Zhu Shanyou, Zhang Hailong, Liu Ronggao, Cao Yun, Zhang Guixin
School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.
ScientificWorldJournal. 2014;2014:919456. doi: 10.1155/2014/919456. Epub 2014 Sep 1.
Sampling designs are commonly used to estimate deforestation over large areas, but comparisons between different sampling strategies are required. Using PRODES deforestation data as a reference, deforestation in the state of Mato Grosso in Brazil from 2005 to 2006 is evaluated using Landsat imagery and a nearly synchronous MODIS dataset. The MODIS-derived deforestation is used to assist in sampling and extrapolation. Three sampling designs are compared according to the estimated deforestation of the entire study area based on simple extrapolation and linear regression models. The results show that stratified sampling for strata construction and sample allocation using the MODIS-derived deforestation hotspots provided more precise estimations than simple random and systematic sampling. Moreover, the relationship between the MODIS-derived and TM-derived deforestation provides a precise estimate of the total deforestation area as well as the distribution of deforestation in each block.
抽样设计通常用于估计大面积的森林砍伐情况,但需要对不同的抽样策略进行比较。以巴西森林砍伐和退化监测计划(PRODES)的森林砍伐数据为参考,利用陆地卫星图像和近同步的中分辨率成像光谱仪(MODIS)数据集,对巴西马托格罗索州2005年至2006年的森林砍伐情况进行评估。利用中分辨率成像光谱仪得出的森林砍伐数据来辅助抽样和外推。根据基于简单外推和线性回归模型对整个研究区域的森林砍伐估计,对三种抽样设计进行比较。结果表明,使用中分辨率成像光谱仪得出的森林砍伐热点进行分层抽样以构建分层和分配样本,比简单随机抽样和系统抽样能提供更精确的估计。此外,中分辨率成像光谱仪得出的森林砍伐数据与专题绘图仪(TM)得出的森林砍伐数据之间的关系,能精确估计森林砍伐总面积以及每个区域的森林砍伐分布情况。