Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources

Abstract

Due to its multiscale impacts, deforestation of tropical rainforests had become a global concern. A number of stakeholders comprising government, research agencies, and NGOs; ranging from local to international levels; have developed their own forest monitoring systems for detecting forest loss. However, discrepancies on deforestation reports from various producers often trigger public debates; which mostly degenerate the productivity of efforts in providing salient, legitimate and credible data on deforestation. Thus, we should reconcile the dispute by acknowledging the deforestation data from all sources. This study assessed the agreement between deforestation maps from various sources. In this case, deforestation maps of Kalimantan within 2009-2013 period from 4 sources were used; i.e. deforestation maps from European Space Agency - Climate Change Initiative (ESA-CCI), Forest Watch Indonesia (FWI), Global Forest Watch (GFW), and Indonesian Ministry of Environment and Forestry (MoEF). We found that the inter-rater agreement between deforestation maps were relatively low, as indicated by Cohen’s kappa (κ), ranging from slight (κ=0.18 between ESA-CCI and GFW) to fair (0.24 ≤ κ ≤ 0.35 for other pairs of sources); due to omission/commission disagreements (47.82% to 87.58%). It suggests that in order to reconcile the dispute, we should remove the omission disagreement by forming the union of deforestation maps. The results from further analyses proved that the union of deforestation maps increased the agreement to moderate (κ=0.44 between union map and FWI) and even substantial (κ=0.79 between union map and GFW). Findings of this study should support the implementation of one map policy.

Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

“…Overall, we demonstrated that the proposed method offers an alternative to conventional approaches to accuracy assessment of deforestation patterns in time and space across differences in resolution, which should apply to various deforestation-monitoring data sets, ranging from near-real-time [13,14] to sub-annual and annual [15,16] monitoring systems, and covering local and national scales [4][5][6] to global scales [2,3,5]. In addition, with regard to the urgent need to take action to mitigate climate change through, among other things, reducing deforestation [32], the method should offer an easily applicable approach to evaluate several available deforestation data sets from various producers to saliently provide the most credible data on deforestation for legitimate guidance on the ground to take immediate action.…”

“…Measurement of the spatial agreement between deforestation maps from various producers is usually carried out based on the point-to-point degree of matching (i.e., with regard to their omission and commission disagreements; see, e.g., [1][2][3][4][5]). Nevertheless, spatial discrepancies due to omission and commission disagreements between deforestation maps from various producers often trigger political debates, regardless of the overall number, the temporal trend, the spatial pattern, or the operational forest definition (threshold) used, which are mostly counterproductive to efforts for providing salient, legitimate, and credible data on deforestation for relevant policy formulation as guidance on the ground [6]. As suggested by [7], when the problem is situated within a political zone, science should be a mediator.…”

“…The method offers an alternative to conventional approaches to accuracy assessment of deforestation patterns in time and space across differences in resolution, which should apply to various deforestation monitoring data sets, ranging from near-real-time [13,14] to sub-annual and annual [15,16] monitoring systems, and covering local and national scales [4][5][6] to global scales [2,3,5]. Beyond the deforestation data sets, another potential application of the method is for validating spatio-temporal patterns of simulation results from land-change models, such as [17][18][19][20].…”