Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN 46202, USA.
Sensors (Basel). 2018 Dec 16;18(12):4452. doi: 10.3390/s18124452.
Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing'an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.
森林在全球碳预算和生态过程中起着重要作用。精确绘制森林覆盖图有助于大大减少对陆地碳平衡估计的不确定性。需要一种可靠和可操作的方法来快速进行区域森林制图。在这项研究中，我们的目标是利用高时空分辨率的 HJ-1 图像和时间序列植被指数，在基于对象的图像分析和决策树分类的背景下，对中国东北地区的森林和亚类进行制图。在单一年份获取多时相 HJ-1 图像提供了获取物候信息的机会。通过分析森林和非森林、森林亚类之间的光谱和物候信息差异，最终提出了基于阈值的决策树。基于实地调查的地面实况数据验证，生成的森林图具有 0.91 ± 0.01 的高总体精度和 95%置信区间。从 HJ-1 图像中提取的森林图与两个现有的全球土地覆盖数据集进行了比较：GlobCover 2009 和 MCD12Q1 2009。基于 HJ-1 的森林面积大于 MCD12Q1 和 GlobCover 的面积，与中国东北地区森林面积占比超过 40%的国家统计数据更为接近。空间不一致主要发生在大兴安岭北部、三江平原和松辽平原西南部。比较结果还表明，来自全球土地覆盖产品的森林亚类信息可能会给生态建模带来很大的不确定性，因此在各种生态模型中应谨慎使用。考虑到更高的时空分辨率，基于 HJ-1 的森林产品可以作为生物地球化学模型（特别是碳循环模型）的非常有用的输入，这些模型需要对森林面积和类型进行准确和最新的估计。
