Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar

Abstract

Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10% to 90% were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50%. The appropriate threshold for tropical rainforests was 80%. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.

Characterizing historical transformation trajectories of the forest landscape in Rome's metropolitan area (Italy) for effective planning of sustainability goals

“…According to the FAO forest definition, we set, for each sample point, a square buffer zone of 70 m × 70 m (0.49 ha) with a systematic grid of 5 × 5 points and used them as ground truth references (Figure 4). Within each plot, we identified forest or NF areas by counting the number of points covered by trees, based on a screen photointerpretation of Google Earth very high‐resolution imagery for the year 2010 (Hansen et al, 2013; Lui & Coomes, 2015; Lwin et al, 2019; Potere, 2008; Tilahun, 2015). In each plot, when the tree crowns covered at least three points on the grid (3/25 = tree cover >10%), the sample was classified as forest, otherwise as non‐forest.…”

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“…Now, from statistical mechanics, in the canonical ensemble, if conditions such as a constant number of particles, constant area and constant temperature are satisfied, it is possible to determine the partition function for each system, with which it would be possible to find its average energy, employing the formalism evidenced in [12][13][14][15]. It is known that the partition function is given by Equation (7) .…”

“…The study finds a significant advantage in the calculation performed by IDEAM due to its field validation but notes a deficit in periodicity and information access. Kay Khaing Lwin et al [7] create comprehensive forest cover maps by processing national images made available by GFCD for Myanmar (Burma), producing different maps with thresholds ranging from 10% to 90% and evaluating them on ecological and national scales. Addressing more semantic concerns, C. Bovolo et al [8] question the definition of "forest" and its basic units for accurate preservation and analysis in remote sensing models, the most widely used in the field.…”

Geographical Factors Trump Community Factors in Deforestation Risk in Two Watershed Conservation Forests in Myanmar

“…Forest gain was defined as a change from a non-forest to forest state from 2000-2012. Areas with greater than 30% tree cover were defined as forest per a previous assessment of the accuracies of different tree cover thresholds for forest cover mapping derived from the Global Forest Change dataset in Myanmar [82]. The overlap of forest loss and gain pixels was assessed because forest loss and gain often occurred in the same pixels.…”