Forest growing stock volume mapping with accompanying uncertainty in heterogeneous landscapes using remote sensing data

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Azamat Suleymanov, Ruslan Shagaliev, Larisa Belan, Ekaterina Bogdan, Iren Tuktarova, Eduard Nagaev, Dilara Muftakhina
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Abstract

Understanding the spatial distribution of forest properties can help improve our knowledge of carbon storage and the impacts of climate change. Despite the active use of remote sensing and machine learning (ML) methods in forest mapping, the associated uncertainty predictions are relatively uncommon. The objectives of this study were: (1) to evaluate the spatial resolution effect on growing stock volume (GSV) mapping using Sentinel-2A and Landsat 8 satellite images, (2) to identify the most key predictors, and (3) to quantify the uncertainty of GSV predictions. The study was conducted in heterogeneous landscapes, covering anthropogenic areas, logging, young plantings and mature trees. We employed an ML approach and evaluated our models by root mean squared error (RMSE) and coefficient of determination (R2) through a 10-fold cross-validation. Our results indicated that the Sentinel-2A provided the best prediction performances (RMSE = 56.6 m3/ha, R2 = 0.53) in compare with Landsat 8 (RMSE = 71.2 m3/ha, R2 = 0.23), where NDVI, LSWI and B08 band (near-infrared spectrum) were identified as key variables, with the highest contribution to the model. Moreover, the uncertainty of GSV predictions using the Sentinel-2A was much smaller compared with Landsat 8. The combined assessment of accuracy and uncertainty reinforces the suitability of Sentinel-2A for applications in heterogeneous landscapes. The higher accuracy and lower uncertainty observed with the Sentinel-2A underscores its effectiveness in providing more reliable and precise information for decision-makers. This research is important for further digital mapping endeavours with accompanying uncertainty, as uncertainty assessment plays a pivotal role in decision-making processes related to spatial assessment and forest management.

Abstract Image

利用遥感数据绘制具有不确定性的异质地貌森林蓄积量地图
了解森林属性的空间分布有助于提高我们对碳储存和气候变化影响的认识。尽管遥感和机器学习(ML)方法在森林测绘中得到了积极应用,但相关的不确定性预测却相对少见。本研究的目标是(1)利用 Sentinel-2A 和 Landsat 8 卫星图像评估空间分辨率对生长蓄积量(GSV)绘图的影响;(2)确定最关键的预测因子;(3)量化 GSV 预测的不确定性。这项研究是在异质景观中进行的,涵盖了人为区域、伐木、幼苗种植和成熟树木。我们采用了 ML 方法,并通过 10 倍交叉验证,用均方根误差(RMSE)和判定系数(R2)对模型进行了评估。结果表明,与 Landsat 8(RMSE = 71.2 立方米/公顷,R2 = 0.23)相比,Sentinel-2A 的预测效果最好(RMSE = 56.6 立方米/公顷,R2 = 0.53),其中 NDVI、LSWI 和 B08 波段(近红外光谱)被认为是关键变量,对模型的贡献最大。此外,与大地遥感卫星 8 相比,使用 Sentinel-2A 预测 GSV 的不确定性要小得多。 对精度和不确定性的综合评估加强了 Sentinel-2A 在异质地貌中的应用。在哨兵-2A 上观测到的较高精度和较低不确定性突出表明,它能有效地为决策者提供更可靠、更精确的信息。这项研究对于进一步开展带有不确定性的数字制图工作非常重要,因为不确定性评估在与空间评估和森林管理有关的决策过程中发挥着关键作用。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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