The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied...The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.展开更多
High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection...High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.展开更多
由于茶园大多分布在地形复杂的山区,地块破碎,分布零散,形状差异大、植被混杂且茶园所处环境长期受到云雨的影响,增加了茶园遥感识别的难度与不确定性,针对这一问题,该研究提出了利用高分1号(GF-1)和哨兵2号(Sentinel-2)时序数据提取茶...由于茶园大多分布在地形复杂的山区,地块破碎,分布零散,形状差异大、植被混杂且茶园所处环境长期受到云雨的影响,增加了茶园遥感识别的难度与不确定性,针对这一问题,该研究提出了利用高分1号(GF-1)和哨兵2号(Sentinel-2)时序数据提取茶园的方法,以浙江省武义县王宅镇为研究区,采用GF-1号为主要数据源,并利用MODIS地表反射率产品和Sentinel-2反射率数据,基于时空融合算法得到时间分辨率5 d的10 m Sentinel-2完整的时序数据。综合利用GF-1在空间细节方面的优势和重建的Sentinel-2高观测频率时序数据在反映茶树生长过程方面的优势,分别基于GF-1的光谱和纹理特征及GF-1的光谱、纹理特征和Sentinel-2时序特征两种特征组合方式,采用随机森林算法提取茶园。结果表明,GF-1光谱、纹理信息结合Sentinel-2时序信息分类结果的准确率、错误率、精确率、召回率和F1分数分别为96.91%、3.09%、89.00%、83.09%和0.86,仅基于GF-1光谱和纹理信息的分类准确率、错误率、精确率、召回率和F1分数分别为94.72%、5.28%、73.09%、84.61%和0.78,添加时序信息分类结果总体优于未添加时序信息的分类结果。表明高空间分辨率结合高频率时序遥感数据是提高茶园分类精度的有效手段。展开更多
文摘The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.
基金supported by National Key Research and Development Program of China under grant number 2022YFB3903404National Natural Science Foundation of China under grant number 42325105,42071350LIESMARS Special Research Funding.
文摘High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.
文摘由于茶园大多分布在地形复杂的山区,地块破碎,分布零散,形状差异大、植被混杂且茶园所处环境长期受到云雨的影响,增加了茶园遥感识别的难度与不确定性,针对这一问题,该研究提出了利用高分1号(GF-1)和哨兵2号(Sentinel-2)时序数据提取茶园的方法,以浙江省武义县王宅镇为研究区,采用GF-1号为主要数据源,并利用MODIS地表反射率产品和Sentinel-2反射率数据,基于时空融合算法得到时间分辨率5 d的10 m Sentinel-2完整的时序数据。综合利用GF-1在空间细节方面的优势和重建的Sentinel-2高观测频率时序数据在反映茶树生长过程方面的优势,分别基于GF-1的光谱和纹理特征及GF-1的光谱、纹理特征和Sentinel-2时序特征两种特征组合方式,采用随机森林算法提取茶园。结果表明,GF-1光谱、纹理信息结合Sentinel-2时序信息分类结果的准确率、错误率、精确率、召回率和F1分数分别为96.91%、3.09%、89.00%、83.09%和0.86,仅基于GF-1光谱和纹理信息的分类准确率、错误率、精确率、召回率和F1分数分别为94.72%、5.28%、73.09%、84.61%和0.78,添加时序信息分类结果总体优于未添加时序信息的分类结果。表明高空间分辨率结合高频率时序遥感数据是提高茶园分类精度的有效手段。
