为了加强大面积范围内未利用地监管,提出通过遥感技术识别存在潜在污染的未利用地.以甘肃省北部地区为研究区,首先,基于Landsat卫星数据进行土地利用/覆被类型遥感解译,确定该区域未利用土地范围.其次,对图像进行主成分分析,将第一主分...为了加强大面积范围内未利用地监管,提出通过遥感技术识别存在潜在污染的未利用地.以甘肃省北部地区为研究区,首先,基于Landsat卫星数据进行土地利用/覆被类型遥感解译,确定该区域未利用土地范围.其次,对图像进行主成分分析,将第一主分量作为灰度共生矩阵的数据源,选用能量、熵、惯性矩、相关作为特征量,同时结合对应图像的灰度变化绝对值提取变化较大的区域.最后,通过对比2010年和2015年Landsat遥感图像的特征量变化情况,提取有明显纹理或灰度变化区域,结合Google Earth高分辨率影像与包含工矿企业位置信息的感兴趣点(point of interest,POI)数据,得到2010—2015年此区域土壤疑似污染点40处,总面积约为10 km2.对其中21处结果进行实地调查验证,其中有19处疑似污染点被证实,识别精度约为90%.提出的基于灰度共生矩阵方法识别未利用地疑似污染的方法,较传统人工解译方法,能够显著节省人力、物力,提高监测效率,并且具有较好的精度.展开更多
Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and ...Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and AW3D30 Digital Surface Model data,combined with three machine learning algorithms—Random Forest(RF),Back Propagation Neural Network(BPNN),and Extreme Gradient Boosting(XGBoost)—to evaluate the performance of canopy height inversion and understory terrain reconstruction.The analysis emphasizes the impact of topographic and vegetation-related factors on model accuracy.Results reveal that slope is the most influential variable,contributing three to five times more to model performance than other features.In low-slope areas,understory terrain tends to be underestimated,whereas high-slope areas often result in overestimation.Moreover,the Normalized Difference Vegetation Index(NDVI)and land cover types,particularly forests and grasslands,significantly affect prediction accuracy,with model performance showing heightened sensitivity to vegetation characteristics in these regions.Among the models tested,XGBoost demonstrated superior performance,achieving a canopy height bias of-0.06 m,a root mean square error(RMSE)of 4.69 m for canopy height,and an RMSE of 9.82 m for understory terrain.Its ability to capture complex nonlinear relationships and handle high-dimensional data underlines its robustness.While the RF model exhibited strong stability and resistance to noise,its accuracy lagged slightly behind XGBoost.The BPNN model,by contrast,struggled in areas with complex terrain.This study offers valuable insights into feature selection and optimization in remote sensing applications,providing a reference framework for enhancing the accuracy and efficiency of environmental monitoring practices.展开更多
在总太阳辐射(TSR,total solar radiation)中,对植物光合作用有效的太阳辐射称为光合有效辐射(PAR,photosynthetically active radiation),波长范围约为400-700 nm,其吸收系数(FPAR)是碳循环研究的一个关键生理变量,也是表征植被生态系...在总太阳辐射(TSR,total solar radiation)中,对植物光合作用有效的太阳辐射称为光合有效辐射(PAR,photosynthetically active radiation),波长范围约为400-700 nm,其吸收系数(FPAR)是碳循环研究的一个关键生理变量,也是表征植被生态系统的基本变量之一。基于30米空间分辨率的Landsat反射率数据,得到青藏高原区域的地表植被类型分类结果,以及不同植被类型生长季内归一化植被指数(NDVI)累积频率的98%(NDVI_max)和2%(NDVI_min)。为克服简单比值指数(SR)和NDVI在单独估算FPAR时分别存在低估和高估的问题,构建基于SR和NDVI的FPAR联合估算模型,生产了1987-2022年青藏高原区域4-9月平均FPAR产品。FPAR作为计算植被固碳量的参数之一,可用于评价植被生态系统状态,在生态环境监测、气候变化研究以及自然资源管理等领域有广泛的潜在应用价值。本产品以Geo TIFF格式保存,空间参考为地理坐标系GCS_WGS_1984(ESPG:4326)。展开更多
This report references data compiled by NASA’s Aqua and Terra Earth observation satellites. From 2000 to 2017, global areas covered by greenery increased by 5 percent, of which 25 percent lie in China. In fact, China...This report references data compiled by NASA’s Aqua and Terra Earth observation satellites. From 2000 to 2017, global areas covered by greenery increased by 5 percent, of which 25 percent lie in China. In fact, China accounts for only 6.6 percent of global vegetation coverage. So, how did these changes happen?展开更多
文摘为了加强大面积范围内未利用地监管,提出通过遥感技术识别存在潜在污染的未利用地.以甘肃省北部地区为研究区,首先,基于Landsat卫星数据进行土地利用/覆被类型遥感解译,确定该区域未利用土地范围.其次,对图像进行主成分分析,将第一主分量作为灰度共生矩阵的数据源,选用能量、熵、惯性矩、相关作为特征量,同时结合对应图像的灰度变化绝对值提取变化较大的区域.最后,通过对比2010年和2015年Landsat遥感图像的特征量变化情况,提取有明显纹理或灰度变化区域,结合Google Earth高分辨率影像与包含工矿企业位置信息的感兴趣点(point of interest,POI)数据,得到2010—2015年此区域土壤疑似污染点40处,总面积约为10 km2.对其中21处结果进行实地调查验证,其中有19处疑似污染点被证实,识别精度约为90%.提出的基于灰度共生矩阵方法识别未利用地疑似污染的方法,较传统人工解译方法,能够显著节省人力、物力,提高监测效率,并且具有较好的精度.
基金funded by the National Key Research and Development Program(Grants No.2023YFE0207900)。
文摘Accurate reconstruction of understory terrain is essential for environmental monitoring and resource management.This study integrates 1:10,000 Digital Elevation Model,Global Ecosystem Dynamics Investigation(GEDI),and AW3D30 Digital Surface Model data,combined with three machine learning algorithms—Random Forest(RF),Back Propagation Neural Network(BPNN),and Extreme Gradient Boosting(XGBoost)—to evaluate the performance of canopy height inversion and understory terrain reconstruction.The analysis emphasizes the impact of topographic and vegetation-related factors on model accuracy.Results reveal that slope is the most influential variable,contributing three to five times more to model performance than other features.In low-slope areas,understory terrain tends to be underestimated,whereas high-slope areas often result in overestimation.Moreover,the Normalized Difference Vegetation Index(NDVI)and land cover types,particularly forests and grasslands,significantly affect prediction accuracy,with model performance showing heightened sensitivity to vegetation characteristics in these regions.Among the models tested,XGBoost demonstrated superior performance,achieving a canopy height bias of-0.06 m,a root mean square error(RMSE)of 4.69 m for canopy height,and an RMSE of 9.82 m for understory terrain.Its ability to capture complex nonlinear relationships and handle high-dimensional data underlines its robustness.While the RF model exhibited strong stability and resistance to noise,its accuracy lagged slightly behind XGBoost.The BPNN model,by contrast,struggled in areas with complex terrain.This study offers valuable insights into feature selection and optimization in remote sensing applications,providing a reference framework for enhancing the accuracy and efficiency of environmental monitoring practices.
文摘在总太阳辐射(TSR,total solar radiation)中,对植物光合作用有效的太阳辐射称为光合有效辐射(PAR,photosynthetically active radiation),波长范围约为400-700 nm,其吸收系数(FPAR)是碳循环研究的一个关键生理变量,也是表征植被生态系统的基本变量之一。基于30米空间分辨率的Landsat反射率数据,得到青藏高原区域的地表植被类型分类结果,以及不同植被类型生长季内归一化植被指数(NDVI)累积频率的98%(NDVI_max)和2%(NDVI_min)。为克服简单比值指数(SR)和NDVI在单独估算FPAR时分别存在低估和高估的问题,构建基于SR和NDVI的FPAR联合估算模型,生产了1987-2022年青藏高原区域4-9月平均FPAR产品。FPAR作为计算植被固碳量的参数之一,可用于评价植被生态系统状态,在生态环境监测、气候变化研究以及自然资源管理等领域有广泛的潜在应用价值。本产品以Geo TIFF格式保存,空间参考为地理坐标系GCS_WGS_1984(ESPG:4326)。
文摘This report references data compiled by NASA’s Aqua and Terra Earth observation satellites. From 2000 to 2017, global areas covered by greenery increased by 5 percent, of which 25 percent lie in China. In fact, China accounts for only 6.6 percent of global vegetation coverage. So, how did these changes happen?
