Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2024-07-31 DOI:10.1002/rse2.414

Chiara Aquino, Edward T. A. Mitchard, Iain M. McNicol, Harry Carstairs, Andrew Burt, Beisit L. P. Vilca, Sylvia Mayta, Mathias Disney

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Abstract

Selective logging is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been removed from four plots of 1 ha each, carefully inventoried by hand and terrestrial laser scanning before and after the logging to give a range of biomass loss (∆AGB) values. We conducted a comparative study of six multispectral optical satellite sensors at 0.3–30 m spatial resolution, to find the best combination of sensor and remote sensing indicator for change detection. Spectral reflectance, the normalised difference vegetation index (NDVI) and texture parameters were extracted after radiometric calibration and image preprocessing. The strength of the relationships between the change in these values and field‐measured ∆AGB (computed in % ha−1) was analysed. The results demonstrate that: (a) texture measures correlates more with ∆AGB than simple spectral parameters; (b) the strongest correlations are achieved for those sensors with spatial resolutions in the intermediate range (1.5–10 m), with finer or coarser resolutions producing worse results, and (c) when texture is computed using a moving square window ranging between 9 and 14 m in length. Maps predicting ∆AGB showed very promising results using a NIR‐derived texture parameter for 3 m resolution PlanetScope (R2 = 0.97 and root mean square error (RMSE) = 1.91% ha−1), followed by 1.5 m SPOT‐7 (R2 = 0.76 and RMSE = 5.06% ha−1) and 10 m Sentinel‐2 (R2 = 0.79 and RMSE = 4.77% ha−1). Our findings imply that, at least for lowland Peru, low‐medium intensity disturbance can be detected best in optical wavelengths using a texture measure derived from 3 m PlanetScope data.

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利用光学卫星数据检测热带森林中的选择性砍伐：秘鲁的一项实验表明，3 米处的纹理效果最佳

众所周知，选择性采伐在热带地区非常普遍，但目前的测绘工作却非常薄弱，部分原因是几乎没有定量数据说明哪种卫星传感器特性和分析方法最适合检测选择性采伐。为了改善这一情况，我们使用了秘鲁亚马逊南部热带森林退化实验（FODEX）地块的数据，在这些地块中，每块 1 公顷的四块土地上都有不同数量的树木被移除，在采伐前后，我们通过人工和地面激光扫描进行了仔细的清点，得出了一系列生物量损失（ΔAGB）值。我们对六种空间分辨率为 0.3-30 米的多光谱光学卫星传感器进行了比较研究，以找到传感器和遥感指标的最佳组合，用于变化检测。经过辐射校准和图像预处理后，提取了光谱反射率、归一化差异植被指数（NDVI）和纹理参数。分析了这些值的变化与实地测量的 ∆AGB（以 % ha-1 计算）之间的关系强度。结果表明(a) 与简单的光谱参数相比，纹理测量值与∆AGB 的相关性更高；(b) 空间分辨率在中间范围（1.5-10 米）的传感器的相关性最强，更细或更粗的分辨率产生的结果更差；(c) 使用长度在 9-14 米之间的移动方窗计算纹理时，相关性最强。使用近红外纹理参数预测 ∆AGB 的地图显示，3 米分辨率的 PlanetScope（R2 = 0.97，均方根误差 (RMSE) = 1.91% ha-1）结果非常好，其次是 1.5 米分辨率的 SPOT-7（R2 = 0.76，均方根误差 (RMSE) = 5.06% ha-1）和 10 米分辨率的 Sentinel-2（R2 = 0.79，均方根误差 (RMSE) = 4.77% ha-1）。我们的研究结果表明，至少对秘鲁低地而言，利用 3 m PlanetScope 数据得出的纹理测量结果，在光波长中检测中低强度干扰的效果最佳。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.