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首页> 外文期刊>International journal of applied earth observation and geoinformation >Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia
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Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

机译：印度尼西亚苏门答腊和加里曼丹的多分辨率光学影像的时间序列分析，以量化森林覆盖率损失

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Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided free of charge by the U.S. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely forest cover loss updates from regional to biome scales. The Indonesian islands of Sumatra and Kalimantan are a center of significant forest cover change within the humid tropics with implications for carbon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational coverage compared to other centers of humid tropical forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud cover obscuring the land surface. At the same time, forest change in Indonesia is transient and does not always result in deforestation, as cleared forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quantify change, are one option for monitoring forest change in cloudy regions. However, the frequency of forest cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occurring within the composite period itself. In this paper, we analyzed all Landsat 7 imagery with <50% cloud cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify forest cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping forest cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized by highly unequal observation counts and frequencies due to persistent cloud cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross forest cover loss and expert mapped gross forest cover loss at 64 sample sites. The mapped gross forest cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces.
机译：通过遥感图像监测潮湿的热带森林的丧失对于许多环境监测目标至关重要，包括碳核算，生物多样性和气候模拟科学应用。由美国地球资源观测和科学地质调查中心（USGS / EROS）免费提供的Landsat影像可实现从区域到生物群落尺度的一致及时的森林覆盖率损失更新。印度尼西亚的苏门答腊岛和加里曼丹群岛是热带潮湿地区森林覆盖率发生重大变化的中心，对碳动态，生物多样性的维护和当地生计都有影响。与其他潮湿的热带森林变化中心（例如巴西的马托格罗索州）相比，苏门答腊和加里曼丹的观测覆盖范围较差，这是由于缺乏从附近地面站进行的持续采集以及云层的持续覆盖使陆地表面变得模糊。同时，印尼的森林变化是短暂的，并不总是导致森林砍伐，因为砍伐的森林迅速被木材种植园和油棕庄园所取代。在给定的时间间隔内选择单个最佳观测值并用于量化变化的纪元复合物是监测多云地区森林变化的一种选择。然而，印度尼西亚森林覆盖率的变化频率混淆了图像合成对量化所有变化的能力。通常会错过在合成周期之间发生的瞬态变化，并且创建无云合成所需的时间长度通常会掩盖在合成周期本身内发生的变化。在本文中，我们分析了所有Landsat 7影像，其中云量覆盖率均小于50％，并使用了中分辨率成像光谱仪（MODIS）的数据和产品，以量化了2000年至2005年苏门答腊和加里曼丹的森林覆盖率损失。我们证明了时间序列方法检查所有良好的土地观测值比绘制基于图像合成的变化图更准确地描绘印度尼西亚的森林覆盖率变化。与其他采用一致的周期性观察的时间序列分析不同，我们的研究区域的特征在于，由于持续的云层覆盖，扫描线校正器关闭（SLC-off）间隙以及缺少完整的归档，导致观察次数和频率高度不相等。我们的方法通过生成通用变量空间来解决这种变化。我们根据基于独立概率样本的森林覆盖总损失估算值和64个样本点的专家制图森林覆盖总损失值评估了我们的结果。苏门答腊岛和加里曼丹省的森林覆盖图总损失为土地面积的2.86％，从2000年至2005年为2.86Mha，其中最集中的地区是廖内省和加里曼丹省的Tengah省。

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