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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Optimal use of land surface temperature data to detect changes in tropical forest cover
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Optimal use of land surface temperature data to detect changes in tropical forest cover

机译：最佳利用土地表面温度数据来检测热带森林覆盖率的变化

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Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the buildup of atmospheric CO_2. Here we examined different ways to use land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05 0.05 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of LST and Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10 10 included the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (∼1–2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pantropical deforestation classifiers. Combined with the normalized difference vegetation index, a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST decreased during 2006–2009 relative to 2001–2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES.
机译：需要对全球森林覆盖率变化进行快速而准确的评估，以集中精力进行保护并更好地了解森林砍伐如何促进大气CO_2的积累。在这里，我们研究了使用土地表面温度（LST）来检测热带森林覆盖率变化的不同方法。在我们的分析中，我们每月使用0.05 0.05 Terra中分辨率成像光谱仪（MODIS）进行LST观测，并使用巴西亚马逊森林砍伐估算程序（PRODES）进行森林覆盖率变化估算。我们还将MODIS LST观测值与根据MODIS和Landsat观测值得出的森林覆盖率损失的独立估计值进行了比较。我们的研究领域大约为10 10，其中包括巴西的马托格罗索州。为了最佳地利用LST数据来检测我们研究区域的热带森林覆盖率，我们发现使用在旱季结束时（每月最小降水量后约1-2个月）采样的数据具有最大的预测能力。在这一年的这一部分中，降水少，地表湿度最小，昼夜LST之间的差异最大。我们使用此信息来开发适用于泛热带森林砍伐分类器的简单时间采样算法。结合归一化差异植被指数，使用昼夜LST的逻辑回归模型在预测森林覆盖率变化方面表现良好。在2006–2009年期间，亚马逊地区许多地区的夜间LST年度变化相对于2001–2005年有所下降，这提供了PRODES报告的本十年后半期森林砍伐水平较低的独立确认。

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