ABSTRACT

To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m.

We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired.

To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019.

TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.

摘要

为了与全球森林砍伐作斗争，在亚年度范围内监测森林动荡是一项关键挑战。为此，新的Planetscope纳米卫星星座改变了游戏规则，重访时间为1天，像素大小为3米。

我们提出了一个基于PlanetScope影像的近实时森林干扰警报系统：阈值奖励和Pen悔算法（TRP）。一旦获取新的PlanetScope图像，它将生成新的森林变化图。

为了校准和验证TRP，构建了一个参考集，作为意大利托斯卡纳五个随机选择的研究区域的完整普查。我们处理了2018年5月1日至2019年7月5日之间获得的572幅PlanetScope图像。

在研究期间，使用TRP绘制森林变化图，最终用户的准确性为86％，最终生产者的准确性为92％。此外，我们使用可用于少量参考数据样本的无偏分层估计量来估计森林变化面积。基于样本的56.89公顷的估计值的95％置信区间包括基于人口普查的56.19公顷的面积估计值。