干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提...干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提取植被光谱指数和土壤盐度指数,共计21个变量,将其输入装袋回归(Bootstrap aggregating,Bagging)算法中,构建了土壤盐度预测模型Model-Ⅰ;使用最相关最小冗余(Max-relevance and min-redundancy,mRMR)方法筛选特征变量,将其输入Bagging中,构建了土壤盐度预测模型Model-Ⅱ,使用野外采样数据来辅助建模并进行验证。通过模型评价指标对Model-Ⅰ和Model-Ⅱ进行评估。结果表明:Model-Ⅱ的预测性能优于Model-Ⅰ(验证集决定系数为0.66,均方根误差为18.00 dS·m-1,四分位数的相对预测误差为3.21),mRMR有效降低了多维特征冗余问题。PlanetScope影像结合mRMR方法成功绘制了高分辨率土壤盐度图,提供了更详细的土壤盐度空间分布信息,研究结果对利用PlanetScope数据监测土壤盐渍化信息起推动作用。展开更多
Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics simila...Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.展开更多
In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral ima...In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.展开更多
To adopt sustainable crop practices in changing climate, understandingthe climatic parameters and water requirements with vegetation is crucial on aspatiotemporal scale. The Planetscope (PS) constellation of more than...To adopt sustainable crop practices in changing climate, understandingthe climatic parameters and water requirements with vegetation is crucial on aspatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps areone of the highest resolution data that can transform agricultural practices andmanagement on a large scale. High-resolution PS nanosatellite data was utilizedin the current study to monitor agriculture’s spatiotemporal assessment for theAl-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVIwas utilized to assess the vegetation pattern change in the study area. The currentstudy area has sparse vegetation, and exposed soil exhibits brightness due to lowsoil moisture, constraining NDVI. Therefore, a machine learning (ML) basedRandom Forest (RF) classification model was used to compare the vegetationextent and computational cost of NDVI. The RF model has been compared withNDVI in the current investigation. It is one of the most precise classificationmethods because it can model the complexity of input variables, handle outliers,treat noise effectively, and avoid overfitting. Multinomial Logistic Regression(MLR) was implemented to compare the performance of both NDVI and RFbased classification. RF model provided good accuracy (98%) for all vegetationclasses based on user accuracy, producer accuracy, and kappa coefficient.展开更多
Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution ...Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution satellites and CubeSat platforms has been launched.In this study,satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables,and the improvement in wheat yield estimation was evaluated.Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression.Nine vegetation indices(NDVI,GNDVI,MSAVI2,MTVI2,MTCI,reNDVI,SAVI,EVI and WDVI)and spectral bands were tested.During the model training,the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data.However,combining environmental data with the PlanetScope data realized the highest estimation accuracy.For the validated models,adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy,and the results were slightly better with the Sentinel-2 data than with the PlanetScope data.Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.展开更多
水体黑臭程度遥感监测是了解城市水质现状和综合评价城市水环境治理效果的重要手段.以南京、常州、无锡和扬州为研究区,共采集171个样点,同步测量水质参数和光学参数,分析黑臭水体与一般水体的水色和光学特征,构建决策树模型进行重度黑...水体黑臭程度遥感监测是了解城市水质现状和综合评价城市水环境治理效果的重要手段.以南京、常州、无锡和扬州为研究区,共采集171个样点,同步测量水质参数和光学参数,分析黑臭水体与一般水体的水色和光学特征,构建决策树模型进行重度黑臭水体、轻度黑臭水体和非黑臭水体(记为一般水体)识别.结果表明:(1)根据色度可将水体分为1~6类水体,其中,类型1~4为黑臭水体,分别为灰黑色、深灰色、灰色和浅灰色水体,类型5和类型6水体为一般水体,分别为绿色系和黄色系水体;(2)类型1水体的非色素颗粒物和有色可溶性有机物含量高,但色素颗粒物的吸收并不占主导,类型2和5水体的吸收以色素颗粒物吸收占主导,类型3、4和6水体的吸收以非色素颗粒物吸收占主导;(3)根据六类水体的反射光谱差异用黑臭水体差值指数(difference of black-odorous water index,DBWI)、三波段面积水体指数(green-red-nir area water index,G-R-NIR AWI)、绿光波段反射率和归一化黑臭水体指数(normalized difference black-odorous water index,NDBWI)构建的水体分类识别决策树,能够有效识别出重、轻度黑臭水体和一般水体;(4)将决策树模型应用于2019年4月9日扬州的PlanetScope影像上,并利用10个同步过境点进行验证,整体识别精度达到80.00%,K值达到0.67.通过水色分类后的城市水体分级模型方法,可推广应用于类似的水体,为黑臭水体监管提供技术方法.展开更多
文摘干旱半干旱地区急需高分辨率的土壤盐度图用于显示盐度空间分布的细微变化,指导盐渍化区域和潜在盐渍化区域制定土地资源管理政策和水资源管理政策,防止土壤进一步退化,保障农业经济可持续发展和粮食安全生产。基于PlanetScope影像,提取植被光谱指数和土壤盐度指数,共计21个变量,将其输入装袋回归(Bootstrap aggregating,Bagging)算法中,构建了土壤盐度预测模型Model-Ⅰ;使用最相关最小冗余(Max-relevance and min-redundancy,mRMR)方法筛选特征变量,将其输入Bagging中,构建了土壤盐度预测模型Model-Ⅱ,使用野外采样数据来辅助建模并进行验证。通过模型评价指标对Model-Ⅰ和Model-Ⅱ进行评估。结果表明:Model-Ⅱ的预测性能优于Model-Ⅰ(验证集决定系数为0.66,均方根误差为18.00 dS·m-1,四分位数的相对预测误差为3.21),mRMR有效降低了多维特征冗余问题。PlanetScope影像结合mRMR方法成功绘制了高分辨率土壤盐度图,提供了更详细的土壤盐度空间分布信息,研究结果对利用PlanetScope数据监测土壤盐渍化信息起推动作用。
基金supported by the National Natural Science Foundation of China(Grant No.42271078)the Key Research and Development Program of Shaanxi(Grant No.2024SF-YBXM669)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0902)。
文摘Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.
文摘In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.
文摘To adopt sustainable crop practices in changing climate, understandingthe climatic parameters and water requirements with vegetation is crucial on aspatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps areone of the highest resolution data that can transform agricultural practices andmanagement on a large scale. High-resolution PS nanosatellite data was utilizedin the current study to monitor agriculture’s spatiotemporal assessment for theAl-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVIwas utilized to assess the vegetation pattern change in the study area. The currentstudy area has sparse vegetation, and exposed soil exhibits brightness due to lowsoil moisture, constraining NDVI. Therefore, a machine learning (ML) basedRandom Forest (RF) classification model was used to compare the vegetationextent and computational cost of NDVI. The RF model has been compared withNDVI in the current investigation. It is one of the most precise classificationmethods because it can model the complexity of input variables, handle outliers,treat noise effectively, and avoid overfitting. Multinomial Logistic Regression(MLR) was implemented to compare the performance of both NDVI and RFbased classification. RF model provided good accuracy (98%) for all vegetationclasses based on user accuracy, producer accuracy, and kappa coefficient.
基金supported by the University of Szeged Open Access Fund[grant no 6077]Ministry of Innovation and Technology of Hungary through the National Research,Development and Innovation Fund Project[grant no TKP2021-NVA].
文摘Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution satellites and CubeSat platforms has been launched.In this study,satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables,and the improvement in wheat yield estimation was evaluated.Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression.Nine vegetation indices(NDVI,GNDVI,MSAVI2,MTVI2,MTCI,reNDVI,SAVI,EVI and WDVI)and spectral bands were tested.During the model training,the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data.However,combining environmental data with the PlanetScope data realized the highest estimation accuracy.For the validated models,adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy,and the results were slightly better with the Sentinel-2 data than with the PlanetScope data.Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.
文摘水体黑臭程度遥感监测是了解城市水质现状和综合评价城市水环境治理效果的重要手段.以南京、常州、无锡和扬州为研究区,共采集171个样点,同步测量水质参数和光学参数,分析黑臭水体与一般水体的水色和光学特征,构建决策树模型进行重度黑臭水体、轻度黑臭水体和非黑臭水体(记为一般水体)识别.结果表明:(1)根据色度可将水体分为1~6类水体,其中,类型1~4为黑臭水体,分别为灰黑色、深灰色、灰色和浅灰色水体,类型5和类型6水体为一般水体,分别为绿色系和黄色系水体;(2)类型1水体的非色素颗粒物和有色可溶性有机物含量高,但色素颗粒物的吸收并不占主导,类型2和5水体的吸收以色素颗粒物吸收占主导,类型3、4和6水体的吸收以非色素颗粒物吸收占主导;(3)根据六类水体的反射光谱差异用黑臭水体差值指数(difference of black-odorous water index,DBWI)、三波段面积水体指数(green-red-nir area water index,G-R-NIR AWI)、绿光波段反射率和归一化黑臭水体指数(normalized difference black-odorous water index,NDBWI)构建的水体分类识别决策树,能够有效识别出重、轻度黑臭水体和一般水体;(4)将决策树模型应用于2019年4月9日扬州的PlanetScope影像上,并利用10个同步过境点进行验证,整体识别精度达到80.00%,K值达到0.67.通过水色分类后的城市水体分级模型方法,可推广应用于类似的水体,为黑臭水体监管提供技术方法.
