Biological soil crusts(BSCs) are the important components of alpine meadow ecosystems.The extent and morphology of BSCs vary greatly with alpine meadow retrogressive succession due to grazing pressure.There is signifi...Biological soil crusts(BSCs) are the important components of alpine meadow ecosystems.The extent and morphology of BSCs vary greatly with alpine meadow retrogressive succession due to grazing pressure.There is significant interest in impacts of crust composition on plant seed germination,especial l y in(semi-) arid environments.However,little is known about the influences of BSCs,and their associations with alpine meadow succession,on germination of typical alpine meadow vascular plant species.In a full factorial common-gardenexperiment,we studied effects of:(1) crust type,(2) seed position,and(3) surface texture on seed germination.We chose three typical alpine meadow plant species(i.e.Poa pratensis,Tibetia himalaica and Potentillen nivea),which belonged to different functional groups(graminoids,legumes,and forbs) and play important roles in all alpine meadow succession stages.Crust type and seed position influenced seed germination,and the inhibitory effects of BSCs depended on the crust type and seed species tested.The major factors influencing seed germination were BSC type,seed position,soil texture,and the interactions between BSC type and seed position; species and seed position; species andsurface texture; and species,crust type,and surface texture.Cyanobacteria crust significantly inhibited germination of all seeds.Seed position also had a significant effect on seed germination(p < 0.001).Fewer seedlings germinated on the surface than below the surface,this was especially true for P.nivea.seeds within cyanobacteria and lichen crusts.Only germination rates of T.himalaica on the soil surface were significantly correlated with plant occurrence frequency within the alpine meadow community.The poor correlation for the other two species is possibly that they are perennials.Our results clearly demonstrated that BSCs can be biological filters during seed germination,depending on the BSC succession stage.Through their influences on seed germination,BSCs can strongly influence community assemblages throughout alpine meadow retrogressive succession.展开更多
Water quality is critical to ensure that marine resources and the environment are utilized in a sustainable manner. The objective of this study is therefore to investigate the optimum placement of marine environmental...Water quality is critical to ensure that marine resources and the environment are utilized in a sustainable manner. The objective of this study is therefore to investigate the optimum placement of marine environmental monitoring sites to monitor water quality in Shanghai, China. To improve the mapping or estimation accuracy of the areas with different water quality grades, the monitoring sites were fixed in transition bands between areas of different grades rather than in other positions. Following bidirectional optimization method, first, 18 candidate sites were selected by filtering out specific site categories. Second, three of these were, in turn, eliminated because of the rule defined by the changes in the areas of water quality grades and by the standard deviation of the interpolation errors of dissolved inorganic nitrogen(DIN) and phosphate(PO_4-P). Furthermore, indicator kriging was employed to depict the transition bands between different water quality grades whenever new sampling sites were added. The four optimization projects of the newly added sites reveal that, all optimized sites were distributed in the transition bands of different water grades, and at the same time in the areas where the historical sites were sparsely distributed. New sites were also found in the overlap region of different transition bands. Additional sites were especially required in these regions to discriminate the boundaries of different water quality grades. Using the bidirectional optimization method of the monitoring sites, the boundaries of different water quality grades could be determined with a higher precision. As a result, the interpolation errors of DIN and PO_4-P could theoretically decrease.展开更多
China has made great achievements in industrial development and is transforming into a powerful manufacturing country.Meanwhile,the industrial land scale is also expanding.However,whether industrial structure upgradin...China has made great achievements in industrial development and is transforming into a powerful manufacturing country.Meanwhile,the industrial land scale is also expanding.However,whether industrial structure upgrading achieves the purpose of restraining industrial land expansion remains unanswered.By calculating the industrial land structure index(ILSI)and industrial land expansion scale(ILES),this study analyzed their temporal and spatial distribution characteristics at both regional and city levels from 2007to 2020 in China.Results show that industrial land expansion presents a different trend in the four regions,the ILES in the eastern region is the largest,and the speed of industrial land expansion has declined since 2013,but it has gradually increased since 2016.The ILSI of the eastern and central regions is higher than that of the western and northeastern regions.Furthermore,a spatial Durbin model(SDM)has been established to estimate the spatial effect of industrial structure upgrading on industrial land expansion from 2007 to2020.Notably,industrial structure upgrading has not slowed industrial land expansion.The eastern and western regions require a greater amount of industrial land while upgrading the industrial structure.The improvement of the infrastructure level and international trade level has promoted industrial land expansion.展开更多
In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy o...In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy of results, or in other words, acquirement of higher ac-curacy at less cost. Counter to drawbacks of previous application models, e.g. lack of compre-hensive and quantitative-comparison, the optimal decision-making model of spatial sampling is proposed. This model first acquires the possible accuracy-cost diagrams of multiple schemes through initial spatial exploration, then regresses them and standardizes them into a unified ref-erence frame, and finally produces the relatively optimal sampling scheme by using the discrete decision-making function (built by this paper) and comparing them in combination with the dia-grams. According to the test result in the survey of the arable land using remotely sensed data, the Sandwich model, while applied in the survey of the thin-feature and cultivated land areas with aerial photos, can better realize the goal of the best balance between investment and accuracy. With this case and other cases, it is shown that the optimal decision-making model of spatial sampling is a good choice in the survey of the farm areas using remote sensing, with its distin-guished benefit of higher precision at less cost or vice versa. In order to extensively apply the model in the surveys of natural resources, including arable farm areas, this paper proposes the prototype of development using the component technology, that could considerably improve the analysis efficiency by insetting program components within the software environment of GIS and RS.展开更多
Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to c...Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to characterize the entire study area.Spatial prediction is required to comprehensively investigate potentially contaminated areas.Consequently,machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution.The characteristics,advantages,and applications of machine learning models used to predict soil pollution are reviewed in this study.Satisfactory model performance generally requires the following:1)selection of the most appropriate model with the required structure;2)selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability;3)improvement of model reliability through comprehensive model evaluation;and 4)integration of geostatistics with the machine learning model.With the enrichment of environmental data and development of algorithms,machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.展开更多
In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed d...In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification.Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification.To assess the performance of discretization methods this article adopts three indicators,which are the compression capability indicator(CCI),consistency indicator(CI),and number of the cut points(NCP).An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods.To investigate the effectiveness of our method,this article applies three discretization methods of the Entropy/MDL,Naive,and SemiNaive to a TM image and three indicators for these discretization methods are then calculated.After comparing the three indicators and the classification accuracies of the discretized remotely sensed images,it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.展开更多
Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the ...Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.展开更多
基金funded by the Chinese National Natural Scientific Foundation(Grant No.31270576)by the grant(Grant No.2016-ZJ-710)from Qinghai Province and Key laboratory of crop molecular breeding of Qinghai Province
文摘Biological soil crusts(BSCs) are the important components of alpine meadow ecosystems.The extent and morphology of BSCs vary greatly with alpine meadow retrogressive succession due to grazing pressure.There is significant interest in impacts of crust composition on plant seed germination,especial l y in(semi-) arid environments.However,little is known about the influences of BSCs,and their associations with alpine meadow succession,on germination of typical alpine meadow vascular plant species.In a full factorial common-gardenexperiment,we studied effects of:(1) crust type,(2) seed position,and(3) surface texture on seed germination.We chose three typical alpine meadow plant species(i.e.Poa pratensis,Tibetia himalaica and Potentillen nivea),which belonged to different functional groups(graminoids,legumes,and forbs) and play important roles in all alpine meadow succession stages.Crust type and seed position influenced seed germination,and the inhibitory effects of BSCs depended on the crust type and seed species tested.The major factors influencing seed germination were BSC type,seed position,soil texture,and the interactions between BSC type and seed position; species and seed position; species andsurface texture; and species,crust type,and surface texture.Cyanobacteria crust significantly inhibited germination of all seeds.Seed position also had a significant effect on seed germination(p < 0.001).Fewer seedlings germinated on the surface than below the surface,this was especially true for P.nivea.seeds within cyanobacteria and lichen crusts.Only germination rates of T.himalaica on the soil surface were significantly correlated with plant occurrence frequency within the alpine meadow community.The poor correlation for the other two species is possibly that they are perennials.Our results clearly demonstrated that BSCs can be biological filters during seed germination,depending on the BSC succession stage.Through their influences on seed germination,BSCs can strongly influence community assemblages throughout alpine meadow retrogressive succession.
基金supported by the National Natural Science Foundation of China(Nos.41376190,41531179,41421001 and 41601425)the Scientific Research Project of Shanghai Marine Bureau(No.Hu Hai Ke2016-05)the Ocean Public Welfare Scientific Research Project,State Oceanic Administration of the People’s Republic of China(Nos.201505008 and 201305027)
文摘Water quality is critical to ensure that marine resources and the environment are utilized in a sustainable manner. The objective of this study is therefore to investigate the optimum placement of marine environmental monitoring sites to monitor water quality in Shanghai, China. To improve the mapping or estimation accuracy of the areas with different water quality grades, the monitoring sites were fixed in transition bands between areas of different grades rather than in other positions. Following bidirectional optimization method, first, 18 candidate sites were selected by filtering out specific site categories. Second, three of these were, in turn, eliminated because of the rule defined by the changes in the areas of water quality grades and by the standard deviation of the interpolation errors of dissolved inorganic nitrogen(DIN) and phosphate(PO_4-P). Furthermore, indicator kriging was employed to depict the transition bands between different water quality grades whenever new sampling sites were added. The four optimization projects of the newly added sites reveal that, all optimized sites were distributed in the transition bands of different water grades, and at the same time in the areas where the historical sites were sparsely distributed. New sites were also found in the overlap region of different transition bands. Additional sites were especially required in these regions to discriminate the boundaries of different water quality grades. Using the bidirectional optimization method of the monitoring sites, the boundaries of different water quality grades could be determined with a higher precision. As a result, the interpolation errors of DIN and PO_4-P could theoretically decrease.
基金Under the auspices of National Natural Science Foundation of China(No.72074181)National Social Science Foundation of China(No.20CJY023)Innovation Capability Support Program of Shaanxi(No.2021KJXX-12)。
文摘China has made great achievements in industrial development and is transforming into a powerful manufacturing country.Meanwhile,the industrial land scale is also expanding.However,whether industrial structure upgrading achieves the purpose of restraining industrial land expansion remains unanswered.By calculating the industrial land structure index(ILSI)and industrial land expansion scale(ILES),this study analyzed their temporal and spatial distribution characteristics at both regional and city levels from 2007to 2020 in China.Results show that industrial land expansion presents a different trend in the four regions,the ILES in the eastern region is the largest,and the speed of industrial land expansion has declined since 2013,but it has gradually increased since 2016.The ILSI of the eastern and central regions is higher than that of the western and northeastern regions.Furthermore,a spatial Durbin model(SDM)has been established to estimate the spatial effect of industrial structure upgrading on industrial land expansion from 2007 to2020.Notably,industrial structure upgrading has not slowed industrial land expansion.The eastern and western regions require a greater amount of industrial land while upgrading the industrial structure.The improvement of the infrastructure level and international trade level has promoted industrial land expansion.
基金the National Key Fundamental Research Development Planning Project(Grant No.KZCX1-Y-02)the High-tech Research and Development(863)Programme of the Ministry of Science and Technology(Grant No.2002AA135230)+1 种基金the Projects of the Chinese Academy of Sciences(Grant Nos.KZ951-A1-302,KZ951-A1-203, KJ951-B1-703) the National Natural Science Foundation of China(Grant Nos.49871064 , 69896250).
文摘In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy of results, or in other words, acquirement of higher ac-curacy at less cost. Counter to drawbacks of previous application models, e.g. lack of compre-hensive and quantitative-comparison, the optimal decision-making model of spatial sampling is proposed. This model first acquires the possible accuracy-cost diagrams of multiple schemes through initial spatial exploration, then regresses them and standardizes them into a unified ref-erence frame, and finally produces the relatively optimal sampling scheme by using the discrete decision-making function (built by this paper) and comparing them in combination with the dia-grams. According to the test result in the survey of the arable land using remotely sensed data, the Sandwich model, while applied in the survey of the thin-feature and cultivated land areas with aerial photos, can better realize the goal of the best balance between investment and accuracy. With this case and other cases, it is shown that the optimal decision-making model of spatial sampling is a good choice in the survey of the farm areas using remote sensing, with its distin-guished benefit of higher precision at less cost or vice versa. In order to extensively apply the model in the surveys of natural resources, including arable farm areas, this paper proposes the prototype of development using the component technology, that could considerably improve the analysis efficiency by insetting program components within the software environment of GIS and RS.
基金supported by the National Key Research and Development Program of China(No.2018YFC1800100)the National Natural Science Foundation of China(No.42277475).
文摘Soil pollution levels can be quantified via sampling and experimental analysis;however,sampling is performed at discrete points with long distances owing to limited funding and human resources,and is insufficient to characterize the entire study area.Spatial prediction is required to comprehensively investigate potentially contaminated areas.Consequently,machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution.The characteristics,advantages,and applications of machine learning models used to predict soil pollution are reviewed in this study.Satisfactory model performance generally requires the following:1)selection of the most appropriate model with the required structure;2)selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability;3)improvement of model reliability through comprehensive model evaluation;and 4)integration of geostatistics with the machine learning model.With the enrichment of environmental data and development of algorithms,machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.40971222)the National High Technology Research and Development Program of China(Grant No.2006AA120106)。
文摘In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification.Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification.To assess the performance of discretization methods this article adopts three indicators,which are the compression capability indicator(CCI),consistency indicator(CI),and number of the cut points(NCP).An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods.To investigate the effectiveness of our method,this article applies three discretization methods of the Entropy/MDL,Naive,and SemiNaive to a TM image and three indicators for these discretization methods are then calculated.After comparing the three indicators and the classification accuracies of the discretized remotely sensed images,it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.
基金supported by the National Natural Science Foundation of China (Grant Nos.42271340,42122009)the Ningbo Science and Technology Innovation 2025 Major Special Project (Grant Nos.2022Z189,2021Z107,2022Z181)the Zhejiang Province ‘Pioneering Soldier’ and ‘Leading Goose’ R&D Project (Grant No.2023C01027)。
文摘Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.
