A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011.
A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011.
复制标题
DOI：
10.1016/j.rse.2017.04.021
复制
发表时间：
2017-06
影响因子：
13.5
通讯作者：
Suming Jin;Limin Yang;Zhe Zhu;C. Homer
中科院分区：
文献类型：
--
作者：
Suming Jin;Limin Yang;Zhe Zhu;C. Homer
土地覆盖变化的监测和制图是支持评价生态系统状况和过渡的重要途径。2001年阿拉斯加州国家土地覆盖数据库（NLCD）是第一个基于2001年前后Landsat图像和地理空间辅助数据的30米分辨率基线土地覆盖产品。我们开发了一种名为AKUP11的综合方法来更新阿拉斯加2001年至2011年的NLCD，并提供该州土地覆盖和土地覆盖变化的10年周期性更新。我们的方法旨在描述与不同驱动因素相关的主要土地覆盖变化，包括主要由野火和森林采伐引起的森林向灌木和草地的转变，干扰后的植被演替过程，以及与天气和气候变化相关的地表水范围和冰川冰雪的变化。对于自然植被区域，开发了AKUP11-VEG组件用于更新土地覆盖，主要包括四个步骤：1)利用Landsat图像和辅助数据集识别受干扰和演替的区域；2)使用SKILL模型（基于知识的综合轨迹土地覆盖标记系统）更新这些地区的土地覆盖状态；3)进行决策树分类；4)通过后处理建模得到最终的土地覆盖和土地覆盖变化产品。对于水和冰雪地区，开发了另一个名为AKUP11-WIS的组件，用于初始土地覆盖变化检测，去除地形阴影效应，并使用3年MODIS积雪范围数据集（2010 - 2012）排除短暂的积雪变化。总体方法在阿拉斯加的三个试点研究区域进行了测试，每个区域由四个Landsat图像足迹组成。试点研究结果表明，2011年更新的土地覆盖标签总体精度为86%，总体精度为90%。该方法为捕获主要扰动事件和更新阿拉斯加的土地覆盖提供了一种稳健、一致和有效的方法。该方法随后被应用于产生整个阿拉斯加州的土地覆盖和土地覆盖变化产品。
Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.