Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia
Article Main
Abstract
Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community’s livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country’s financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017–2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress.
Article Details
Article Details
Keywords
Forest change, NBR, Remote sensing, Satellite imagery, Spectral trajectory
References
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Nerfa, L., Rhemtulla, J. M. & Zerriffi, H. (2020). Forest dependence is more than forest income: Development of a new index of forest product collection and livelihood resources. World Development, 125, 104689. https://doi.org/10.1016/j.worlddev.2019.104689
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Putraditama, A., Kim, Y.-S. & Sánchez Meador, A. J. (2019). Community forest management and forest cover change in Lampung, Indonesia. Forest Policy and Economics, 106, 101976. https://doi.org/10.1016/j.forpol.2019.101976
Rakatama, A. & Pandit, R. (2020). Reviewing social forestry schemes in Indonesia: Opportunities and challenges. Forest Policy and Economics, 111, 102052. https://doi.org/10.1016/j.forpol.2019.102052
Sadono, R., Pujiono, E. & Lestari, L. (2020). Land cover changes and carbon storage before and after community forestry program in Bleberan village, Gunungkidul, Indonesia, 1999–2018. Forest Science and Technology, 16(3), 134–144. https://doi.org/10.1080/21580103.2020.1801523
Santika, T., Meijaard, E., Budiharta, S., Law, E. A., Kusworo, A., Hutabarat, J. A., Indrawan, T. P., Struebig, M., Raharjo, S., Huda, I., Sulhani, Ekaputri, A. D., Trison, S., Stigner, M. & Wilson, K. A. (2017). Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities. Global Environmental Change, 46, 60–71. https://doi.org/10.1016/j.gloenvcha.2017.08.002
Sari, S. P., Feyen, J., Koedam, N. & Van Coillie, F. (2022). Monitoring Trends of Mangrove Disturbance at the Tin Mining Area of Bangka Island Using Landsat Time Series and Landtrendr. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 457–460. https://doi.org/10.1109/IGARSS46834.2022.98 83322
Sarre, A. (Ed.). (2020). Global forest resources assessment, 2020: Main report. Food and Agriculture Organization of the United Nations.
Schmidt, G. L., Jenkerson, C. B., Masek, J., Vermote, E. & Gao, F. (2013). Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description (U.S. Geological Survey Open-File Report 2013–1057; p. 17). U.S. Geological Survey.
Shen, J., Chen, G., Hua, J., Huang, S. & Ma, J. (2022). Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method. Remote Sensing, 14(13), 3238. https://doi.org/10.3390/rs14133238
Strassburg, B. B. N., Rodrigues, A. S. L., Gusti, M., Balmford, A., Fritz, S., Obersteiner, M., Kerry Turner, R. & Brooks, T. M. (2012). Impacts of incentives to reduce emissions from deforestation on global species extinctions. Nature Climate Change, 2(5), 350–355. https://doi.org/10.1038/nclimate1375
Sunderlin, W. D., Angelsen, A., Belcher, B., Burgers, P., Nasi, R., Santoso, L. & Wunder, S. (2005). Livelihoods, forests, and conservation in developing countries: An Overview. World Development, 33(9), 1383–1402. https://doi.org/10.1016/j.worlddev.2004.10.004
Umarhadi, D. A. & Danoedoro, P. (2020). The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area. International Journal on Advanced Science, Engineering and Information Technology, 10(3), 1317. https://doi.org/10.18517/ijaseit.10.3.7739
Umarhadi, D. A., Widyatmanti, W., Kumar, P., Yunus, A. P., Khedher, K. M., Kharrazi, A. & Avtar, R. (2022). Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis. Science of The Total Environment, 816, 151561. https://doi.org/10.1016/j.scitotenv.2021.151561
Vermote, E., Justice, C., Claverie, M. & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008
Widjayanti, R. R. D. (1989). Dinamika Kelompok Tani Hutan dalam Program Perhutanan Sosial (Social Forestry) Studi Kasus di Desa Sukobubuk Pati, Jawa Timur.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., … Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195
Xiao, W., Deng, X., He, T. & Chen, W. (2020). Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China. Remote Sensing, 12(10), 1612. https://doi.org/10.3390/rs12101612
Yang, Y., Erskine, P. D., Lechner, A. M., Mulligan, D., Zhang, S. & Wang, Z. (2018). Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal of Cleaner Production, 178, 353–362. https://doi.org/10.1016/j.jclepro.2018.01.050
Yin, X., Kou, W., Yun, T., Gu, X., Lai, H., Chen, Y., Wu, Z. & Chen, B. (2022). Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm. Forests, 13(12), 2038. https://doi.org/10.3390/f13122038
Yokota, Y., Harada, K., Rohman, Silvi, N. O., Wiyono, Tanaka, M. & Inoue, M. (2014). Contributions of Company-Community Forestry Partnerships (PHBM) to the Livelihoods of Participants in Java, Indonesia: A Case Study in Madiun, East Java. Japan Agricultural Research Quarterly: JARQ, 48(3), 363–377. https://doi.org/10.6090/jarq.48.363
Zhu, L., Liu, X., Wu, L., Tang, Y. & Meng, Y. (2019). Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery. Remote Sensing, 11(10), 1234. https://doi.org/10.3390/rs11101234
Buizer, M., Humphreys, D. & de Jong, W. (2014). Climate change and deforestation: The evolution of an intersecting policy domain. Environmental Science & Policy, 35, 1–11. https://doi.org/10.1016/j.envsci.2013.06.001
Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E. & Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sensing of Environment, 205, 131–140. https://doi.org/10.1016/j.rse.2017.11.015
Cohen, W. B., Yang, Z. & Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation. Remote Sensing of Environment, 114(12), 2911–2924. https://doi.org/10.1016/j.rse.2010.07.010
de Jong, S. M., Shen, Y., de Vries, J., Bijnaar, G., van Maanen, B., Augustinus, P. & Verweij, P. (2021). Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. International Journal of Applied Earth Observation and Geoinformation, 97, 102293. https://doi.org/10.1016/j.jag.2020.102293
Directorate General of Social Forestry and Environmental Partnerships. (2022, October 3). Capaian Perhutanan Sosial Sampai Dengan 1 Oktober 2022. http://pskl.menlhk.go.id/berita/437-capaian-perhutanan-sosial-sampai-dengan-1-oktober-2022.html?showall=&limitstart=
Dlamini, L. Z. D. & Xulu, S. (2019). Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data. Sustainability, 11(24), 6916. https://doi.org/10.3390/su11246916
Fu, B., Lan, F., Xie, S., Liu, M., He, H., Li, Y., Liu, L., Huang, L., Fan, D., Gao, E. & Chen, Z. (2022). Spatio-temporal coupling coordination analysis between marsh vegetation and hydrology change from 1985 to 2019 using LandTrendr algorithm and Google Earth Engine. Ecological Indicators, 137, 108763. https://doi.org/10.1016/j.ecolind.2022.108763
Fujiwara, T., Septiana, R. M., Awang, S. A., Widayanti, W. T., Bariatul, H., Hyakumura, K. & Sato, N. (2012). Changes in local social economy and forest management through the introduction of collaborative forest management (PHBM), and the challenges it poses on equitable partnership: A case study of KPH Pemalang, Central Java, Indonesia. Tropics, 20(4), 115–134. https://doi.org/10.3759/tropics.20.115
García, M. J. L. & Caselles, V. (1991). Mapping burns and natural reforestation using thematic Mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106 049109354290
Gilmour, D. A. (2016). Forty years of community-based forestry: A review of its extent and effectiveness. Food and agriculture organization of the United Nations.
Gullison, R. E., Frumhoff, P. C., Canadell, J. G., Field, C. B., Nepstad, D. C., Hayhoe, K., Avissar, R., Curran, L. M., Friedlingstein, P., Jones, C. D. & Nobre, C. (2007). Tropical Forests and Climate Policy. Science, 316(5827), 985–986. https://doi.org/10.1126/science.1136163
Kennedy, R. E., Yang, Z. & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), 2897–2910. https://doi.org/10.1016/j.rse.2010.07.008
Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W. & Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing, 10(5), 691. https://doi.org/10.3390/rs10050691
Maryudi, A. (2017). Creating New Forest Governance Structure for the 12.7 Million-Promise. Jurnal Ilmu Kehutanan, 11(1), 1–3.
Ministry of Environment and Forestry. (2019). Statistik Lingkungan Hidup dan Kehutanan Tahun 2018. Pusat Data dan Informasi KLHK.
Ministry of Environment and Forestry. (2021). Peraturan Menteri Lingkungan Hidup dan Kehutanan tentang Pengelolaan Perhutanan Sosial (Ministerial Regulation on the Social Forestry Management) No. 9 Year 2021. Ministry of Environment and Forestry Republic of Indonesia.
Ministry of Environment and Forestry. (2022). The State of Indonesia’s Forests 2022. Ministry of Environment and Forestry, Republic of Indonesia.
Mitchard, E. T. A. (2018). The tropical forest carbon cycle and climate change. Nature, 559(7715), 527–534. https://doi.org/10.1038/s41586-018-0300-2
Nawir, A. A. & Rumboko, L. (2007). History and State of Deforestation and Land Degradation. In A. A. Nawir, Murniati, & L. Rumboko (Eds.), Forest rehabilitation in Indonesia: Where to after more than three decades? (pp. 11–32). Center for International Forestry Research.
Nerfa, L., Rhemtulla, J. M. & Zerriffi, H. (2020). Forest dependence is more than forest income: Development of a new index of forest product collection and livelihood resources. World Development, 125, 104689. https://doi.org/10.1016/j.worlddev.2019.104689
Pranoto, D. V. (2020). Analysis of Diversity Sources and Amount of Household Income and Their Inequalities in IPHPS Holder Farmers in Sukobubuk Village, Pati District [Undergraduate Thesis]. Universitas Gadjah Mada.
Putraditama, A., Kim, Y.-S. & Sánchez Meador, A. J. (2019). Community forest management and forest cover change in Lampung, Indonesia. Forest Policy and Economics, 106, 101976. https://doi.org/10.1016/j.forpol.2019.101976
Rakatama, A. & Pandit, R. (2020). Reviewing social forestry schemes in Indonesia: Opportunities and challenges. Forest Policy and Economics, 111, 102052. https://doi.org/10.1016/j.forpol.2019.102052
Sadono, R., Pujiono, E. & Lestari, L. (2020). Land cover changes and carbon storage before and after community forestry program in Bleberan village, Gunungkidul, Indonesia, 1999–2018. Forest Science and Technology, 16(3), 134–144. https://doi.org/10.1080/21580103.2020.1801523
Santika, T., Meijaard, E., Budiharta, S., Law, E. A., Kusworo, A., Hutabarat, J. A., Indrawan, T. P., Struebig, M., Raharjo, S., Huda, I., Sulhani, Ekaputri, A. D., Trison, S., Stigner, M. & Wilson, K. A. (2017). Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities. Global Environmental Change, 46, 60–71. https://doi.org/10.1016/j.gloenvcha.2017.08.002
Sari, S. P., Feyen, J., Koedam, N. & Van Coillie, F. (2022). Monitoring Trends of Mangrove Disturbance at the Tin Mining Area of Bangka Island Using Landsat Time Series and Landtrendr. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 457–460. https://doi.org/10.1109/IGARSS46834.2022.98 83322
Sarre, A. (Ed.). (2020). Global forest resources assessment, 2020: Main report. Food and Agriculture Organization of the United Nations.
Schmidt, G. L., Jenkerson, C. B., Masek, J., Vermote, E. & Gao, F. (2013). Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description (U.S. Geological Survey Open-File Report 2013–1057; p. 17). U.S. Geological Survey.
Shen, J., Chen, G., Hua, J., Huang, S. & Ma, J. (2022). Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method. Remote Sensing, 14(13), 3238. https://doi.org/10.3390/rs14133238
Strassburg, B. B. N., Rodrigues, A. S. L., Gusti, M., Balmford, A., Fritz, S., Obersteiner, M., Kerry Turner, R. & Brooks, T. M. (2012). Impacts of incentives to reduce emissions from deforestation on global species extinctions. Nature Climate Change, 2(5), 350–355. https://doi.org/10.1038/nclimate1375
Sunderlin, W. D., Angelsen, A., Belcher, B., Burgers, P., Nasi, R., Santoso, L. & Wunder, S. (2005). Livelihoods, forests, and conservation in developing countries: An Overview. World Development, 33(9), 1383–1402. https://doi.org/10.1016/j.worlddev.2004.10.004
Umarhadi, D. A. & Danoedoro, P. (2020). The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area. International Journal on Advanced Science, Engineering and Information Technology, 10(3), 1317. https://doi.org/10.18517/ijaseit.10.3.7739
Umarhadi, D. A., Widyatmanti, W., Kumar, P., Yunus, A. P., Khedher, K. M., Kharrazi, A. & Avtar, R. (2022). Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis. Science of The Total Environment, 816, 151561. https://doi.org/10.1016/j.scitotenv.2021.151561
Vermote, E., Justice, C., Claverie, M. & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008
Widjayanti, R. R. D. (1989). Dinamika Kelompok Tani Hutan dalam Program Perhutanan Sosial (Social Forestry) Studi Kasus di Desa Sukobubuk Pati, Jawa Timur.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., … Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195
Xiao, W., Deng, X., He, T. & Chen, W. (2020). Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China. Remote Sensing, 12(10), 1612. https://doi.org/10.3390/rs12101612
Yang, Y., Erskine, P. D., Lechner, A. M., Mulligan, D., Zhang, S. & Wang, Z. (2018). Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal of Cleaner Production, 178, 353–362. https://doi.org/10.1016/j.jclepro.2018.01.050
Yin, X., Kou, W., Yun, T., Gu, X., Lai, H., Chen, Y., Wu, Z. & Chen, B. (2022). Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm. Forests, 13(12), 2038. https://doi.org/10.3390/f13122038
Yokota, Y., Harada, K., Rohman, Silvi, N. O., Wiyono, Tanaka, M. & Inoue, M. (2014). Contributions of Company-Community Forestry Partnerships (PHBM) to the Livelihoods of Participants in Java, Indonesia: A Case Study in Madiun, East Java. Japan Agricultural Research Quarterly: JARQ, 48(3), 363–377. https://doi.org/10.6090/jarq.48.363
Zhu, L., Liu, X., Wu, L., Tang, Y. & Meng, Y. (2019). Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery. Remote Sensing, 11(10), 1234. https://doi.org/10.3390/rs11101234
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How to Cite
Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia. (2023). Journal of Applied and Natural Science, 15(3), 1051-1060. https://doi.org/10.31018/jans.v15i3.4781
