Suyamto, Desi; Condro, Aryo Adhi; Prasetyo, Lilik B; Wijayanto, Arif K Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources Conference vol. 556, no. 1, IOP Conf. Ser.: Earth Environ. Sci, 2020. @conference{Suyamto2020,
title = {Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources},
author = {Desi Suyamto and Aryo Adhi Condro and Lilik B Prasetyo and Arif K Wijayanto},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/556/1/012011/pdf},
doi = {10.1088/1755-1315/556/1/012011},
year = {2020},
date = {2020-09-22},
volume = {556},
number = {1},
publisher = {IOP Conf. Ser.: Earth Environ. Sci},
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.},
keywords = {deforestation},
pubstate = {published},
tppubtype = {conference}
}
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. |
Hultera,; Prasetyo, Lilik B; Setiawan, Yudi Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, vol. 10, no. 2, pp. 294-306, 2020, ISSN: 2086-4639. @article{Hultera2020,
title = {Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District},
author = {Hultera and Lilik B Prasetyo and Yudi Setiawan},
url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/29821},
doi = {10.29244/jpsl.10.2.294-306},
issn = {2086-4639},
year = {2020},
date = {2020-07-03},
journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan},
volume = {10},
number = {2},
pages = {294-306},
abstract = {Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha.},
keywords = {deforestation},
pubstate = {published},
tppubtype = {article}
}
Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha. |
