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TROPICAL DRY FOREST DEGRADATION ESTIMATION AT LOCAL SCALE WITH UAV IMAGES

机译:无人机图像对当地热带干林退化的估算

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Forest degradation is a dynamic process, and its accurate mapping and detection has been limited by the lack of spatial and temporal resolution of conventional remote sensing, especially in tropical dry forests (TDF). The objective of this work is to assess UAV images for mapping and quantifying forest degradation of the TDF at local scale. Firstly, the accuracy of UAV images to estimate forest attributes (canopy height, canopy cover, biomass and frequency of individuals) is evaluated. These attributes are then integrated to estimate the status of forest degradation. UAV images were obtained for both rainy and dry season, also field measurements at 22 plots. UAV images were processed by photogrammetry of motion structure, and a canopy height model (CHM) and a mosaic in RGB are created. The CHM calculates canopy height, in combination with the RGB mosaic, the canopy cover was delimitated through an object-based image analysis. For the estimation of biomass and frequency of individuals, multiple linear regression models are developed, which allows the attributes data from field to be related to the height and canopy coverage estimated by UAV images. Forest degradation states are estimated using a relative degradation index. The preliminary results show that the processing of the UAV images has obtained a good accuracy for the average and maximum canopy height with an error of 0.4 - 3.1 m, respectively. The delimitation of the canopy cover has an overall accuracy of 95%. Forest attributes from UAV images are expected to continue to be calculated reliably, compared to those at the ground level.
机译:森林退化是一个动态过程,由于缺乏常规遥感的时空分辨率,特别是在热带干旱森林(TDF)中,其精确的制图和检测受到限制。这项工作的目的是评估无人机图像,以便在当地范围内绘制和量化TDF的森林退化。首先,评估了无人机图像估计森林属性(冠层高度,冠层覆盖,生物量和个体频率)的准确性。然后将这些属性综合起来,以估计森林退化的状况。在雨季和旱季都获得了无人机图像,还获得了22个地块的野外测量结果。通过运动结构的摄影测量来处理无人机图像,并创建冠层高度模型(CHM)和RGB马赛克。 CHM结合RGB马赛克计算冠层高度,并通过基于对象的图像分析来确定冠层的覆盖范围。为了估计个体的生物量和频率,开发了多个线性回归模型,该模型允许来自田野的属性数据与无人机图像估计的身高和树冠覆盖率相关。使用相对退化指数估算森林退化状态。初步结果表明,对无人机图像的处理已获得了良好的平均冠层高度和最大冠层高度精度,误差分别为0.4-3.1 m。遮篷的边界的总体精度为95%。与地面相比,从无人机图像获得的森林属性有望继续可靠地计算。

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