacement of German-speaking population)in the Krkono?e Mountains and the Hruby Jeseník Mountains.
基金funded by the Gansu Provincial Department of Education's University Teacher Innovation Fund Project(2025A-001)the Gansu Provincial Philosophy and Social Science Planning Project(2024YB088).
文摘Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development,accelerating the achievement of the"dual carbon"goals,and promoting the high-quality development of ecological environment.The carbon sequestration capacity within the mountain-desert-oasis system(MDOS),a unique landscape pattern,exhibits significant gradient characteristics,and its carbon sink potential can be substantially improved through multi-scale spatial optimization.This study employed the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model to estimate carbon storage and sequestration(CSS)in the Gansu section of Heihe River Basin,China,a representative MDOS,based on land use/land cover(LULC)data from 1990 to 2020.The Patch-level Land Use Simulation(PLUS)model was coupled to simulate LULC and estimate carrying CSS under natural development(ND),ecological protection(EP),water constraint(WC),and economic development(ED)scenarios for 2035.Furthermore,the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits,exploring the guiding significance of different scenarios for pattern optimization.The results showed that CSS spatial distribution is closely correlated with LULC pattern,and CSS is expected to improve in the future.CSS showed an overall increase across subsystems during 1990–2020,but varied across LULC types.CSS of construction land in all subsystems exhibited an increasing trend,while CSS of unused land showed a decreasing trend,with specific changes of 1.68×103 and 3.43×105 t,respectively.Regional CSS dynamics were mainly driven by conversions among unused land,cultivated land,and grassland.The CSS pattern of MDOS was divided into carbon sink functional region(CSFR),low carbon conservation region(LCCR),low carbon economic region(LCER),and economic development region(EDR).Water resources coordination served as the basis of pattern optimization,while the four dimensions—ecological carbon sink,low-carbon maintenance,agricultural carbon reduction and sink enhancement,and urban carbon emission reduction—framed the optimization framework.ND,EP,WC,and ED scenarios provided guidance as the basic reference,optimal benefit,"dual carbon"baseline,and upper development limit,respectively.Additionally,the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems,demonstrating the applicability of these sub-partitions.These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.
基金financial assistance from the European Union(Contract number:AFS2023/444-387)。
文摘The Kulpawn River Basin(KRB)plays a critical role in supporting rural livelihoods through agriculture,water supply,and biodiversity conservation.However,between 1995 and 2023,significant land use and land cover(LULC)changes have been observed,affecting ecosystem services(ESs).This study evaluated the ecosystem service values(ESVs)associated with LULC changes.The random forest algorithm was applied to extract LULC information from Landsat images for 1995,2005,2015,and 2023.The benefit transfer method was employed to estimate the ESVs over the study period.Questionnaires were also used to assess the views of respondents on the drivers of the ES changes in the basin.The results showed that agricultural lands expanded by 14.14%,built-up areas by 15.17%,and light savannah forest by 8.73%,while dense savannah forest and water bodies declined by 25.71%and 20.00%,respectively.The total estimated ESV was 410.09×10^(8),362.92×10^(8),335.30×10^(8),and 319.28×10^(8) USD/(hm^(2)·a)in 1995,2005,2015,and 2023,respectively,indicating that the total ESV declined from 410.09×10^(8) USD/(hm^(2)·a)in 1995 to 319.28×10^(8) USD/(hm^(2)·a)in 2023.The study concludes that the reduction in ESVs is due to the LULC changes resulting from agricultural activities,expansion of built-up areas,population sprawl,and artisanal mining activities.Hence,there is an urgent need to develop programs and strategies to mitigate and curtail the degradation of LULC and ESVs in the basin.These findings reveal a growing ecological vulnerability,threatening water security and rural livelihoods.The study offers valuable insights to guide sustainable land use planning and ecosystem conservation strategies.
基金supported by the National Key R&D Program of China(2022YFD1900503).
文摘Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance.Land use and cover change(LUCC)is one of the primary drivers influencing carbon storage variations in terrestrial ecosystems.Therefore,evaluating the impacts of LUCC on carbon storage is crucial for achieving strategic goals such as the China’s dual carbon goals(including carbon peaking and carbon neutrality).This study focuses on the Aral Irrigation Area in Xinjiang Uygur Autonomous Region,China,to assess the impacts of LUCC on regional carbon storage and their spatiotemporal dynamics.A comprehensive LUCC database from 2000 to 2020 was developed using Landsat satellite imagery and the random forest classification algorithm.The integrated valuation of ecosystem services and trade-offs(InVEST)model was applied to quantify carbon storage and analyze its response to LUCC.Additionally,future LUCC patterns for 2030 were projected under multiple development scenarios using the patch-generating land use simulation(PLUS)model.These future LUCC scenarios were integrated with the InVEST model to simulate carbon storage trends under different land management pathways.Between 2000 and 2020,the dominant land use types in the study area were cropland(area proportion of 35.52%),unused land(34.80%),and orchard land(12.19%).The conversion of unused land and orchard land significantly expanded the area of cropland,which increased by 115,742.55 hm^(2).During this period,total carbon storage and carbon density increased by 7.87×10^(6) Mg C and 20.19 Mg C/hm^(2),respectively.The primary driver of this increase was the conversion of unused land into cropland,accounting for 49.28%of the total carbon storage gain.Carbon storage was notably lower along the northeastern and southeastern edges.By 2030,the projected carbon storage is expected to increase by 0.99×10^(6),1.55×10^(6),and 1.71×10^(6) Mg C under the natural development,cropland protection,and ecological conservation scenarios,respectively.In contrast,under the urban development scenario,carbon storage is projected to decline by 0.40×10^(6) Mg C.In line with China’s dual carbon goals,the ecological conservation scenario emerges as the most effective strategy for enhancing carbon storage.Accordingly,strict enforcement of the cropland red line is recommended.This study provides a valuable scientific foundation for regional ecosystem restoration and sustainable development in arid regions.
文摘Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.
文摘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.
基金the National Natural Science Foundation of China (Grant No.41571422)the National Key Research and Development Program of China (No.2016YFA0600103).
文摘The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain.The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF?2 multispectral data.The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance,and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911.Therefore,considering the LULC classification performance and data characteristics,GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring.Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.
基金funded by the EU ERDF and the Spanish Ministry of Economy and Competitiveness(MINECO)under AEI Project TIN2017-83964-Rthe Directorate-General for Research and Knowledge Transfer-Junta de Andalucia under Project UrbanITA P2000809.
文摘The paramo,plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon.This research was carried out in the province of Tungurahua,specifically the Quero district.The aim is to develop a classification of the land use land cover(LULC)in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance,for which three different approaches were applied:Pixel-Based Image Analysis(PBIA),Geographic Object-Based Image Analysis(GEOBIA),and a Deep Neural Network(DNN).Various parameters were used,such as the Normalized Difference Vegetation Index(NDVI),the Bare Soil Index(BSI),texture,altitude,and slope.Seven classes were used:paramo,pasture,crops,herbaceous vegetation,urban,shrubrainland,and forestry plantations.The data was obtained with the help of onsite technical experts,using geo-referencing and reference maps.Among the models used the highest-ranked was DNN with an overall precision of 87.43%,while for the paramo class specifically,GEOBIA reached a precision of 95%.
基金funded by the National Major Science and Technology Program for Water Pollution Control and Treatment(2017ZX07101-002)the Discipline Construction Program of ZHANG Huayong,Distinguished Professor of School of Life Sciences,Shandong University(61200082363001).
文摘Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human and environmental pressures remain poorly understood.In this study,we analyzed the spatiotemporal evolution of NEP in the Horqin Sandy Land,China from 2000 to 2020,and observed the variation in NEP across different land use types.We further identified and quantified the effects of human activities,topographical features,climatic conditions,and soil properties on NEP through the application of structural equation modeling(SEM)and boosted regression trees(BRT).The results showed that the multi-year average NEP ranged from–137.79 to 461.96 g C/m^(2) in the Horqin Sandy Land,with 88.21%of the area showing a significant increasing trend.Among different land use types,forestland exhibited the highest NEP values,followed by cropland,grassland,impervious land,and unused land.The NEP in carbon sink areas was primarily regulated by potential evapotranspiration(negatively correlated)and precipitation(positively correlated).Slope was identified as the most significant positive determinant in carbon source areas.Forestland exhibited climate–topography interactions driving NEP,whereas cropland and grassland relied on temperature;unused land and impervious land were susceptible to land use/cover change and human footprint.This study has significant implications for maintaining the carbon sink function and promoting ecological engineering programs that aim to enhance the capacity of terrestrial carbon sinks in the semi-arid agro-pastoral ecotone.
基金supported by the National Natural Science Foundation of China[grant number 41930104]by the Research Grants Council of Hong Kong[grant number PolyU 152219/18E].
文摘Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.
文摘Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic aperture radar (SAR) imagery offers new opportunities for land cover classification in frequently cloud covered environments. In this study, we investigated the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying strip of Douala, Cameroon when compared with Landsat enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major LULC classes in the region included water, settlement, bare ground, dark mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other vegetation and palms. Textural variables including mean, correlation, contrast and entropy were derived from the Sentinel-1 C band. Various conventional image processing techniques and the support vector machine (SVM) algorithm were applied. Only four land cover classes (settlement, water, mangroves and other vegetation and rubber) could be calibrated and validated using SAR imagery due to speckles. The Sentinel-1 only classification yielded a lower overall classification accuracy (67.65% when compared to all Landsat bands (88.7%)). The integrated Sentinel-1 and Landsat data showed no significant differences in overall accuracy assessment (88.71% and 88.59%, respectively). The three best spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall accuracy assessment (91.96%). in the study. These results demonstrate a lower potential of Sentinel-1 for land cover classification in the Douala estuary when compared with cloud free Landsat images. However, comparable results were obtained when only broad classes were considered.
文摘This paper carries out quantitative analysis on the land use/cover （LU/C） change of 13anjin Binhai New Area in recent 10 years through using land use transition matrix from the three-stage LU/C classification maps of 2000, 2005 and 2010 drafted by means of the National Land Classification System of China based on Landsat TM satellite remote sensing image and the Tianjin Binhai New Area 1：50 000 relief maps. On this basis, the impact of such driving factors as the economy and population on LU/C is further analyzed. The results show that the area of the building land in Binhai New Area has increased significantly over the ten years, and the greenland, wetland, and shoals of high ecological value have been dramatically transformed into the building land and unused land for the development and construction, and the change is more significant in the later five years.
基金Supported by the National Key Technology R&D Program(2012BAD15B03)the Sino-German Cooperation Program for Agricultural Technology(16/10-11 CHN37)~~
文摘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.
