Several deficiencies exist in the present evaluation of land reclamation quality in mining areas.These include the absence of an established set of evaluation index systems and standard acceptance criteria,as well as ...Several deficiencies exist in the present evaluation of land reclamation quality in mining areas.These include the absence of an established set of evaluation index systems and standard acceptance criteria,as well as the use of traditional sam-pling techniques,which are costly and in eficiency,and time-consuming.Compared with the traditional sampling survey methods,remote sensing has the advantages of a wide detection range,diverse information collection,multiple data-acquiring strategies,high speed,and short cycle.In this study,we used the Xinzhuang coal mining field in Yongcheng,Henan Province as an example to extract information and invert surface parameters using remote sensing techniques,based on national and local reclamation regulations and standards.Subsequently,using remote sensing,we constructed an index system for evaluating land reclamation quality in three aspects:reclaiming project quality,soil quality,and ecological benefits.Through the grading standards of evaluation indicators and quantitative remote sensing models,we determined the extracted information on the area of indicators,roads,ditches,soil moisture,organic matter,and ecological benefits after reclamation.Based on this,we established a quality evaluation model for mining land reclamation using an improved index and method.The evaluation units were divided,and the weight of the evaluation index was determined using the analytic hierarchy process and data envelopment analysis(AHP-DEA)method.The land reclamation quality in the study area was comprehensively evaluated,field accuracy was verified,and the results were analyzed.The results show that,except for the removal of roads,houses,and fishponds in the study area,all 13 evaluation units achieved a score of 60 points or higher.The quality of reclamation met the standards,and the evaluation results were consistent with the conclusions of the field investigation and project acceptance report,demonstrating the reliability and feasibility of the method developed in this study.The research results will provide technical support for the scientific evaluation of land reclamation quality.展开更多
Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was eva...Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.展开更多
Based on Remote Sensing （RS）, Geographic Information System （GIS）, and combining Principal Component Analysis, this paper designed a numerical integrated evaluation model for mountain eco-environment on the base ...Based on Remote Sensing （RS）, Geographic Information System （GIS）, and combining Principal Component Analysis, this paper designed a numerical integrated evaluation model for mountain eco-environment on the base of grid scale. Using this model, we evaluated the mountain eco-environmental quality in a case study area-the upper reaches of Minjiang River, and achieved a good result, which accorded well with the real condition. The study indicates that, the integrated evaluation model is suitable for multi-layer spatial factor computation, effectively lowing man＇s subjective influence in the evaluation process; treating the whole river basin as a system, the model shows full respect to the circulation of material and energy, synthetically embodies the determining impact of such natural condition as water-heat and landform, as well as human interference in natural eco-system; the evaluation result not only clearly presents mountainous vertical distribution features of input factors, but also provides a scientific and reliable thought for quantitatively evaluating mountain eco-environment.展开更多
Based on related literature and this research, an ecological security evaluation from the pixel scale to the small watershed or county scale was presented using remote sensing data and related models. With the driver-...Based on related literature and this research, an ecological security evaluation from the pixel scale to the small watershed or county scale was presented using remote sensing data and related models. With the driver-pressure, state and exposure to pollution-response (DPSER) model as a basis, a conceptual framework of regional ecological evaluation and an index system were established. The extraction and standardization of evaluation indices were carried out with GIS techniques, an information extraction model and a data standardization model. The conversion of regional ecological security results from the pixel scale to a small watershed or county scale was obtained with an evaluation model and a scaling model. Two conceptual scale conversion models of regional ecological security from the pixel scale to the county scale were proposed: 1) scale conversion of ecological security regime results from pixel to small watershed; and 2) scale conversion from pixel to county. These research results could provide useful ideas for regional ecological security evaluation as well as ecological and environmental management.展开更多
1. Backgrounds Land classification and evaluation are necessary foundments for county-level deci-sions of land use planning and economic development. On the bases of physical conditions,the targets of land classificat...1. Backgrounds Land classification and evaluation are necessary foundments for county-level deci-sions of land use planning and economic development. On the bases of physical conditions,the targets of land classification and land evaluation adhere to particular principles andmethodologies are to find out the quality, quantity and spatial distribution regularities of re-gional structure of land, and to obtain relevant information of land suitabilities and展开更多
[目的/意义]耕地识别是农业遥感的重要应用领域之一,但现阶段深度学习等人工智能方法在中国西南丘陵区遥感识别的研究应用深度仍然不够,识别精度有待提升。[方法]为了快速、精确地获取耕地面积、分布等信息,基于高分辨率的高分六号(GF-6...[目的/意义]耕地识别是农业遥感的重要应用领域之一,但现阶段深度学习等人工智能方法在中国西南丘陵区遥感识别的研究应用深度仍然不够,识别精度有待提升。[方法]为了快速、精确地获取耕地面积、分布等信息,基于高分辨率的高分六号(GF-6)遥感影像,运用UNet++、DeeplabV3+、UNet与PSPNet等新型深度学习模型对四川省绵阳市三台县耕地信息进行识别,并对各深度学习模型、传统机器学习方法——随机森林法及新型土地覆盖产品SinoLC-1的识别精度进行对比分析,以期深入探索深度学习方法在地物遥感识别领域的应用前景。[结果和讨论]深度学习模型在F_(1)分数、整体精度(Overall Accuracy,OA)、Kappa系数等精度评价指标的表现上,相比于传统机器学习方法和新型土地覆盖产品均有显著提升,精度提升幅度分别可达20%和50%;其中添加了密集跳跃连接技术的UNet++模型的识别效果最好,其F_(1)分数、交并比(Intersection over Union,IoU)、平均交并比(Mean Intersection over Union,MIoU)、OA值和Kappa系数值分别为0.92、85.93%、81.93%、90.60%和0.80。应用UNet++模型对2种由仅光谱特征以及光谱+地形特征两种不同特征构建的影像进行耕地提取,光谱+地形特征模型的IoU、OA和Kappa 3个指标比仅光谱特征模型分别提高了0.98%、1.10%和0.01。[结论]深度学习技术在应用于高分辨率遥感影像中的耕地识别方面展现出显著的实用价值,融合光谱和地形特征可以实现信息互补,能进一步改善耕地的识别效果。本研究可为相关部门更好地管理和利用耕地资源、推动农业可持续发展提供技术支撑。展开更多
基金supported by the National Natural Science Foundation of China (41301617)the Scientific and Technological Key Project in Henan Province (222102320005)the Key Scientific Research Project of Henan Higher Education (22A420002).
文摘Several deficiencies exist in the present evaluation of land reclamation quality in mining areas.These include the absence of an established set of evaluation index systems and standard acceptance criteria,as well as the use of traditional sam-pling techniques,which are costly and in eficiency,and time-consuming.Compared with the traditional sampling survey methods,remote sensing has the advantages of a wide detection range,diverse information collection,multiple data-acquiring strategies,high speed,and short cycle.In this study,we used the Xinzhuang coal mining field in Yongcheng,Henan Province as an example to extract information and invert surface parameters using remote sensing techniques,based on national and local reclamation regulations and standards.Subsequently,using remote sensing,we constructed an index system for evaluating land reclamation quality in three aspects:reclaiming project quality,soil quality,and ecological benefits.Through the grading standards of evaluation indicators and quantitative remote sensing models,we determined the extracted information on the area of indicators,roads,ditches,soil moisture,organic matter,and ecological benefits after reclamation.Based on this,we established a quality evaluation model for mining land reclamation using an improved index and method.The evaluation units were divided,and the weight of the evaluation index was determined using the analytic hierarchy process and data envelopment analysis(AHP-DEA)method.The land reclamation quality in the study area was comprehensively evaluated,field accuracy was verified,and the results were analyzed.The results show that,except for the removal of roads,houses,and fishponds in the study area,all 13 evaluation units achieved a score of 60 points or higher.The quality of reclamation met the standards,and the evaluation results were consistent with the conclusions of the field investigation and project acceptance report,demonstrating the reliability and feasibility of the method developed in this study.The research results will provide technical support for the scientific evaluation of land reclamation quality.
基金Project supported by the Natural Science Foundation of ZhejiangProvince China (No. Y506126).
文摘Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.
文摘Based on Remote Sensing （RS）, Geographic Information System （GIS）, and combining Principal Component Analysis, this paper designed a numerical integrated evaluation model for mountain eco-environment on the base of grid scale. Using this model, we evaluated the mountain eco-environmental quality in a case study area-the upper reaches of Minjiang River, and achieved a good result, which accorded well with the real condition. The study indicates that, the integrated evaluation model is suitable for multi-layer spatial factor computation, effectively lowing man＇s subjective influence in the evaluation process; treating the whole river basin as a system, the model shows full respect to the circulation of material and energy, synthetically embodies the determining impact of such natural condition as water-heat and landform, as well as human interference in natural eco-system; the evaluation result not only clearly presents mountainous vertical distribution features of input factors, but also provides a scientific and reliable thought for quantitatively evaluating mountain eco-environment.
基金Project supported by the National Natural Science Foundation of China (No. 40301002) and the State EnvironmentalProtection Administration of China.
文摘Based on related literature and this research, an ecological security evaluation from the pixel scale to the small watershed or county scale was presented using remote sensing data and related models. With the driver-pressure, state and exposure to pollution-response (DPSER) model as a basis, a conceptual framework of regional ecological evaluation and an index system were established. The extraction and standardization of evaluation indices were carried out with GIS techniques, an information extraction model and a data standardization model. The conversion of regional ecological security results from the pixel scale to a small watershed or county scale was obtained with an evaluation model and a scaling model. Two conceptual scale conversion models of regional ecological security from the pixel scale to the county scale were proposed: 1) scale conversion of ecological security regime results from pixel to small watershed; and 2) scale conversion from pixel to county. These research results could provide useful ideas for regional ecological security evaluation as well as ecological and environmental management.
文摘1. Backgrounds Land classification and evaluation are necessary foundments for county-level deci-sions of land use planning and economic development. On the bases of physical conditions,the targets of land classification and land evaluation adhere to particular principles andmethodologies are to find out the quality, quantity and spatial distribution regularities of re-gional structure of land, and to obtain relevant information of land suitabilities and
文摘[目的/意义]耕地识别是农业遥感的重要应用领域之一,但现阶段深度学习等人工智能方法在中国西南丘陵区遥感识别的研究应用深度仍然不够,识别精度有待提升。[方法]为了快速、精确地获取耕地面积、分布等信息,基于高分辨率的高分六号(GF-6)遥感影像,运用UNet++、DeeplabV3+、UNet与PSPNet等新型深度学习模型对四川省绵阳市三台县耕地信息进行识别,并对各深度学习模型、传统机器学习方法——随机森林法及新型土地覆盖产品SinoLC-1的识别精度进行对比分析,以期深入探索深度学习方法在地物遥感识别领域的应用前景。[结果和讨论]深度学习模型在F_(1)分数、整体精度(Overall Accuracy,OA)、Kappa系数等精度评价指标的表现上,相比于传统机器学习方法和新型土地覆盖产品均有显著提升,精度提升幅度分别可达20%和50%;其中添加了密集跳跃连接技术的UNet++模型的识别效果最好,其F_(1)分数、交并比(Intersection over Union,IoU)、平均交并比(Mean Intersection over Union,MIoU)、OA值和Kappa系数值分别为0.92、85.93%、81.93%、90.60%和0.80。应用UNet++模型对2种由仅光谱特征以及光谱+地形特征两种不同特征构建的影像进行耕地提取,光谱+地形特征模型的IoU、OA和Kappa 3个指标比仅光谱特征模型分别提高了0.98%、1.10%和0.01。[结论]深度学习技术在应用于高分辨率遥感影像中的耕地识别方面展现出显著的实用价值,融合光谱和地形特征可以实现信息互补,能进一步改善耕地的识别效果。本研究可为相关部门更好地管理和利用耕地资源、推动农业可持续发展提供技术支撑。
