Biodiversity Observation for Land and Ecosystem Health (BOLEH): A Robust Method to Evaluate the Management Impacts on the Bundle of Carbon and Biodiversity Ecosystem Services in Tropical Production Forests

Kitayama, Kanehiro; Fujiki, Shogoro; Aoyagi, Ryota; Imai, Noriko; Sugau, John B.; Titin, Jupiri; Nilus, Reuben; Lagan, Peter; Sawada, Yoshimi; Kugan, Frederick; Mannan, Sam

doi:10.3390/su10114224

Cited by 15 publications

(12 citation statements)

References 36 publications

(71 reference statements)

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“…Continuous forest management is crucial, which requires precise knowledge of forest characteristics through detailed information extraction [3]. Spatiotemporal change detection, logging, and evaluating the forest management regime in the region can bring more transparency into the management and ecosystem services in the forest [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Ahmadi

Kalantar

Saeidi³

et al. 2020

Remote Sensing

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The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.

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Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Ahmadi

Kalantar

Saeidi³

et al. 2020

Remote Sensing

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“…Analyzing the welfare effects of organic and Fairtrade standards, Meemken and colleagues (2017) used panel data from coffee producers in Uganda to study the direct effects of these two certification standards on household expenditures, child education, and nutrition. Linear studies with an environmental focus have similarly assessed impact through such metrics related to gross tree cover loss (Garrett et al, 2016), pesticide usage (Ho et al, 2018), and carbon density (Kitayama et al, 2018).…”

Section: Assessing Impactmentioning

confidence: 99%

Questioning Impact: A Cross-Disciplinary Review of Certification Standards for Sustainability

et al. 2022

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This article provides a review of scholarly approaches to assessing the impact of certification standards for sustainability. While we observe that some theoretical advances have afforded a better understanding of the potential impacts of adopting such standards, we also find that progress has been constrained due to a strong emphasis on assessing impact via linear causal pathways. This linear focus on the net effects for single stakeholders, such as farmers and producers, local communities and ecosystems, falls short of adequately capturing the broader impact of certifications across social and ecological dimensions. Inspired by theories on complex systems thinking, we present a framework based on a systems-based impact logic that better captures and assesses the impacts of certification standards within broader social-ecological systems. Our framework can be used as a heuristic to design impact-related studies and assess the impact of certification standards across disciplinary vantage points and empirical contexts.

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“…We flew unmanned aerial vehicles (UAVs; Mavic 2 Pro and Phantom4 Pro, DJI) and obtained aerial photography in August and September 2019 as ground-truth vegetation data. We took pictures of the 18 plots of Kitayama et al [44], 11 plots of Langner et al [45], two plots of Imai et al [46], and some of the abandoned logging roads (see Figure 1b). The total flight area was about 820 ha.…”

Section: Uav Imagery Processingmentioning

confidence: 99%

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

et al. 2022

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Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests.

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Biodiversity Observation for Land and Ecosystem Health (BOLEH): A Robust Method to Evaluate the Management Impacts on the Bundle of Carbon and Biodiversity Ecosystem Services in Tropical Production Forests

Cited by 15 publications

References 36 publications

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Questioning Impact: A Cross-Disciplinary Review of Certification Standards for Sustainability

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

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