Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was eff...Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.展开更多
Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application ...Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.展开更多
The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjia...The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.展开更多
Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been stud...Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been studied since the early 1980 s, mature methods and efficient supporting tools are still lacking. This paper presents a geospatial knowledge-based verification and improvement approach for global land cover(GLC) mapping at 30-m resolution. A set of verification rules is derived from three types of land cover and its change knowledge(natural, cultural and temporal constraints). A group of web-based supporting tools is developed to facilitate the integration of and access to large amounts of ancillary data and to support online data manipulation and analysis as well as collaborative verification workflows. With this approach, two 30-m GLC datasets(Globe Land-2000 and Globe Land-2010) were verified and modified. The results indicate that the data quality of Globe Land30 has been largely improved.展开更多
Recent developments of 30 m global land characterization datasets (e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from...Recent developments of 30 m global land characterization datasets (e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC (Finer Resolution Observa- tion and Monitoring-Global Land Cover) and FROM-GLC-seg (Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area (NL-ISA) and MODIS urban extent (MODIS-urban), to produce an improved 30 m global land cover map-FROM-GLC-agg (Aggregation). It was pos-processed using additional coarse res- olution datasets (i.e., MCD12Q1, GlobCover2009, MOD44W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy (i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map (at 30 m) and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.展开更多
We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from th...We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.展开更多
Vegetation and rainfall are two important factors affecting soil erosion and thus resulting in nutrient loss in the Chinese Loess Plateau.A field experiment was conducted to investigate the effects of rainfall intensi...Vegetation and rainfall are two important factors affecting soil erosion and thus resulting in nutrient loss in the Chinese Loess Plateau.A field experiment was conducted to investigate the effects of rainfall intensities(60,100 and 140 mm h-1) and vegetation(Caragana korshinskii) coverages(0%,30% and 80%) on soil loss,nutrient loss,and the composition and volume fractal dimension of eroded sediment particles under simulated rainfall conditions.The results showed that vegetation cover,rainfall intensity and their interaction all had significant effects on sediment transport and the sedimentbound nutrient loss.Higher rainfall intensity and lower coverage led to higher sediment and nutrient losses.Positive linear relationships were observed between soil loss and nutrient loss.The treatments showed more significant effects on the enrichment ratio(ER) of nitrogen(ERN) than organic matter(EROM) and phosphorus(ERP).Compared with the original surface soil,the eroded sediment contained more fine particles.Under the same coverage,the clay content significantly decreased with increasing rainfall intensity.The ER of sediment-bound nutrients was positively correlated with that of clay,suggesting that the clay fraction was preferentially eroded and soil nutrients were mainly adsorbed onto or contained within this fraction.There were increments in the fractal dimension of the sediment particles compared to that of the original surface soil.Moreover,the fractal dimension was positively correlated with clay,silt,and sediment-bound OM,N,and P contents,whereas it was negatively correlated with sand content.This study demonstrated that fractal dimension analysis can be used to characterize differences in particle-size distribution and nutrient loss associated with soil erosion.展开更多
Antarctica plays a key role in global energy balance and sea level change.It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differe...Antarctica plays a key role in global energy balance and sea level change.It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked.Land cover in Antarctica is one of the most important drivers of changes in the Earth system.Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models.However,there is a lack of complete Antarctic land cover dataset derived from a consistent data source.To fill this data gap,we have produced a database named Antarctic Land Cover Database for the Year 2000(AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus(ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer(MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale.Three land cover types were included in this map,separately,ice-free rocks,blue ice,and snow/firn.This classification legend was determined based on a review of the land cover systems in Antarctica(LCCSA) and an analysis of different land surface types and the potential of satellite data.Image classification was conducted through a combined usage of computer-aided and manual interpretation methods.A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner.An overall accuracy of 92.3%and the Kappa coefficient of 0.836 were achieved.Results show that the areas and percentages of ice-free rocks,blue ice,and snow/firn are 73268.81 km2(0.537%),225937.26 km2(1.656%),and 13345460.41 km2(97.807%),respectively.The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality.These indicate that AntarcticaLC2000,the new land cover dataset for Antarctica entirely derived from satellite data,is a reliable product for a broad spectrum of applications.展开更多
基金Under the auspices of Fundamental Research Funds for Central Universities,China University of Geosciences(Wuhan)(No.CUGL150417)National Natural Science Foundation of China(No.41274036,41301026)
文摘Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.
基金Under the auspices of National Natural Science Foundation of China (No.40871188)Knowledge Innovation Programs of Chinese Academy of Sciences (No.INFO-115-C01-SDB4-05)
文摘Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.
基金Under the auspices of National Natural Science Foundation of China (No. 40871188) National Key Technologies R&D Program of China (No. 2006BAD23B03)
文摘The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.
基金funded by the National Natural Science Foundation of China (Grant No. 41231172)the Special Fund for Surveying, Mapping and Geoinformation Scientific Research in the Public Welfare (Grant No. 201512028)National High-Tech R&D Program of China (Grant No. 2013AA122802)
文摘Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been studied since the early 1980 s, mature methods and efficient supporting tools are still lacking. This paper presents a geospatial knowledge-based verification and improvement approach for global land cover(GLC) mapping at 30-m resolution. A set of verification rules is derived from three types of land cover and its change knowledge(natural, cultural and temporal constraints). A group of web-based supporting tools is developed to facilitate the integration of and access to large amounts of ancillary data and to support online data manipulation and analysis as well as collaborative verification workflows. With this approach, two 30-m GLC datasets(Globe Land-2000 and Globe Land-2010) were verified and modified. The results indicate that the data quality of Globe Land30 has been largely improved.
基金supported by the National High-tech R&D Program of China(Grant No.2009AA12200101)the National Natural Science Foundation of China(Grant No.41301445)+1 种基金an Open Fund from the State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201202)a research grant from Tsinghua University(Grant No.2012Z02287)
文摘Recent developments of 30 m global land characterization datasets (e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC (Finer Resolution Observa- tion and Monitoring-Global Land Cover) and FROM-GLC-seg (Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area (NL-ISA) and MODIS urban extent (MODIS-urban), to produce an improved 30 m global land cover map-FROM-GLC-agg (Aggregation). It was pos-processed using additional coarse res- olution datasets (i.e., MCD12Q1, GlobCover2009, MOD44W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy (i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map (at 30 m) and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.
基金partially supported by the National High Technology Program(2013AA122804)the Special Fund for Meteorology Scientific Research in the Public Welfare(GYHY201506023)of ChinaOpen Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201514)
文摘We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.
基金Supported by the National Basic Research Program (973 Program) of China (No.2007CB407205)the National Key Technologies Research and Development Program of China during the 11th Five-Year Plan Period (No.2006BAD09B03)the CAS Action Plan for the Development of Western China (No.KZCX2-XB2-05)
文摘Vegetation and rainfall are two important factors affecting soil erosion and thus resulting in nutrient loss in the Chinese Loess Plateau.A field experiment was conducted to investigate the effects of rainfall intensities(60,100 and 140 mm h-1) and vegetation(Caragana korshinskii) coverages(0%,30% and 80%) on soil loss,nutrient loss,and the composition and volume fractal dimension of eroded sediment particles under simulated rainfall conditions.The results showed that vegetation cover,rainfall intensity and their interaction all had significant effects on sediment transport and the sedimentbound nutrient loss.Higher rainfall intensity and lower coverage led to higher sediment and nutrient losses.Positive linear relationships were observed between soil loss and nutrient loss.The treatments showed more significant effects on the enrichment ratio(ER) of nitrogen(ERN) than organic matter(EROM) and phosphorus(ERP).Compared with the original surface soil,the eroded sediment contained more fine particles.Under the same coverage,the clay content significantly decreased with increasing rainfall intensity.The ER of sediment-bound nutrients was positively correlated with that of clay,suggesting that the clay fraction was preferentially eroded and soil nutrients were mainly adsorbed onto or contained within this fraction.There were increments in the fractal dimension of the sediment particles compared to that of the original surface soil.Moreover,the fractal dimension was positively correlated with clay,silt,and sediment-bound OM,N,and P contents,whereas it was negatively correlated with sand content.This study demonstrated that fractal dimension analysis can be used to characterize differences in particle-size distribution and nutrient loss associated with soil erosion.
基金supported by the Chinese Arctic and Antarctic Administration.National Basic Research Program of China(Grant No.2012CB957704)National Natural Science Foundation of China(Grant Nos.41676176 & 41676182)National High-tech R&D Program of China(Grant No.2008AA09Z117)
文摘Antarctica plays a key role in global energy balance and sea level change.It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked.Land cover in Antarctica is one of the most important drivers of changes in the Earth system.Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models.However,there is a lack of complete Antarctic land cover dataset derived from a consistent data source.To fill this data gap,we have produced a database named Antarctic Land Cover Database for the Year 2000(AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus(ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer(MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale.Three land cover types were included in this map,separately,ice-free rocks,blue ice,and snow/firn.This classification legend was determined based on a review of the land cover systems in Antarctica(LCCSA) and an analysis of different land surface types and the potential of satellite data.Image classification was conducted through a combined usage of computer-aided and manual interpretation methods.A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner.An overall accuracy of 92.3%and the Kappa coefficient of 0.836 were achieved.Results show that the areas and percentages of ice-free rocks,blue ice,and snow/firn are 73268.81 km2(0.537%),225937.26 km2(1.656%),and 13345460.41 km2(97.807%),respectively.The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality.These indicate that AntarcticaLC2000,the new land cover dataset for Antarctica entirely derived from satellite data,is a reliable product for a broad spectrum of applications.