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Monitoring Mangrove Forest Degradation in Mangrove Nature Tourism Park Angke Kapuk, North Jakarta, Indonesia Using NDVI

Yıl 2024, , 29 - 42, 27.06.2024
https://doi.org/10.33904/ejfe.1395676

Öz

Mangrove forests in Angke Kapuk, North Jakarta, are integral parts of the coastal ecosystem and play important roles in supporting environmental sustainability. One component of the Angke Kapuk Mangrove Forest is the Mangrove Nature Tourism Park (MNTP), Angke Kapuk, covering an area of 99.82 hectares. This study aims to analyze mangrove forest degradation in Angke Kapuk Nature Reserve using the Normalized Difference Vegetation Index (NDVI, which allows for mapping mangrove vegetation density and monitoring changes in the vegetation density over time. The objective of this study is to determine the degradation of mangrove forests from 2018 to 2023 using the NDVI derived from Landsat 8 and Landsat 9 satellite imagery. The findings of this study showed a change of 13.16 hectares in forested areas between 2018 and 2023, suggesting forest degradation. Accuracy assessment resulted in 80% overall accuracy with a kappa coefficient of 76.2%. Based on the literature, our results are similar to the acceptable level of accuracy, which is considered to be above 80%. Monitoring mangrove forest areas can serve as a preventive measure to address the issue of mangrove forest degradation. These results underscore the necessity of sustainable forestry monitoring efforts in the MNTP area, as it contributes significantly to providing ecosystem services and maintaining environmental sustainability.

Destekleyen Kurum

This work has been generously supported by Yayasan Mangrove Indonesia Lestari

Teşekkür

The authors would also like to express their gratitude to the Department of Forestry of DKI Jakarta Province for their invaluable support

Kaynakça

  • Abd-El Monsef, H., Smith, S.E. 2017. A new approach for estimating mangrove canopy cover using LANDSAT 8 imagery. Computers and Electronics in Agriculture, 135:183-194. https://doi.org/10.1016/ j.compag.2017.02.007
  • Arifanti, V.B., Sidik, F., Mulyanto, B. 2022. Challenges and Strategies for Sustainable Mangrove Management in Indonesia: A Review. Forest, 13(5): 695. https://doi.org/10.3390/f13050695
  • Annatakarn, K., Fooprateepsiri, R., Suwanprapab, M., Supunyachotsakul, C., Witchayangkoon, B. 2022. Finding threshold for NDVI to classify green area: case study in the central Thailand. Journal of Hunan University Natural Sciences, 49(4). https://doi.org/ 10.55463/issn.1674-2974.49.4.34
  • Bernstein, L.S., Adler-Golden, S.M., Jin, X., Gregor, B., Sundberg, R.L. 2012. Quick atmospheric correction (QUAC) code for VNIR-SWIR spectral imagery: Algorithm details. In 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. http://dx.doi.org /10.1109/WHISPERS.2012.6874311
  • Carugati, L., Gatto, B., Rastelli, E., Martire, M.L., Coral, C., Greto, S., Danovaro, R. 2018. Impact of mangrove forests degradation on biodiversity and ecosystem functioning. Scientific Reports, 8: 13298. https:// doi.org/10.1038/s41598-018-31683-0
  • Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37(1), 35-46.
  • Davis, Z., Nesbitt, L., Guhn, M., van den Bosch, M. 2023. Assessing changes in urban vegetation using Normalised Difference Vegetation Index (NDVI) for epidemiological studies. Urban Forestry and Urban Greening, 88. https://doi.org/10.1016/j.ufug.2023. 128080
  • Department of Forestry of DKI Jakarta Province. 2022. Preparation of forest management plan for the management of the Angke Kapuk Forest Area, North Jakarta, DKI Jakarta Province. Jakarta.
  • de Silva, W., Amarasinghe, M.D. 2023. Coastal protection function of mangrove ecosystems: a case study from Sri Lanka. Journal of Coastal Conservation, 27(6). https://doi.org/10.1007/s11852-023-00990-8
  • Dewi, E. K., Trisakti, B. 2017. Comparing atmospheric correction methods for Landsat OLI data. International Journal of Remote Sensing and Earth Sciences (IJReSES), 13(2):105-120. http://dx.doi.org /10.30536/j.ijreses.2016.v13.a2472
  • Efriyeldi, E., Syahrial, S., Effendi, I., Almanar, I.P., Syakti, A.D. 2023. The mangrove ecosystem in a harbor-impacted city in Dumai, Indonesia: A conservation status. Regional Studies in Marine Science, 65. https://doi.org/10.1016/j.rsma.2023.103 092
  • Ewaldo, K., Karuniasa, M., Takarina, N.D. 2023. Carrying capacity of mangrove ecotourism area in Pantai Indah Kapuk, North Jakarta, Indonesia. Biodiversitas, 24(10): 5808-5819. http://dx.doi.org/ 10.13057/biodiv/d241063
  • FAO [Food and Agriculture Organization of the United Nations]. 2020. Global Forest Resources Assessment 2020 Main Report. In Reforming China’s Healthcare System. Rome. Italy. DOI: 10.4324/9781315184487-1.
  • Farras, H.R.H., Abdurrahman, U., Fadhil, P.I., Nur, A.A. 2022. Mapping of Coastal Mangrove at Mangrove Nature Tourism Park, Angke Kapuk, North Jakarta, Indonesia. Korea-Indonesia Marine Technology Cooperation Research Center. Jakarta
  • Faruque, M.J., Hasan, M.Y., Islam, K.Z., Young, B., Ahmed, M.T., Monir, M.U., Shovon, S.M., Kakon, J.F., Kundu, P. 2022. Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment, 25, 100699. https://doi.org/ 10.1016/j.rsase.2022.100699
  • Fayech, D., Tarhouni, J., 2021. Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area. Modeling Earth Systems and Environment, 7(3): 1667–1682. https://doi.org/10.1007/s40808-020008 96-6
  • Gerard, F.F., George, C.T., Hayman, G., Chavana-Bryant, C., Weedon GP. 2020. Leaf phenology amplitude derived from MODIS NDVI and EVI: Maps of leaf phenology synchrony for Meso- and South America. Geosciences Data Journal, 7:13–26. https://doi.org/10.1002/gdj3.87
  • Giri, C. 2023. Frontiers in Global Mangrove Forest Monitoring. Remote Sensing, 15(15). https://doi.org/10.3390/rs15153852
  • Goldberg, L., Lagomasino, D., Thomas, N., Fatoyinbo, T., 2020. Global declines in human‐driven mangrove loss. Global Change Biology, 26(10), 5844-5855. https://doi.org/10.1111/gcb.15275
  • Guo, Y., Zeng, F. 2012. Atmospheric correction comparison of SPOT-5 image based on model FLAASH and model QUAC. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39: 7-11. https://doi. org/10.5194/isprsarchives-XXXIX-B7-7-2012
  • Hasani, Q., Anisa, A., Damai, A.A., Yuliana, D., Yudha, I.G., Julian, D., 2023. Biodiversitas, 24(7): 3735-3742. https://doi.org/10.13057/biodiv/d240710
  • Iacono, L.E., Pacios, D., Vazquez-Poletti, J.L. 2023. SNDVI: A new scalable serverless framework to compute NDVI. Frontiers in High Performance Computing, 1, 1151530.
  • Jianya, G., Haigang, S., Guorui, M., Qiming, Z. 2008. A review of multi-temporal remote sensing data change detection algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7): 757-762.
  • Kędziorski, P., Kogut, T., Oberski, T. 2023. Impact of radiometric correction on the processing of UAV images. Scientific Journals of the Maritime University of Szczecin, 73 (145): 5-14. DOI: 10.17402/550.
  • Lintz, J., Simonett, D.S. 1976. Sensors for spacecraft. Remote Sensing of Environment, 323-343. https://doi.org/10.1177/030913337900300412
  • Meera, S.P., Bhattacharyya, M. Kumar, A. 2023. Dynamics of mangrove functional traits under osmotic and oxidative stresses. Plant Growth Regul 101, 285–306. https://doi.org/10.1007/s10725-023-01034-9
  • Moravec, D., Komárek, J., Medina, S. L., Molina, I. 2021. Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors. Remote Sensing, 13(8): 3550. https://doi.org/ 10.3390/rs13183550
  • Mayalanda, Y., Yulianda, F., Setyobudiandi, I. 2014. Strategy for mangrove ecosystem rehabilitation throughout damaged level analysis at Muara Angke Wildlife Sanctuary, Jakarta. International Journal of Bonorowo Wetlands, 4(1): 12-36. https://doi.org/ 10.13057/bonorowo/w040102
  • Muchsin, F., Harmoko, A., Prasasti, I., Rahayu, M.I., Fibriawati, L., Pradhono, K.A. 2022. Comparison of The Radiometric Correction Landsat-8 Image Based on Object Spectral Response and Vegetation Index. International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(2): 177-188. http://dx.doi. org/10.30536/j.ijreses.2021.v18.a3632
  • Naser, M.A., Khosla, R., Longchamps, L., Dahal, S. 2020. Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sensing, 12(5): 824. https://doi.org/ 10.3390/rs12050824
  • Pamungkas, B., Kurnia, R., Riani, E., Taryono. 2020. Classification of Mangrove Ecosystem Area in Pantai Bahagia Village, Muara Gembong, Bekasi Regency, Using Sentinel Imagery with Normalized Difference Vegetation Index Method. Journal of Tropical Marine Science and Technology, 12(3):821-831. https://doi. org/10.29244/jitkt.v12i3.32241
  • Paolini, L., Grings, F., Sobrino, J.A., Jiménez Muñoz, J.C., Karszenbaum, H. 2006. Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies. International Journal of Remote Sensing, 27(4):685-704. https://doi.org/10.1080/ 01431160500183057
  • Perri, S., Detto, M., Porporato, A., Molini, A. 2023. Salinity-induced limits to mangrove canopy height. Global Ecology and Biogeography, 32(9):1561–1574. https://doi.org/10.1111/geb.13720
  • Picon, A., Bereciartua-perez, A., Eguskiza, I., Romero-Rodriguez, J., Jimenez-Ruiz, C.J., Eggers, T., Klukas, C., Navarra-Mestre, R. 2022. Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation. Artificial Intelligence in Agriculture, 6: Pages 199-210. https://doi.org/10.1016/j.aiia.2022. 09.004
  • Purnamasari, E., Kamal, M., Wicaksono, P. 2021. Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Regional Studies in Marine Science, 44. https://doi.org/10.1016/j.rsma. 2021.101730
  • Rondon, M., Ewane, B.E., Abdullah, M., Watt, M., Blanton, A., Abulibdeh, A., Mohan, M. 2023. Remote sensing-based assessment of mangrove ecosystems in the Gulf Cooperation Council countries: A systematic review. Frontiers in Marine Science, 10, 1241928. https://doi.org/10.3389/fmars.2023.1241928
  • Sari, N., Patria, M.P., Soesilo, T.E.B., Tejakusuma, I.G. 2019. The Structure of Mangrove Communities in Response to Water Quality in Jakarta Bay, Indonesia. Biodiversitas, 20(7): 1873-1879, DOI: 10.13057/ biodiv/d200712
  • Schaduw, J.N.W. 2019. Community Structure and Canopy Coverage Percentage of Salawati Island Mangroves, Raja Ampat Regency, West Papua Province. Majalah Geografi Indonesia, 33(1): 26-34. https://doi.org/10.22146/mgi.34745
  • Shankar, V.S., Purti, N., Singh, R.P., Khudsar, F.A. 2020. Secondary Ecological Succession of Mangrove in the 2004 Tsunami Created Wetlands of South Andaman, India. In Mangrove Ecosystem Restoration. IntechOpen. DOI: 10.5772/intechopen.94113
  • Sofian, A., Kusmana, C., Fauzi, A., Omo Rusdiana. 2019. Evaluation of Angke Kapuk Jakarta Bay Mangrove Ecosystem and Its Consequences on Ecosystem Services. Jurnal Kelautan Nasional. http://dx.doi.org/10.15578/jkn.v15i1.7722
  • Sokolović, D., Bajric, M., Akay, A.E. 2022. Using GIS-based Network Analysis to Evaluate the Accessible Forest Areas Considering Forest Fires: The Case of Sarajevo. European Journal of Forest Engineering, 8(2):93-99. https://doi.org/10.33904/ejfe.1211687
  • Sraun, M., Bawole, R., Marwa, J., Sinery, A. S., Cabuy, R. L. 2022. Diversity, composition, structure and canopy cover of mangrove trees in six locations along Bintuni riverbank, Bintuni Bay, West Papua, Indonesia. Biodiversitas, 23(11): 5835–5843. https:// doi.org/10.13057/biodiv/d231137
  • Van Anh, B.K. 2023. Evaluation The Changing of Can Gio Vegetation Index By The Sentinel-2 Database From 2015 to 2023.
  • Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., Tucker, C. J. 2015. Use of the Normalized Difference Vegetation Index (NDVI) to assess Land degradation at multiple scales: current status, future trends, and practical considerations. Lund University Centre for Sustainability Studies - LUCSUS. Lund. Sweden.
  • Zhu, J.J., Yan, B. 2022. Blue carbon sink function and carbon neutrality potential of mangroves. Science of the Total Environment, 822: 153438. https://doi.org/ 10.1016/j.scitotenv.2022.153438
Yıl 2024, , 29 - 42, 27.06.2024
https://doi.org/10.33904/ejfe.1395676

Öz

Kaynakça

  • Abd-El Monsef, H., Smith, S.E. 2017. A new approach for estimating mangrove canopy cover using LANDSAT 8 imagery. Computers and Electronics in Agriculture, 135:183-194. https://doi.org/10.1016/ j.compag.2017.02.007
  • Arifanti, V.B., Sidik, F., Mulyanto, B. 2022. Challenges and Strategies for Sustainable Mangrove Management in Indonesia: A Review. Forest, 13(5): 695. https://doi.org/10.3390/f13050695
  • Annatakarn, K., Fooprateepsiri, R., Suwanprapab, M., Supunyachotsakul, C., Witchayangkoon, B. 2022. Finding threshold for NDVI to classify green area: case study in the central Thailand. Journal of Hunan University Natural Sciences, 49(4). https://doi.org/ 10.55463/issn.1674-2974.49.4.34
  • Bernstein, L.S., Adler-Golden, S.M., Jin, X., Gregor, B., Sundberg, R.L. 2012. Quick atmospheric correction (QUAC) code for VNIR-SWIR spectral imagery: Algorithm details. In 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. http://dx.doi.org /10.1109/WHISPERS.2012.6874311
  • Carugati, L., Gatto, B., Rastelli, E., Martire, M.L., Coral, C., Greto, S., Danovaro, R. 2018. Impact of mangrove forests degradation on biodiversity and ecosystem functioning. Scientific Reports, 8: 13298. https:// doi.org/10.1038/s41598-018-31683-0
  • Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37(1), 35-46.
  • Davis, Z., Nesbitt, L., Guhn, M., van den Bosch, M. 2023. Assessing changes in urban vegetation using Normalised Difference Vegetation Index (NDVI) for epidemiological studies. Urban Forestry and Urban Greening, 88. https://doi.org/10.1016/j.ufug.2023. 128080
  • Department of Forestry of DKI Jakarta Province. 2022. Preparation of forest management plan for the management of the Angke Kapuk Forest Area, North Jakarta, DKI Jakarta Province. Jakarta.
  • de Silva, W., Amarasinghe, M.D. 2023. Coastal protection function of mangrove ecosystems: a case study from Sri Lanka. Journal of Coastal Conservation, 27(6). https://doi.org/10.1007/s11852-023-00990-8
  • Dewi, E. K., Trisakti, B. 2017. Comparing atmospheric correction methods for Landsat OLI data. International Journal of Remote Sensing and Earth Sciences (IJReSES), 13(2):105-120. http://dx.doi.org /10.30536/j.ijreses.2016.v13.a2472
  • Efriyeldi, E., Syahrial, S., Effendi, I., Almanar, I.P., Syakti, A.D. 2023. The mangrove ecosystem in a harbor-impacted city in Dumai, Indonesia: A conservation status. Regional Studies in Marine Science, 65. https://doi.org/10.1016/j.rsma.2023.103 092
  • Ewaldo, K., Karuniasa, M., Takarina, N.D. 2023. Carrying capacity of mangrove ecotourism area in Pantai Indah Kapuk, North Jakarta, Indonesia. Biodiversitas, 24(10): 5808-5819. http://dx.doi.org/ 10.13057/biodiv/d241063
  • FAO [Food and Agriculture Organization of the United Nations]. 2020. Global Forest Resources Assessment 2020 Main Report. In Reforming China’s Healthcare System. Rome. Italy. DOI: 10.4324/9781315184487-1.
  • Farras, H.R.H., Abdurrahman, U., Fadhil, P.I., Nur, A.A. 2022. Mapping of Coastal Mangrove at Mangrove Nature Tourism Park, Angke Kapuk, North Jakarta, Indonesia. Korea-Indonesia Marine Technology Cooperation Research Center. Jakarta
  • Faruque, M.J., Hasan, M.Y., Islam, K.Z., Young, B., Ahmed, M.T., Monir, M.U., Shovon, S.M., Kakon, J.F., Kundu, P. 2022. Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment, 25, 100699. https://doi.org/ 10.1016/j.rsase.2022.100699
  • Fayech, D., Tarhouni, J., 2021. Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area. Modeling Earth Systems and Environment, 7(3): 1667–1682. https://doi.org/10.1007/s40808-020008 96-6
  • Gerard, F.F., George, C.T., Hayman, G., Chavana-Bryant, C., Weedon GP. 2020. Leaf phenology amplitude derived from MODIS NDVI and EVI: Maps of leaf phenology synchrony for Meso- and South America. Geosciences Data Journal, 7:13–26. https://doi.org/10.1002/gdj3.87
  • Giri, C. 2023. Frontiers in Global Mangrove Forest Monitoring. Remote Sensing, 15(15). https://doi.org/10.3390/rs15153852
  • Goldberg, L., Lagomasino, D., Thomas, N., Fatoyinbo, T., 2020. Global declines in human‐driven mangrove loss. Global Change Biology, 26(10), 5844-5855. https://doi.org/10.1111/gcb.15275
  • Guo, Y., Zeng, F. 2012. Atmospheric correction comparison of SPOT-5 image based on model FLAASH and model QUAC. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39: 7-11. https://doi. org/10.5194/isprsarchives-XXXIX-B7-7-2012
  • Hasani, Q., Anisa, A., Damai, A.A., Yuliana, D., Yudha, I.G., Julian, D., 2023. Biodiversitas, 24(7): 3735-3742. https://doi.org/10.13057/biodiv/d240710
  • Iacono, L.E., Pacios, D., Vazquez-Poletti, J.L. 2023. SNDVI: A new scalable serverless framework to compute NDVI. Frontiers in High Performance Computing, 1, 1151530.
  • Jianya, G., Haigang, S., Guorui, M., Qiming, Z. 2008. A review of multi-temporal remote sensing data change detection algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7): 757-762.
  • Kędziorski, P., Kogut, T., Oberski, T. 2023. Impact of radiometric correction on the processing of UAV images. Scientific Journals of the Maritime University of Szczecin, 73 (145): 5-14. DOI: 10.17402/550.
  • Lintz, J., Simonett, D.S. 1976. Sensors for spacecraft. Remote Sensing of Environment, 323-343. https://doi.org/10.1177/030913337900300412
  • Meera, S.P., Bhattacharyya, M. Kumar, A. 2023. Dynamics of mangrove functional traits under osmotic and oxidative stresses. Plant Growth Regul 101, 285–306. https://doi.org/10.1007/s10725-023-01034-9
  • Moravec, D., Komárek, J., Medina, S. L., Molina, I. 2021. Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors. Remote Sensing, 13(8): 3550. https://doi.org/ 10.3390/rs13183550
  • Mayalanda, Y., Yulianda, F., Setyobudiandi, I. 2014. Strategy for mangrove ecosystem rehabilitation throughout damaged level analysis at Muara Angke Wildlife Sanctuary, Jakarta. International Journal of Bonorowo Wetlands, 4(1): 12-36. https://doi.org/ 10.13057/bonorowo/w040102
  • Muchsin, F., Harmoko, A., Prasasti, I., Rahayu, M.I., Fibriawati, L., Pradhono, K.A. 2022. Comparison of The Radiometric Correction Landsat-8 Image Based on Object Spectral Response and Vegetation Index. International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(2): 177-188. http://dx.doi. org/10.30536/j.ijreses.2021.v18.a3632
  • Naser, M.A., Khosla, R., Longchamps, L., Dahal, S. 2020. Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sensing, 12(5): 824. https://doi.org/ 10.3390/rs12050824
  • Pamungkas, B., Kurnia, R., Riani, E., Taryono. 2020. Classification of Mangrove Ecosystem Area in Pantai Bahagia Village, Muara Gembong, Bekasi Regency, Using Sentinel Imagery with Normalized Difference Vegetation Index Method. Journal of Tropical Marine Science and Technology, 12(3):821-831. https://doi. org/10.29244/jitkt.v12i3.32241
  • Paolini, L., Grings, F., Sobrino, J.A., Jiménez Muñoz, J.C., Karszenbaum, H. 2006. Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies. International Journal of Remote Sensing, 27(4):685-704. https://doi.org/10.1080/ 01431160500183057
  • Perri, S., Detto, M., Porporato, A., Molini, A. 2023. Salinity-induced limits to mangrove canopy height. Global Ecology and Biogeography, 32(9):1561–1574. https://doi.org/10.1111/geb.13720
  • Picon, A., Bereciartua-perez, A., Eguskiza, I., Romero-Rodriguez, J., Jimenez-Ruiz, C.J., Eggers, T., Klukas, C., Navarra-Mestre, R. 2022. Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation. Artificial Intelligence in Agriculture, 6: Pages 199-210. https://doi.org/10.1016/j.aiia.2022. 09.004
  • Purnamasari, E., Kamal, M., Wicaksono, P. 2021. Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Regional Studies in Marine Science, 44. https://doi.org/10.1016/j.rsma. 2021.101730
  • Rondon, M., Ewane, B.E., Abdullah, M., Watt, M., Blanton, A., Abulibdeh, A., Mohan, M. 2023. Remote sensing-based assessment of mangrove ecosystems in the Gulf Cooperation Council countries: A systematic review. Frontiers in Marine Science, 10, 1241928. https://doi.org/10.3389/fmars.2023.1241928
  • Sari, N., Patria, M.P., Soesilo, T.E.B., Tejakusuma, I.G. 2019. The Structure of Mangrove Communities in Response to Water Quality in Jakarta Bay, Indonesia. Biodiversitas, 20(7): 1873-1879, DOI: 10.13057/ biodiv/d200712
  • Schaduw, J.N.W. 2019. Community Structure and Canopy Coverage Percentage of Salawati Island Mangroves, Raja Ampat Regency, West Papua Province. Majalah Geografi Indonesia, 33(1): 26-34. https://doi.org/10.22146/mgi.34745
  • Shankar, V.S., Purti, N., Singh, R.P., Khudsar, F.A. 2020. Secondary Ecological Succession of Mangrove in the 2004 Tsunami Created Wetlands of South Andaman, India. In Mangrove Ecosystem Restoration. IntechOpen. DOI: 10.5772/intechopen.94113
  • Sofian, A., Kusmana, C., Fauzi, A., Omo Rusdiana. 2019. Evaluation of Angke Kapuk Jakarta Bay Mangrove Ecosystem and Its Consequences on Ecosystem Services. Jurnal Kelautan Nasional. http://dx.doi.org/10.15578/jkn.v15i1.7722
  • Sokolović, D., Bajric, M., Akay, A.E. 2022. Using GIS-based Network Analysis to Evaluate the Accessible Forest Areas Considering Forest Fires: The Case of Sarajevo. European Journal of Forest Engineering, 8(2):93-99. https://doi.org/10.33904/ejfe.1211687
  • Sraun, M., Bawole, R., Marwa, J., Sinery, A. S., Cabuy, R. L. 2022. Diversity, composition, structure and canopy cover of mangrove trees in six locations along Bintuni riverbank, Bintuni Bay, West Papua, Indonesia. Biodiversitas, 23(11): 5835–5843. https:// doi.org/10.13057/biodiv/d231137
  • Van Anh, B.K. 2023. Evaluation The Changing of Can Gio Vegetation Index By The Sentinel-2 Database From 2015 to 2023.
  • Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., Tucker, C. J. 2015. Use of the Normalized Difference Vegetation Index (NDVI) to assess Land degradation at multiple scales: current status, future trends, and practical considerations. Lund University Centre for Sustainability Studies - LUCSUS. Lund. Sweden.
  • Zhu, J.J., Yan, B. 2022. Blue carbon sink function and carbon neutrality potential of mangroves. Science of the Total Environment, 822: 153438. https://doi.org/ 10.1016/j.scitotenv.2022.153438
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ormancılık (Diğer)
Bölüm Research Articles
Yazarlar

Tsaniya Nurafifah Suryana 0000-0003-3164-7467

Sherlina Purnamasari 0009-0005-8843-1810

Kevin Ewaldo 0009-0006-7826-7188

Erken Görünüm Tarihi 5 Haziran 2024
Yayımlanma Tarihi 27 Haziran 2024
Gönderilme Tarihi 24 Kasım 2023
Kabul Tarihi 26 Şubat 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Nurafifah Suryana, T., Purnamasari, S., & Ewaldo, K. (2024). Monitoring Mangrove Forest Degradation in Mangrove Nature Tourism Park Angke Kapuk, North Jakarta, Indonesia Using NDVI. European Journal of Forest Engineering, 10(1), 29-42. https://doi.org/10.33904/ejfe.1395676

Creative Commons License

The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.