基于变化更新的中东欧国家森林覆盖制图OA北大核心CSTPCD
Forest Cover Mapping of Central and Eastern European Countries Based on Change Detection and Update
[目的]分析中东欧国家森林资源覆盖状况,了解中东欧各国林业的现实情形和独特优势,为中国与中东欧国家加强林业合作提供基础数据.[方法]基于Google Earth Engine(GEE)平台获取中东欧国家 2020年生长季无云影像,利用随机森林算法对影像进行分类,应用连续变化检测和分类算法得到森林变化信息并更新初步分类结果,最终获得中东欧国家土地覆盖分类结果.利用欧盟统计局土地利用/覆盖区域框架调查(LUCAS)数据、目视解译数据以及联合国粮农组织统计数据(FAOSTAT)对本研究分类结果进行验证和评估,结合谷歌高分辨率影像对制图结果进行分析.[结果]本研究分类结果利用LUCAS数据和目视解译数据的森林类型用户精度分别为0.930和 0.911,生产者精度分别为 0.860和 0.956,总体精度分别为 0.810和 0.881,整体上优于GlobeLand30 2020产品(森林类型用户精度分别为 0.920和 0.900,生产者精度分别为 0.690和 0.840,总体精度分别为 0.700和 0.832).根据研究结果计算出中东欧国家总体森林覆盖率为 39.6%,相比GlobeLand30 2020结果(34.4%),与FAOSTAT的森林覆盖率(40.0%)更接近.目视上看,本制图结果在细节刻画上比GlobeLand30 2020产品更丰富,更能准确反映森林分布特征.截至 2020年,中东欧国家森林覆盖呈北部、东南地区和西南地区丰富密集,中部分布广泛且相对较少的特点,爱沙尼亚、拉脱维亚、斯洛文尼亚、黑山等国家森林分布茂密,波兰、匈牙利等国家森林分布较为稀少.[结论]针对中东欧国家森林覆盖应用需求,本研究提出基于产品变化更新的森林覆盖制图方法,生产了中东欧国家 2020年森林覆盖产品.本研究方法可为大区域森林覆盖制图提供新的借鉴和参考,结果有助于宏观了解中东欧国家森林覆盖状况.
[Objective]The Central and Eastern European Countries(CEEC)which are the Belt and Road initiative are important forces in Europe and link Asia and Europe.Forest resources monitoring helps understand the forest development in CEEC,and is the basis for forestry cooperation between China and CEEC.[Method]Taking CEEC as the study area,the cloudless images of CEEC in the growing season in 2020 are obtained based on the Google Earth Engine platform.The images are classified by random forest algorithm.The forest change information is obtained by the continue change detection and classification(CCDC)algorithm,and updated classification results by this information.Finally,the land cover results of CEEC are obtained.The results are evaluated and verified through the land use/cover area frame survey(LUCAS)data,visual interpretation data,and Food and Agricultural Organization of the United Nations statistical data(FAOSTAT).Finally,the mapping results are analyzed through Google high-resolution images.[Result]Results show that our map obtained the users'accuracy for forest type is 0.930 and 0.911 which validated by the LUCAS and visual interpretation data,the producer's accuracy for forest type is 0.860 and 0.956,respectively,overall accuracy is 0.810 and 0.881,respectively.The accuracy of our map is better than the GlobeLand30 2020 products,in which the user's accuracy for forest type is 0.920 and 0.900,while the producer's accuracy for forest type is 0.690 and 0.840,respectively,and the overall accuracy is 0.700 and 0.832.According to the research results,the overall forest coverage rate of CEEC is 39.6%,this result is closer to the FAOSTAT result(40.0%)than the GlobeLand30 2020 result(34.4%).The final mapping results are richer in detail than the GlobeLand30 2020 product and can more accurately reflect the distribution characteristics of forests.In 2020,the forest cover of CEEC is rich in the north,southeast,and southwest,and less in the south.Estonia,Latvia,Slovenia,Montenegro,and other countries have rich forest distribution,while Poland,Hungary,and other countries have relatively rare forest distribution.[Conclusion]This study has developed a forest cover mapping method based on change detection and update according to the application requirements of forest cover in CEEC and obtain a 2020 LC map.The product has high precision and provides a new reference for regional forest cover mapping research.This result is helpful in understanding the forest cover situation in CEEC.
王春玲;史锴源;庞勇;蒙诗栎
北京林业大学信息学院 北京 100083||国家林业和草原局林业智能信息处理工程技术研究中心 北京 100083中国林业科学研究院资源信息研究所 北京 100091||国家林业和草原局林业遥感与信息技术重点实验室 北京 100091
林学
中东欧国家Google Earth Engine森林资源遥感监测
Central and Eastern European Countries(CEEC)Google Earth Engine(GEE)forest resourceremote sensing monitoring
《林业科学》 2024 (005)
116-126 / 11
中央级公益性科研院所基本科研业务费专项资金重点项目"中国-中东欧林业合作联合研究中心平台构建"(CAFYBB2018GD001).
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