Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina
Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina
复制标题
使用基于卫星图像的地图和地面清单数据来估计巴西圣卡塔琳娜州剩余大西洋森林的面积
DOI：
10.1016/j.rse.2012.10.023
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发表时间：
2013
影响因子：
13.5
通讯作者：
Adilson Luiz Nicoletti
中科院分区：
文献类型：
--
作者：
A. Vibrans;R. McRoberts;Paolo Moser;Adilson Luiz Nicoletti
由于图像处理、后勤和数据获取的限制，从基于遥感的地图估计大面积森林属性，如森林覆盖面积，是具有挑战性的。此外，估计和补偿错误分类以及估计不确定性的技术通常不熟悉。巴西南部圣卡塔里纳州的森林面积是从四张基于卫星图像的土地覆盖地图中分别估算出来的，并通过对作为圣卡塔琳娜森林和植物调查一部分进行评估的1000多个点的森林/非森林的观察得出了一个独立的估计数。后一组数据也被用作评估这四张地图的精度评估样本。MAP分析包括识别分类错误，构建误差矩阵，计算相关的精度度量，估计偏差，并使用模型辅助回归估计器构建比例森林估计的95%可信区间。地图的总体精度从0.876到0.929不等。估计数的标准误差均小于简单随机抽样估计数的标准误差，系数从大约1.23到大约1.69不等。模型辅助回归估计器易于实现，用于调整估计的分类偏差和构建可信区间。
Estimation of large area forest attributes, such as area of forest cover, from remote sensing-based maps is challenging because of image processing, logistical, and data acquisition constraints. In addition, techniques for estimating and compensating for misclassification and estimating uncertainty are often unfamiliar. Forest area for the state of Santa Catarina in southern Brazil was estimated from each of four satellite image-based land cover maps, and an independent estimate was obtained using observations of forest/non-forest for more than 1000 points assessed as part of the Santa Catarina Forest and Floristic Inventory. The latter data were also used as an accuracy assessment sample for evaluating the four maps. The map analyses consisted of identifying classification errors, constructing error matrices, calculating associated accuracy measures, estimating bias, and constructing 95% confidence intervals for proportion forest estimates using a model-assisted regression estimator. Overall accuracies for the maps ranged from 0.876 to 0.929. The standard errors of the estimates were all smaller than the standard error of the simple random sampling estimate by factors ranging from approximately1.23 to approximately 1.69. The model-assisted regression estimator lends itself to easy implementation for adjusting for estimated classification bias and for constructing confidence intervals.