Understanding historical forest dynamics, specifically changes in forest
biomass and carbon stocks, has become critical for assessing current forest
climate benefits and projecting future benefits under various policy,
regulatory, and stewardship scenarios. Carbon accounting frameworks based
exclusively on national forest inventories are limited to broad-scale
estimates, but model-based approaches that combine these inventories with
remotely sensed data can yield contiguous fine-resolution maps of forest
biomass and carbon stocks across landscapes over time. Here we describe a
fundamental step in building a map-based stock-change framework: mapping
historical forest biomass at fine temporal and spatial resolution (annual, 30m)
across all of New York State (USA) from 1990 to 2019, using freely available
data and open-source tools.
Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA)
data, and off-the-shelf LiDAR collections we developed three modeling
approaches for mapping historical forest aboveground biomass (AGB): training on
FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps
(indirect), and an ensemble averaging predictions from the direct and indirect
models. Model prediction surfaces (maps) were tested against FIA estimates at
multiple scales. All three approaches produced viable outputs, yet tradeoffs
were evident in terms of model complexity, map accuracy, saturation, and
fine-scale pattern representation. The resulting map products can help identify
where, when, and how forest carbon stocks are changing as a result of both
anthropogenic and natural drivers alike. These products can thus serve as
inputs to a wide range of applications including stock-change assessments,
monitoring reporting and verification frameworks, and prioritizing parcels for
protection or enrollment in improved management programs.

本文描述了将纽约州 1990 年至 2019 年的历史植被覆盖范围建立成细分辨率地图的方法，利用 Landsat 成像、美国森林服务局森林清查分析数据以及即用型光探测与测距（LiDAR）收集数据建立三种不同的历史森林地上生物量映射建模方法，允许在多个时空尺度上精细测量并监测森林生态系统的演变和碳储量变化。

在小区至景观尺度上绘制历史森林生物量地图，用于库存变化评估

Mapping historical forest biomass for stock-change assessments at parcel  to landscape scales

Forest biomass is a key influence for future climate, and the world urgently
needs highly scalable financing schemes, such as carbon offsetting
certifications, to protect and restore forests. Current manual forest carbon
stock inventory methods of measuring single trees by hand are time, labour, and
cost-intensive and have been shown to be subjective. They can lead to
substantial overestimation of the carbon stock and ultimately distrust in
forest financing. The potential for impact and scale of leveraging advancements
in machine learning and remote sensing technologies is promising but needs to
be of high quality in order to replace the current forest stock protocols for
certifications.
In this paper, we present ReforesTree, a benchmark dataset of forest carbon
stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we
show that a deep learning-based end-to-end model using individual tree
detection from low cost RGB-only drone imagery is accurately estimating forest
carbon stock within official carbon offsetting certification standards.
Additionally, our baseline CNN model outperforms state-of-the-art
satellite-based forest biomass and carbon stock estimates for this type of
small-scale, tropical agro-forestry sites. We present this dataset to encourage
machine learning research in this area to increase accountability and
transparency of monitoring, verification and reporting (MVR) in carbon
offsetting projects, as well as scaling global reforestation financing through
accurate remote sensing.

本文介绍了一个基准数据集 ReforesTree，使用 RGB 无人机图像中的个体树检测，深度学习模型准确地估计了厄瓜多尔六个农林碳补偿区域的森林碳储量，以鼓励该领域的机器学习研究，提高监测、验证和报告在碳补偿项目中的透明度和监督，并通过准确的遥感来推动全球森林再造项目。