Causal Analysis of Accuracy Obtained Using High-Resolution Global Forest Change Data to Identify Forest Loss in Small Forest Plots

Yusuke Yamada,Katsuto Shimizu,Toshihiro Ohkubo

doi:10.3390/rs12152489

Yusuke Yamada, Katsuto Shimizu + Show 1 more

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https://doi.org/10.3390/rs12152489

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Abstract

Identifying areas of forest loss is a fundamental aspect of sustainable forest management. Global Forest Change (GFC) datasets developed by Hansen et al. (in Science 342:850–853, 2013) are publicly available, but the accuracy of these datasets for small forest plots has not been assessed. We used a forest-wide polygon-based approach to assess the accuracy of using GFC data to identify areas of forest loss in an area containing numerous small forest plots. We evaluated the accuracy of detection of individual forest-loss polygons in the GFC dataset in terms of a “recall ratio”, the ratio of the spatial overlap of a forest-loss polygon determined from the GFC dataset to the area of a corresponding reference forest-loss polygon, which we determined by visual interpretation of aerial photographs. We analyzed the structural relationships of recall ratio with area of forest loss, tree species, and slope of the forest terrain by using linear non-Gaussian acyclic modelling. We showed that only 11.1% of forest-loss polygons in the reference dataset were successfully identified in the GFC dataset. The inferred structure indicated that recall ratio had the strongest relationships with area of forest loss, forest tree species, and height of the forest canopy. Our results indicate the need for careful consideration of structural relationships when using GFC datasets to identify areas of forest loss in regions where there are small forest plots. Moreover, further studies are required to examine the structural relationships for accuracy of land-use classification in forested areas in various regions and with different forest characteristics.

Highlights

Because forest losses greatly affect the benefits derived from the preservation of ecosystems [1,2,3], understanding their spatial distribution is important for effective forest management
As noted in the preceding section, we showed that larger areas of forest loss were better identified by the Global Forest Change (GFC) dataset
Higher mean digital canopy model (DCM) values and sharper crown forms had positive effects on recall ratio. These results indicate that forest losses that provided large amounts of timber from major forestry species (Japanese cedar and Japanese cypress) were more detected in the GFC dataset

Summary

Introduction

Because forest losses greatly affect the benefits derived from the preservation of ecosystems [1,2,3], understanding their spatial distribution is important for effective forest management. Many research projects have identified the ecological importance of the spatial patterns of forest and areas of forest degradation or loss [4,5]. Forest loss in riparian areas often results in deterioration of stream water quality [6,7,8]. Understanding the regional distribution of areas of forest, forest loss, and forest degradation is useful for taking stock of the current situation, predicting future conditions, and undertaking sustainable forest planning [11,12,13]. Detecting forest losses is a fundamental requirement for sustainable forest management

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