In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing...In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing dead trees surpasses that of fallen and decomposing woody debris.This stems from a suite of factors including pest disturbances,management decisions,and a changing climate.With increasingly dry and hot conditions,dead timber on a landscape increases the probability that a fire will occur.Identifying and characterizing the presence of standing dead trees on a landscape helps with forest management efforts including reductions in the wildfire hazard presented by the trees,and vulnerability of nearby park assets should the trees burn.Using forest-based classification,exploratory data analysis,and cluster vulnerability analysis,this study characterized the occurrence and implications of standing dead trees within Yellowstone National Park.The findings show standing dead trees across the entire study area with varying densities.These clusters were cross-referenced with vulnerability parameters of distance to roads,distance to trails,distance to water,distance to buildings,and slope.These parameters inform fire ignition,propagation,and impact.The weighted sum of these parameters was used to determine the vulnerability incurred on the park assets by the clusters and showed the highest values nearest to park entrances and points of interest.High vulnerability clusters warrant priority management to reduce wildfire impact.The framework of this study can be applied to other sites and incorporate additional vulnerability variables to assess forest fuel and impact.This can provide a reference for management to prioritize areas for resource conservation and improve fire prevention and suppression efficiency.展开更多
Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the impor...Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.展开更多
A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission （ PM2.5 ）, which originated from residential wood burning （RWB） in this study. Dem...A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission （ PM2.5 ）, which originated from residential wood burning （RWB） in this study. Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system（GIS）, and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM2.5 emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.展开更多
A hedonic linear regression model is constructed in this paper to estimate property value, In our model, the property value （sales price） is a function of several selected variables such as the property characterist...A hedonic linear regression model is constructed in this paper to estimate property value, In our model, the property value （sales price） is a function of several selected variables such as the property characteristics, social neighborhoods, level of neighborhood environmental contaminations, level of neighborhood crimes, and locational accessibility to jobs or services, Definitions and calculation of these variables are approached by using Geographic Information System tools, For improving estimation, gravity model is employed to measure both levels of neighborhood toxic sites and crimes; and a time-based method is used to measure the loeational accessibility rather than simple straight-line distance measurement. This study discovers that the relationship between house value and its nearby highway is nonlinear, The methodology could help policy makers assess the external effects of a property. Our model also could be used potentially to identify the current and historic trends of development caused by neighborhood or environments change in the study area.展开更多
Management practices are one of the most important factors affecting forest structure and function.Landowners in southern United States manage forests using appropriately sized areas,to meet management objectives that...Management practices are one of the most important factors affecting forest structure and function.Landowners in southern United States manage forests using appropriately sized areas,to meet management objectives that include economic return,sustainability,and esthetic enjoyment.Road networks spatially designate the socioenvironmental elements for the forests,which represented and aggregated as forest management units.Road networks are widely used for managing forests by setting logging roads and firebreaks.We propose that common types of forest management are practiced in road-delineated units that can be determined by remote sensing satellite imagery coupled with crowd-sourced road network datasets.Satellite sensors do not always capture roadcaused canopy openings,so it is difficult to delineate ecologically relevant units based only on satellite data.By integrating citizen-based road networks with the National Land Cover Database,we mapped road-delineated management units across the regional landscape and analyzed the size frequency distribution of management units.We found the road-delineated units smaller than 0.5 ha comprised 64%of the number of units,but only0.98%of the total forest area.We also applied a statistical similarity test(Warren's Index)to access the equivalency of road-delineated units with forest disturbances by simulating a serious of neutral landscapes.The outputs showed that the whole southeastern U.S.has the probability of road-delineated unit of 0.44 and production forests overlapped significantly with disturbance areas with an average probability of 0.50.展开更多
In recent decades, human development pressures have results in conversions of vast tracts of Amazonian tropical rain forests to agriculture and other human land uses. In addition to the loss of large forest cover, rem...In recent decades, human development pressures have results in conversions of vast tracts of Amazonian tropical rain forests to agriculture and other human land uses. In addition to the loss of large forest cover, remaining forests are also fragmented into smaller habitats. Fragmented forests suffer several biological and ecological changes due to edge effects that can exacerbate regional forest degradation. The Brazilian Amazon has had greatly contrasting land cover dynamics in the past decade with the highest historical rates of deforestation (2001-2005) followed by the lowest rates of forest loss in decades, since 2006. Currently, the basin-wide status and implications of forest fragmentation on remnant forests is not well known. We performed a regional forest fragmentation analysis for seven states of the Brazilian Amazon between 2001 and 2010 using a recent deforestation data. During this period, the number of forest fragments (>2 ha) doubled, nearly 125,000 fragments were formed by human activities with more than 50% being smaller than 10 ha. Over the decade, forest edges increased by an average of 36,335 km/year. However, the rate was much greater from 2001-2005 (50,046 km/year) then 2006-2010 (25,365 km/year) when deforestation rates dropped drastically. In 2010, 55% of basin-wide forest edges were < 10 years old due to the creation of large number of small fragments where intensive biological and ecological degradation is ongoing. Over the past decade protected areas have been expanded dramatically over the Brazilian Amazon and, as of 2010, 51% of remaining forests across the basin are within protected areas and only 1.5% of protected areas has been deforested. Conversely, intensive forest cover conversion has been occurred in unprotected forests. While 17% of Amazonian forests are within 1 km of forest edges in 2010, the proportion increases to 34% in unprotected areas varying between 14% and 95% among the studied states. Our results indicate that the Brazilian Amazon now largely consists of two contrasting forest conditions: protected areas with vast undisturbed forests and unprotected forests that are highly fragmented and disturbed landscapes.展开更多
We present an approach to regional environ- mental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index （NDVI） data over the period 2001-2008 ...We present an approach to regional environ- mental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index （NDVI） data over the period 2001-2008 and land cover change （LCC） analysis of the 2001 and 2008 MODIS Global Land Cover product （MCD 12Q 1）. NDVI trends were overwhelmingly negative across the grain belt with statistically significant （p ≤0.05） positive trends covering only 1% of the land surface. LCC was dominated by transitions between three classes; cropland, grassland, and a mixed cropland/natural vegeta- tion mosaic. Combining our analyses of NDVI trends and LCC, we found a pattern of agricultural abandonment （cropland to grassland） in the southern range of the grain belt coinciding with statistically significant （p≤0.05） negative NDVI trends and likely driven by regional drought. In the northern range of the grain belt we found an opposite tendency toward agricultural intensification; in this case, represented by LCC from cropland mosaic to pure cropland, and also associated with statistically significant （p≤0.05） negative NDVI trends. Relatively small clusters of statistically significant （p ≤ 0.05） positive NDVI trends corresponding with both localized land abandonment and localized agricultural intensification show that land use decision making is not uniform across the region. Land surface change in the Northern Eurasian grain belt is part of a larger pattern of land cover land use change （LCLUC） in Eastern Europe, Russia, and former territories of the Soviet Union following realignment of socialist land tenure and agricultural markets. Here, we show that a combined analysis of LCC and NDVI trends provides a more complete picture of the complex- ities of LCLUC in the Northern Eurasian grain belt,involving both broader climatic forcing, and narrower anthropogenic impacts, than might be obtained from either analysis alone.展开更多
Grain production in the countries of the former USSR sharply declined during the past two decades and has only recently started to recover. In the context of the current economic and food-price crisis, Russia, Ukraine...Grain production in the countries of the former USSR sharply declined during the past two decades and has only recently started to recover. In the context of the current economic and food-price crisis, Russia, Ukraine, and Kazakhstan might be presented with a window of opportunity to reemerge on the global agricultural market, if they succeed in increasing their productivity. The future of their agriculture, however, is highly sensitive to a combination of internal and external factors, such as institutional changes, land-use changes, climate variability and change, and global economic trends. The future of this region＇s grain production is likely to have a significant impact on the global and regional food security over the next decades.展开更多
To collect and provide periodically updated information on global forest resources,their management and use,the United Nations Food and Agriculture Organization(FAO)has been coordinating global forest resources assess...To collect and provide periodically updated information on global forest resources,their management and use,the United Nations Food and Agriculture Organization(FAO)has been coordinating global forest resources assessments(FRA)every 510 years since 1946.To complement the FRA national-based statistics and to provide an independent assessment of forest cover and change,a global remote sensing survey(RSS)has been organized as part of FAO FRA 2010.In support of the FAO RSS,an image data set appropriate for global analysis of forest extent and change has been produced.Landsat data from the Global Land Survey 19902005 were systematically sampled at each longitude and latitude intersection for all points on land.To provide a consistent data source,an operational algorithm for Landsat data pre-processing,normalization,and cloud detection was created and implemented.In this paper,we present an overview of the data processing,characteristics,and validation of the FRA RSS Landsat dataset.The FRA RSS Landsat dataset was evaluated to assess overall quality and quantify potential limitations.展开更多
Following flooding disasters,satellite images provide valuable information required for generating flood inundation maps.Multispectral or optical imagery can be used for generating flood maps when the inundated areas ...Following flooding disasters,satellite images provide valuable information required for generating flood inundation maps.Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds.We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre-and post-flood satellite images.Values of the Normalized Difference Water Index(NDWI)and Modified NDWI(MNDWI)will be higher in the post-flood image for flooded areas compared to the pre-flood image.Based on a threshold value,pixels corresponding to the flooded areas can be separated from non-flooded areas.Inundation maps derived from differencing MNDWI values accurately captured the flooded areas.However the output image will be influenced by the choice of the pre-flood image,hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years.Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features.Advantages of the proposed technique are that flood impacted areas can be identified rapidly,and that the pre-existing water bodies can be excluded from the inundation maps.Using pairs of other satellite data,several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.展开更多
The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technol...The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and users.While previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial data.Employing a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced initiatives.This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection.Furthermore,it addresses salient challenges,encompassing data quality,inherent biases,and ethical dimensions.We contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.展开更多
Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests.Field-based measurements of forest above-ground biomass(AGB)can be costly and difficult to conduct.Multi-source ...Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests.Field-based measurements of forest above-ground biomass(AGB)can be costly and difficult to conduct.Multi-source remote sensing data offers the potential to improve the accuracy of modelled AGB predictions.Here,four machine learning methods:Random Forest(RF),Gradient Boosting Decision Tree(GBDT),Classification and Regression Trees(CART),and Minimum Distance(MD)were used to construct forest AGB models of Taiyue Mountain forest,Shanxi Province,China using single and multi-sourced remote sensing data and the Google Earth Engine platform.Results showed that the machine learning method that most accurately predicted AGB were GBDT and spectral index for coniferous(R2=0.99;RMSE=65.52 Mg/ha),broadleaved(R2=0.97;RMSE=29.14 Mg/ha),and mixed-species(R2=0.97;RMSE=81.12 Mg/ha)forest types.Models constructed using bivariate variable combinations that included the spectral index improved the AGB estimation accuracy of mixed-species(R2=0.99;RMSE=59.52 Mg/ha)forest types and reduced slightly the accuracy of coniferous(R2=0.99;RMSE=101.46 Mg/ha)and broadleaved(R2=0.97;RMSE=37.59 Mg/ha)forest AGB estimation.Overall,parameterizing machine learning algorithms with multi-source remote sensing variables can improve the prediction accuracy of mixed-species forests.展开更多
基金Wyoming NASA EPSCoR Faculty Research Grant(Grant#80NSSC19M0061)Yellowstone National Park Services for their generous support and funding that made this research possible.
文摘In the Rocky Mountain and Pacific Northwest regions of the United States,forests include extensive portions of standing dead trees.These regions showcase an intriguing phenomenon where the combined biomass of standing dead trees surpasses that of fallen and decomposing woody debris.This stems from a suite of factors including pest disturbances,management decisions,and a changing climate.With increasingly dry and hot conditions,dead timber on a landscape increases the probability that a fire will occur.Identifying and characterizing the presence of standing dead trees on a landscape helps with forest management efforts including reductions in the wildfire hazard presented by the trees,and vulnerability of nearby park assets should the trees burn.Using forest-based classification,exploratory data analysis,and cluster vulnerability analysis,this study characterized the occurrence and implications of standing dead trees within Yellowstone National Park.The findings show standing dead trees across the entire study area with varying densities.These clusters were cross-referenced with vulnerability parameters of distance to roads,distance to trails,distance to water,distance to buildings,and slope.These parameters inform fire ignition,propagation,and impact.The weighted sum of these parameters was used to determine the vulnerability incurred on the park assets by the clusters and showed the highest values nearest to park entrances and points of interest.High vulnerability clusters warrant priority management to reduce wildfire impact.The framework of this study can be applied to other sites and incorporate additional vulnerability variables to assess forest fuel and impact.This can provide a reference for management to prioritize areas for resource conservation and improve fire prevention and suppression efficiency.
基金Supported by the National Natural Science Foundation of China (Nos.40671145 and 60573115)the Provincial Natural Science Foundation of Guangdong,China (Nos.04300504 and 05006623)
文摘Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.
基金The research contract fromCalifornia Air Resources Board (ARB) ,USAthe Talented FoundationfromNortheast Institute of Geography and AgriculturalEcology,Chinese Academy of Sciences ,China(No.C08Y17)
文摘A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission （ PM2.5 ）, which originated from residential wood burning （RWB） in this study. Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system（GIS）, and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM2.5 emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.
基金Under the auspices of the Research Client West Oakland Environmental Indicators Taskforce, Talented Foundationof Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (No. C08Y17)
文摘A hedonic linear regression model is constructed in this paper to estimate property value, In our model, the property value （sales price） is a function of several selected variables such as the property characteristics, social neighborhoods, level of neighborhood environmental contaminations, level of neighborhood crimes, and locational accessibility to jobs or services, Definitions and calculation of these variables are approached by using Geographic Information System tools, For improving estimation, gravity model is employed to measure both levels of neighborhood toxic sites and crimes; and a time-based method is used to measure the loeational accessibility rather than simple straight-line distance measurement. This study discovers that the relationship between house value and its nearby highway is nonlinear, The methodology could help policy makers assess the external effects of a property. Our model also could be used potentially to identify the current and historic trends of development caused by neighborhood or environments change in the study area.
基金funding from the Macrosystems Biology Program Grant EF#1241860 from United States National Science Foundation(NSF)。
文摘Management practices are one of the most important factors affecting forest structure and function.Landowners in southern United States manage forests using appropriately sized areas,to meet management objectives that include economic return,sustainability,and esthetic enjoyment.Road networks spatially designate the socioenvironmental elements for the forests,which represented and aggregated as forest management units.Road networks are widely used for managing forests by setting logging roads and firebreaks.We propose that common types of forest management are practiced in road-delineated units that can be determined by remote sensing satellite imagery coupled with crowd-sourced road network datasets.Satellite sensors do not always capture roadcaused canopy openings,so it is difficult to delineate ecologically relevant units based only on satellite data.By integrating citizen-based road networks with the National Land Cover Database,we mapped road-delineated management units across the regional landscape and analyzed the size frequency distribution of management units.We found the road-delineated units smaller than 0.5 ha comprised 64%of the number of units,but only0.98%of the total forest area.We also applied a statistical similarity test(Warren's Index)to access the equivalency of road-delineated units with forest disturbances by simulating a serious of neutral landscapes.The outputs showed that the whole southeastern U.S.has the probability of road-delineated unit of 0.44 and production forests overlapped significantly with disturbance areas with an average probability of 0.50.
文摘In recent decades, human development pressures have results in conversions of vast tracts of Amazonian tropical rain forests to agriculture and other human land uses. In addition to the loss of large forest cover, remaining forests are also fragmented into smaller habitats. Fragmented forests suffer several biological and ecological changes due to edge effects that can exacerbate regional forest degradation. The Brazilian Amazon has had greatly contrasting land cover dynamics in the past decade with the highest historical rates of deforestation (2001-2005) followed by the lowest rates of forest loss in decades, since 2006. Currently, the basin-wide status and implications of forest fragmentation on remnant forests is not well known. We performed a regional forest fragmentation analysis for seven states of the Brazilian Amazon between 2001 and 2010 using a recent deforestation data. During this period, the number of forest fragments (>2 ha) doubled, nearly 125,000 fragments were formed by human activities with more than 50% being smaller than 10 ha. Over the decade, forest edges increased by an average of 36,335 km/year. However, the rate was much greater from 2001-2005 (50,046 km/year) then 2006-2010 (25,365 km/year) when deforestation rates dropped drastically. In 2010, 55% of basin-wide forest edges were < 10 years old due to the creation of large number of small fragments where intensive biological and ecological degradation is ongoing. Over the past decade protected areas have been expanded dramatically over the Brazilian Amazon and, as of 2010, 51% of remaining forests across the basin are within protected areas and only 1.5% of protected areas has been deforested. Conversely, intensive forest cover conversion has been occurred in unprotected forests. While 17% of Amazonian forests are within 1 km of forest edges in 2010, the proportion increases to 34% in unprotected areas varying between 14% and 95% among the studied states. Our results indicate that the Brazilian Amazon now largely consists of two contrasting forest conditions: protected areas with vast undisturbed forests and unprotected forests that are highly fragmented and disturbed landscapes.
文摘We present an approach to regional environ- mental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index （NDVI） data over the period 2001-2008 and land cover change （LCC） analysis of the 2001 and 2008 MODIS Global Land Cover product （MCD 12Q 1）. NDVI trends were overwhelmingly negative across the grain belt with statistically significant （p ≤0.05） positive trends covering only 1% of the land surface. LCC was dominated by transitions between three classes; cropland, grassland, and a mixed cropland/natural vegeta- tion mosaic. Combining our analyses of NDVI trends and LCC, we found a pattern of agricultural abandonment （cropland to grassland） in the southern range of the grain belt coinciding with statistically significant （p≤0.05） negative NDVI trends and likely driven by regional drought. In the northern range of the grain belt we found an opposite tendency toward agricultural intensification; in this case, represented by LCC from cropland mosaic to pure cropland, and also associated with statistically significant （p≤0.05） negative NDVI trends. Relatively small clusters of statistically significant （p ≤ 0.05） positive NDVI trends corresponding with both localized land abandonment and localized agricultural intensification show that land use decision making is not uniform across the region. Land surface change in the Northern Eurasian grain belt is part of a larger pattern of land cover land use change （LCLUC） in Eastern Europe, Russia, and former territories of the Soviet Union following realignment of socialist land tenure and agricultural markets. Here, we show that a combined analysis of LCC and NDVI trends provides a more complete picture of the complex- ities of LCLUC in the Northern Eurasian grain belt,involving both broader climatic forcing, and narrower anthropogenic impacts, than might be obtained from either analysis alone.
文摘Grain production in the countries of the former USSR sharply declined during the past two decades and has only recently started to recover. In the context of the current economic and food-price crisis, Russia, Ukraine, and Kazakhstan might be presented with a window of opportunity to reemerge on the global agricultural market, if they succeed in increasing their productivity. The future of their agriculture, however, is highly sensitive to a combination of internal and external factors, such as institutional changes, land-use changes, climate variability and change, and global economic trends. The future of this region＇s grain production is likely to have a significant impact on the global and regional food security over the next decades.
文摘To collect and provide periodically updated information on global forest resources,their management and use,the United Nations Food and Agriculture Organization(FAO)has been coordinating global forest resources assessments(FRA)every 510 years since 1946.To complement the FRA national-based statistics and to provide an independent assessment of forest cover and change,a global remote sensing survey(RSS)has been organized as part of FAO FRA 2010.In support of the FAO RSS,an image data set appropriate for global analysis of forest extent and change has been produced.Landsat data from the Global Land Survey 19902005 were systematically sampled at each longitude and latitude intersection for all points on land.To provide a consistent data source,an operational algorithm for Landsat data pre-processing,normalization,and cloud detection was created and implemented.In this paper,we present an overview of the data processing,characteristics,and validation of the FRA RSS Landsat dataset.The FRA RSS Landsat dataset was evaluated to assess overall quality and quantify potential limitations.
基金We thank the US Geological Survey (USGS) for providing no-cost Landsat data and supporting this work under Grant/Cooperative Agreement No. G18AP00077 to the first author.
文摘Following flooding disasters,satellite images provide valuable information required for generating flood inundation maps.Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds.We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre-and post-flood satellite images.Values of the Normalized Difference Water Index(NDWI)and Modified NDWI(MNDWI)will be higher in the post-flood image for flooded areas compared to the pre-flood image.Based on a threshold value,pixels corresponding to the flooded areas can be separated from non-flooded areas.Inundation maps derived from differencing MNDWI values accurately captured the flooded areas.However the output image will be influenced by the choice of the pre-flood image,hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years.Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features.Advantages of the proposed technique are that flood impacted areas can be identified rapidly,and that the pre-existing water bodies can be excluded from the inundation maps.Using pairs of other satellite data,several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.
基金supported by the Faculty Startup Fund of the College of Arts and Sciences at Emory University.
文摘The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and users.While previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial data.Employing a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced initiatives.This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection.Furthermore,it addresses salient challenges,encompassing data quality,inherent biases,and ethical dimensions.We contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.
基金support by the National Key Research and Development Program of China(Intergovernmental and international cooperation in science,technology and innovation)under Grant Number 2022YFE0127700Royal Society International Exchanges 2022 Cost Share(NSFC)under Grant number IEC\NSFC\223567.
文摘Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests.Field-based measurements of forest above-ground biomass(AGB)can be costly and difficult to conduct.Multi-source remote sensing data offers the potential to improve the accuracy of modelled AGB predictions.Here,four machine learning methods:Random Forest(RF),Gradient Boosting Decision Tree(GBDT),Classification and Regression Trees(CART),and Minimum Distance(MD)were used to construct forest AGB models of Taiyue Mountain forest,Shanxi Province,China using single and multi-sourced remote sensing data and the Google Earth Engine platform.Results showed that the machine learning method that most accurately predicted AGB were GBDT and spectral index for coniferous(R2=0.99;RMSE=65.52 Mg/ha),broadleaved(R2=0.97;RMSE=29.14 Mg/ha),and mixed-species(R2=0.97;RMSE=81.12 Mg/ha)forest types.Models constructed using bivariate variable combinations that included the spectral index improved the AGB estimation accuracy of mixed-species(R2=0.99;RMSE=59.52 Mg/ha)forest types and reduced slightly the accuracy of coniferous(R2=0.99;RMSE=101.46 Mg/ha)and broadleaved(R2=0.97;RMSE=37.59 Mg/ha)forest AGB estimation.Overall,parameterizing machine learning algorithms with multi-source remote sensing variables can improve the prediction accuracy of mixed-species forests.
