Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral te...Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover(LULC)mapping.A basic Raman spectroscopy model demonstrates that Savitzky-Golay(SG)filtering,Wavelet denoising,and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULCclassification.Savitzky-Golay filtering yielded the most efficient results,increasing classification accuracy from 0.71(noisy)to 1.00(denoised),resulting in perfect classification with zero errors and enhancing the Precision-Recall curve,as Area Under the Precision-Recall Curve(AUC-PR)transformed from 0.84 to 1.00.The study examined wavelet denoising in conjunction with a 1D Convolutional Autoencoder,assessing the noise reduction capability through visual evaluation.Based on Raman-based spectral analysis,a traditional method complemented with machine learning denoising provides promising fields for feature identification in remote sensing images,thereby improving the quality of LULC-related mapping outcomes.展开更多
With large scale topographic map charted in accordance with Topographic Map Symbols of 1:500 1:1 000 1:2 000(GB/T 20257.1-2007) as the base map of land survey,the land use status information was collected from the map...With large scale topographic map charted in accordance with Topographic Map Symbols of 1:500 1:1 000 1:2 000(GB/T 20257.1-2007) as the base map of land survey,the land use status information was collected from the map based on the standard in Present Status Classification of Land Utilization(GB/T 21010-2007).The study discussed in details the information of some land types including water system,residential sites,facilities,transportation,pipeline,vegetation,soils and so on,and pointed out problems on extracting land use status information from large scale topographic map.In order to share resources and save social costs,it suggested unifying the standard to classify land types and define all kinds of land types by quantitative values.展开更多
With the rapid development of economic activities in cities and construction industry in our country, the number of construction projects has been increasing. At the same time, this has promoted the development of lan...With the rapid development of economic activities in cities and construction industry in our country, the number of construction projects has been increasing. At the same time, this has promoted the development of land surveying and mapping work in our country to a certain extent. Land surveying and mapping is a complicated and arduous systematic work in our country, which mainly includes the surveying and mapping of planning and construction land, daily land surveying and mapping and various cadastral map data surveying and mapping systems. As the basic work of construction engineering, the quality of land surveying and mapping directly affects the overall progress of construction. Based on this, this paper analyzes the factors affecting the quality of land surveying and mapping and the control measures.展开更多
This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indica...This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data.These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing.Furthermore,in order to improve the quality of the land cover mapping,this research employed the spatial and temporal Markov random field classification approach.Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification.This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.展开更多
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 th...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.展开更多
Data fusion has shown potential to improve the accuracy of land cover mapping,and selection of the optimal fusion technique remains a challenge.This study investigated the performance of fusing Sentinel-1(S-1)and Sent...Data fusion has shown potential to improve the accuracy of land cover mapping,and selection of the optimal fusion technique remains a challenge.This study investigated the performance of fusing Sentinel-1(S-1)and Sentinel-2(S-2)data,using layer-stacking method at the pixel level and Dempster-Shafer(D-S)theory-based approach at the decision level,for mapping six land cover classes in Thu Dau Mot City,Vietnam.At the pixel level,S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets(i.e.fused datasets).The datasets were categorized into two groups.One group included the datasets containing only spectral and backscattering bands,and the other group included the datasets consisting of these bands and their extracted features.The random forest(RF)classifier was then applied to the datasets within each group.At the decision level,the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory.Finally,the accuracy of the mapping results at both levels within each group was compared.The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group.The highest overall accuracy(OA)and Kappa coefficient of the map using D-S theory were 92.67%and 0.91,respectively.The decision-level fusion helped increase the OA of the map by 0.75%to 2.07%compared to that of corresponding S-2 products in the groups.Meanwhile,the data fusion at the pixel level delivered the mapping results,which yielded an OA of 4.88%to 6.58%lower than that of corresponding S-2 products in the groups.展开更多
Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for pr...Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for proper urban planning and management. Thepossible method described in the present paper to obtain urban land use types is based on theprinciple that land use can be derived from the land cover existing in a neighborhood. Here, movingwindow is used to represent the spatial pattern of land cover within a neighborhood and seven windowsizes (61mx61m, 68mx68m, 75mx75m, 87mx87m, 99mx99m, 110mx110m and 121mxl21m) are applied todetermining the most proper window size. Then, the unsupervised method of ISODATA is employed toclassify the layered land cover density maps obtained by the moving window. The results of accuracyevaluation show that the window size of 99mx99m is proper to infer urban land use categories and theproposed method has produced a land use map with a total accuracy of 85%.展开更多
We present here a new approach to the development of a global land cover map. We combined three existing global land cover maps (MOD12, GLC2000, and UMD) based on the principle that the majority view prevails and vali...We present here a new approach to the development of a global land cover map. We combined three existing global land cover maps (MOD12, GLC2000, and UMD) based on the principle that the majority view prevails and validated the resulting map by using information collected as part of the Degree Confluence Project (DCP). We used field survey information gathered by DCP volunteers from 4211 worldwide locations to validate the new land cover map, as well as the three existing land cover maps that were combined to create it. Agreement between the DCP-derived information and the land cover maps was 61.3% for our new land cover map, 60.3% for MOD12, 58.9% for GLC2000, and 55.2% for UMD. Although some of the improvements we achieved were not statistically significant, this project has shown that an improved land cover map can be developed and well-validated globally using our method.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
Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and...Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.展开更多
Preventive management of groundwater resources and their protection against pollution is one of the major challenges of our society. Groundwater systems are related with the surficial processes like territorial admini...Preventive management of groundwater resources and their protection against pollution is one of the major challenges of our society. Groundwater systems are related with the surficial processes like territorial administration that is one of the most important tasks into the human development, because it involves serious problems to define the spatial medium, the industrial site-selection and the land-use allocation. Land-use and anthropogenic distribution could be the origin of the emission of pollutants that constitutes a serious health risk in urban areas. Nitrate was used as a pollution indicator in the Pablillo River Basin (PRB), to know the evolution of groundwater quality between 1981 and 2009 using GIS platform linked to vulnerability maps of DRASTIC (Depth to the water table;net groundwater Recharge;Aquifer type;Soil type;Topography;Impact of the vadose zone and hydraulic Conductivity of the saturated zone). The study area is centered on the Linares city;changes in aquifer vulnerability were assessed over time on two stages (2007 and 2001). In both cases, depth from surface to groundwater plays an important role by being the most dynamic variable over time. This study shows that the depth of water table is the key factor in the evaluation of groundwater vulnerability. The significance of land-use impact in contamination process called Index of Pollution Risk (IPR) and nitrate distribution process in the aquifer system was used as anthropogenic indicator together with the IPR in order to associate the land-use, the aquifer-vulnerability and human-activities. The final map of IPR allows determining possible polluted zones verified by high nitrate contents over the aquifer system. Land-use proved to be an important parameter necessary to correct the vulnerability maps using the DRASTIC method. This assessment is valid for situations where a specific time is defined because six of seven parameters change their properties in a very long term. The IPR-map could be an important key tool to prevent complex scenarios of groundwater contamination and to improve the aquifer management for decision makers, governments and private companies.展开更多
The symbolization of land polygon is an important part of cartography.In the mapping of traditional present land-use maps,the symbol of land polygons was usually filled by the method of filling or plotting,but these m...The symbolization of land polygon is an important part of cartography.In the mapping of traditional present land-use maps,the symbol of land polygons was usually filled by the method of filling or plotting,but these methods can't solve the spatial conflicts of the symbol.According to the principle of cartography,the rule of how to symbolize the land polygon was summarized,and a new method that can generate and deploy the land symbols was presented.By making use of C#programming language and Arc Engine developing components,the algorithm can generate land symbols presenting triangle and adjust the coordinate of the symbol.Through mapping the present land-use map of Honghe county,this algorithm can reduce 88.84%of the spatial conflicts error rate compared with the traditional methods.It improves the accuracy and efficiency of the map symbolic.展开更多
The projected 300% growth rate in the population of Enugu area and its environs by the year 2020 and the expected increase in waste generation necessitated the need to map out areas for waste disposal for future utili...The projected 300% growth rate in the population of Enugu area and its environs by the year 2020 and the expected increase in waste generation necessitated the need to map out areas for waste disposal for future utilization and as a protective strategy for the environment in Enugu area. Land capability index mapping using Geographic Information System (GIS) is one of the appropriate tools required for solving this problem. A total of 12 landuse determinants were selected as thematic data layers, and as basic factors influencing the choice of waste disposal landuse option in the area. The themes (thematic maps) generated from field/laboratory measurements and from literature, include slope, water table, surface and subsurface water conditions, elevation, geology, soil, drainage and geo-structural stability (fault, erosion, landslide and flooding) maps. The maps were scanned, digitized, georeferenced, and polygonized using autocard drawing capabilities to convert them into vector format and later exported to arc view software for analysis. The final processing using overlay model builder yields layers that display areas of preferred waste disposal sites in a map form, which generally shows areas of varying suitability (suitable, moderately (low) suitable and unsuitable). The waste disposal map of Enugu area shows that blocks1 (Obeagu area) and 3 (Ebe/Nsude areas) represent suitable and unsuitable areas, respectively, while block 2 (Ngwo area) has low suitability for waste disposal.展开更多
In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary ...In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.展开更多
Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be...Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following sub-sections.展开更多
The original surveying tools and methods are backward and low efficient and they will also generate lots of errors in the National secondary land surveying. MapSUV rural land surveying palm mapping system (MapSUV palm...The original surveying tools and methods are backward and low efficient and they will also generate lots of errors in the National secondary land surveying. MapSUV rural land surveying palm mapping system (MapSUV palm mapping system) based on 3S techniques, combines MapGIS rural land surveying database management system. It supports the spatial location information collection and attributes data entry. By combining with GPS receiver, it ensures high accuracy in small volume, which greatly facilitates land surveying. This paper main researched system structure, function module design and key techniques. It introduced the practical process of map spot attribute checking and map spot boundary. Then it gave the application assessment. The results shows that this system greatly improves the work efficiency of outdoor surveying and shorten the time of land surveying, database build and updating.展开更多
文摘Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover(LULC)mapping.A basic Raman spectroscopy model demonstrates that Savitzky-Golay(SG)filtering,Wavelet denoising,and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULCclassification.Savitzky-Golay filtering yielded the most efficient results,increasing classification accuracy from 0.71(noisy)to 1.00(denoised),resulting in perfect classification with zero errors and enhancing the Precision-Recall curve,as Area Under the Precision-Recall Curve(AUC-PR)transformed from 0.84 to 1.00.The study examined wavelet denoising in conjunction with a 1D Convolutional Autoencoder,assessing the noise reduction capability through visual evaluation.Based on Raman-based spectral analysis,a traditional method complemented with machine learning denoising provides promising fields for feature identification in remote sensing images,thereby improving the quality of LULC-related mapping outcomes.
基金Supported by Programs of Scientific and Technological Foundation of Nanjing Forestry University (X09-050-2)~~
文摘With large scale topographic map charted in accordance with Topographic Map Symbols of 1:500 1:1 000 1:2 000(GB/T 20257.1-2007) as the base map of land survey,the land use status information was collected from the map based on the standard in Present Status Classification of Land Utilization(GB/T 21010-2007).The study discussed in details the information of some land types including water system,residential sites,facilities,transportation,pipeline,vegetation,soils and so on,and pointed out problems on extracting land use status information from large scale topographic map.In order to share resources and save social costs,it suggested unifying the standard to classify land types and define all kinds of land types by quantitative values.
文摘With the rapid development of economic activities in cities and construction industry in our country, the number of construction projects has been increasing. At the same time, this has promoted the development of land surveying and mapping work in our country to a certain extent. Land surveying and mapping is a complicated and arduous systematic work in our country, which mainly includes the surveying and mapping of planning and construction land, daily land surveying and mapping and various cadastral map data surveying and mapping systems. As the basic work of construction engineering, the quality of land surveying and mapping directly affects the overall progress of construction. Based on this, this paper analyzes the factors affecting the quality of land surveying and mapping and the control measures.
基金supported in part by the National High-Tech R&D Program(863 program)under grant number 2009AA122004the National Natural Science Foundation of China under grant number 60171009the Hong Kong Research Grant Council under grant number CUHK 444612.
文摘This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data.These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing.Furthermore,in order to improve the quality of the land cover mapping,this research employed the spatial and temporal Markov random field classification approach.Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification.This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.
基金National Natural Science Foundation of China(Nos.42371406,42071441,42222106,61976234).
文摘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.
基金the Hungarian Scientific Research Fund in support of the ongoing research,“Time series analysis of land cover dynamics using medium-and high-resolution satellite images”[grant number NKFIH 124648K],at the Department of Physical Geography and Geoinformatics(the former name of the Department of Geoinformatics,Physical and Environmental Geography),University of Szeged,Szeged,Hungary.
文摘Data fusion has shown potential to improve the accuracy of land cover mapping,and selection of the optimal fusion technique remains a challenge.This study investigated the performance of fusing Sentinel-1(S-1)and Sentinel-2(S-2)data,using layer-stacking method at the pixel level and Dempster-Shafer(D-S)theory-based approach at the decision level,for mapping six land cover classes in Thu Dau Mot City,Vietnam.At the pixel level,S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets(i.e.fused datasets).The datasets were categorized into two groups.One group included the datasets containing only spectral and backscattering bands,and the other group included the datasets consisting of these bands and their extracted features.The random forest(RF)classifier was then applied to the datasets within each group.At the decision level,the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory.Finally,the accuracy of the mapping results at both levels within each group was compared.The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group.The highest overall accuracy(OA)and Kappa coefficient of the map using D-S theory were 92.67%and 0.91,respectively.The decision-level fusion helped increase the OA of the map by 0.75%to 2.07%compared to that of corresponding S-2 products in the groups.Meanwhile,the data fusion at the pixel level delivered the mapping results,which yielded an OA of 4.88%to 6.58%lower than that of corresponding S-2 products in the groups.
基金Under the auspices of Jiangsu Provincial Natural ScienceFoundation(No .BK2002420 )
文摘Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for proper urban planning and management. Thepossible method described in the present paper to obtain urban land use types is based on theprinciple that land use can be derived from the land cover existing in a neighborhood. Here, movingwindow is used to represent the spatial pattern of land cover within a neighborhood and seven windowsizes (61mx61m, 68mx68m, 75mx75m, 87mx87m, 99mx99m, 110mx110m and 121mxl21m) are applied todetermining the most proper window size. Then, the unsupervised method of ISODATA is employed toclassify the layered land cover density maps obtained by the moving window. The results of accuracyevaluation show that the window size of 99mx99m is proper to infer urban land use categories and theproposed method has produced a land use map with a total accuracy of 85%.
文摘We present here a new approach to the development of a global land cover map. We combined three existing global land cover maps (MOD12, GLC2000, and UMD) based on the principle that the majority view prevails and validated the resulting map by using information collected as part of the Degree Confluence Project (DCP). We used field survey information gathered by DCP volunteers from 4211 worldwide locations to validate the new land cover map, as well as the three existing land cover maps that were combined to create it. Agreement between the DCP-derived information and the land cover maps was 61.3% for our new land cover map, 60.3% for MOD12, 58.9% for GLC2000, and 55.2% for UMD. Although some of the improvements we achieved were not statistically significant, this project has shown that an improved land cover map can be developed and well-validated globally using our method.
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.
基金Special thanks are due to the Water Resources Management Authority (WRMA) and Ministry of Livestock and Fisheries Development in Kenya, the International Institute for Geo-information Science and Earth Observation (ITC) in Netherlands and European Union for logistical and financial support.
文摘Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.
文摘Preventive management of groundwater resources and their protection against pollution is one of the major challenges of our society. Groundwater systems are related with the surficial processes like territorial administration that is one of the most important tasks into the human development, because it involves serious problems to define the spatial medium, the industrial site-selection and the land-use allocation. Land-use and anthropogenic distribution could be the origin of the emission of pollutants that constitutes a serious health risk in urban areas. Nitrate was used as a pollution indicator in the Pablillo River Basin (PRB), to know the evolution of groundwater quality between 1981 and 2009 using GIS platform linked to vulnerability maps of DRASTIC (Depth to the water table;net groundwater Recharge;Aquifer type;Soil type;Topography;Impact of the vadose zone and hydraulic Conductivity of the saturated zone). The study area is centered on the Linares city;changes in aquifer vulnerability were assessed over time on two stages (2007 and 2001). In both cases, depth from surface to groundwater plays an important role by being the most dynamic variable over time. This study shows that the depth of water table is the key factor in the evaluation of groundwater vulnerability. The significance of land-use impact in contamination process called Index of Pollution Risk (IPR) and nitrate distribution process in the aquifer system was used as anthropogenic indicator together with the IPR in order to associate the land-use, the aquifer-vulnerability and human-activities. The final map of IPR allows determining possible polluted zones verified by high nitrate contents over the aquifer system. Land-use proved to be an important parameter necessary to correct the vulnerability maps using the DRASTIC method. This assessment is valid for situations where a specific time is defined because six of seven parameters change their properties in a very long term. The IPR-map could be an important key tool to prevent complex scenarios of groundwater contamination and to improve the aquifer management for decision makers, governments and private companies.
基金Project(41061043)support by the National Natural Science Foundation of China
文摘The symbolization of land polygon is an important part of cartography.In the mapping of traditional present land-use maps,the symbol of land polygons was usually filled by the method of filling or plotting,but these methods can't solve the spatial conflicts of the symbol.According to the principle of cartography,the rule of how to symbolize the land polygon was summarized,and a new method that can generate and deploy the land symbols was presented.By making use of C#programming language and Arc Engine developing components,the algorithm can generate land symbols presenting triangle and adjust the coordinate of the symbol.Through mapping the present land-use map of Honghe county,this algorithm can reduce 88.84%of the spatial conflicts error rate compared with the traditional methods.It improves the accuracy and efficiency of the map symbolic.
文摘The projected 300% growth rate in the population of Enugu area and its environs by the year 2020 and the expected increase in waste generation necessitated the need to map out areas for waste disposal for future utilization and as a protective strategy for the environment in Enugu area. Land capability index mapping using Geographic Information System (GIS) is one of the appropriate tools required for solving this problem. A total of 12 landuse determinants were selected as thematic data layers, and as basic factors influencing the choice of waste disposal landuse option in the area. The themes (thematic maps) generated from field/laboratory measurements and from literature, include slope, water table, surface and subsurface water conditions, elevation, geology, soil, drainage and geo-structural stability (fault, erosion, landslide and flooding) maps. The maps were scanned, digitized, georeferenced, and polygonized using autocard drawing capabilities to convert them into vector format and later exported to arc view software for analysis. The final processing using overlay model builder yields layers that display areas of preferred waste disposal sites in a map form, which generally shows areas of varying suitability (suitable, moderately (low) suitable and unsuitable). The waste disposal map of Enugu area shows that blocks1 (Obeagu area) and 3 (Ebe/Nsude areas) represent suitable and unsuitable areas, respectively, while block 2 (Ngwo area) has low suitability for waste disposal.
文摘In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.
文摘Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following sub-sections.
文摘The original surveying tools and methods are backward and low efficient and they will also generate lots of errors in the National secondary land surveying. MapSUV rural land surveying palm mapping system (MapSUV palm mapping system) based on 3S techniques, combines MapGIS rural land surveying database management system. It supports the spatial location information collection and attributes data entry. By combining with GPS receiver, it ensures high accuracy in small volume, which greatly facilitates land surveying. This paper main researched system structure, function module design and key techniques. It introduced the practical process of map spot attribute checking and map spot boundary. Then it gave the application assessment. The results shows that this system greatly improves the work efficiency of outdoor surveying and shorten the time of land surveying, database build and updating.
