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Detecting Vegetation Regrowth After Fires in Small Watershed Settings Using Remotely Sensed Data and Local Community Participation Approach

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Vegetation Fires and Pollution in Asia

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Abstract

The purpose of this paper is to present an operational framework to detect vegetation regrowth after fires and implement alternative income options in small watershed settings. The framework combined remotely sensed (RS) data sets to provide spatial and temporal resolution observations and local community participation (PAR) process to detect vegetation regrowth and generate income from alternative options. THEOS satellite image was spatially analyzed with the K-Nearest Neighbor classification method to identify various land use types, including growing maize areas in the highlands, in 2016. The imagery data from the Sentinel-2 satellite, integrated with unmanned aerial vehicle (UAV), were also spatially explored to classify the land use types in 2019 with the techniques of K-Nearest Neighbor classification and visual interpretation to monitor the regrowth of vegetation on highlands. The classification accuracy was assessed by sampling coordinated positions for various land use types in 2016 and 2019, with overall accuracies and kappa statistics of 93.7%, 0.924, and 94.8%, 0.938, respectively. Thirty farmers implemented selected alternative income options to increase vegetation regrowth and eliminated all hotspots in the highlands. Compared to income from maize production in the highland, all options have significantly increased the average farm incomes from 1159 to 2368 USD per farm per year in the community areas. Our operational framework could be practically implemented in other small watersheds in Thailand and Mekong River Basin (MRB).

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References

  • Amarasingam, N., A.S. Ashan Salgadoe, K. Powell, L.F. Gonzalez, and S. Natarajan. 2022. A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment 26: 100712. https://doi.org/10.1016/j.rsase.2022.100712.

    Article  Google Scholar 

  • Ayele, G.T., A.K. Tebeje, S.S. Demissie, M.A. Belete, M.A. Jemberrie, W.M. Teshome, D.T. Mengistu, and E.Z. Teshale. 2018. Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research 11: 1–18. https://doi.org/10.1177/1178622117751603.

    Article  Google Scholar 

  • Biswas, S., K.D. Lasko, and K.P. Vadrevu. 2015a. Fire disturbance in tropical forests of Myanmar—Analysis using MODIS satellite datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (5): 2273–2281.

    Article  Google Scholar 

  • Biswas, S., K.P. Vadrevu, Z.M. Lwin, K. Lasko, and C.O. Justice. 2015b. Factors controlling vegetation fires in protected and non-protected areas of Myanmar. PLoS ONE 10 (4): e0124346.

    Article  Google Scholar 

  • Center of Disaster Prevention and Mitigation of Thailand. 2020. Protecting and resolving forest fires, smog and particulate matters (PM2.5) in 2020–2021, 12. Department of Disaster Prevention and Mitigation, Ministry of Interior, Krung Thep Maha Nakhon (Bangkok), Thailand (in Thai).

    Google Scholar 

  • Chen, X. 2002. Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development. International Journal of Remote Sensing 23 (1): 107–124. https://doi.org/10.1080/01431160010007051.

    Article  Google Scholar 

  • Chiang Mai Agricultural Extension Office. 2022. The report of crop production data; Estimating agricultural residues, 13. Chiang Mai, Thailand (in Thai).

    Google Scholar 

  • Congalton, R.G., and K. Green. 2009. Assessing the accuracy of remotely sensed data; Principles and practices, 2nd ed., 105–110. Boca Raton: CRC Press; Taylor & Francis Group.

    Google Scholar 

  • Creighton, J.L. 2005. The public participation handbook: Making better decisions through citizen involvement, 102–137. Jossey-Bass, A Wiley Imprint.

    Google Scholar 

  • DJI. 2019. Phantom 4 Pro; Specs. Shenzhen DJI Sciences and Technologies Ltd. Available at: https://doi.org/10.1579/0044-7447(2008)37[219:pganpi]2.0.co;2.

  • ESA. 2019. Sentinel products. The European Space Agency (ESA); Sentinel Online. Available at: https://sentinel.esa.int/web/sentinel/sentinel-data-access/sentinel-products. Accessed: October 30, 2019.

  • Eskandari, S., and A. Moradi. 2020. Mapping the land uses and analysing the landscape elements in south-western Iran: Application of Landsat-7 field data and landscape metrics. International Journal of Conservations Science 11 (2): 557–564.

    Google Scholar 

  • ESRI. 2022. Change detection in ArcGIS Pro. ArcGIS Pro; Resources. Available at: https://pro.arcgis.com/en/pro-app/2.8/help/analysis/image-analyst/change-detection-in-arcgis-pro.htm#ESRI_SECTION1_9FF9489173C741DD95472F21B5AD8374. Accessed: March 1, 2022.

  • FAO. 1995. Forest resources assessment 1990, Global Synthesis, Food and Agriculture Organization of the United Nations, Rome.

    Google Scholar 

  • GISTDA, 2016. THEOS satellite image; our service; Satellite—Thaichote (THEOS). Data Service Center, Geo-Informatics and Space Technology Development Agency (GISTDA), Krung Thep Maha Nakhon (Bangkok), Thailand. Available at: https://www2.gistda.or.th/main/th/satellite. Accessed: January 28, 2022.

  • Greenpeace Southeast Asia. 2020. Maize, land use change, and transboundary haze pollution, 16. Available at: https://www.greenpeace.org/southeastasia/publication/4117/maize-land-use-change-and-transboundary-haze-pollution/. Accessed: January 28, 2022.

  • Harrison, O. 2018. Machine learning basics with the K-nearest neighbors algorithm. Available at: https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761. Accessed: January 31, 2022.

  • Hognogi, G.-G., A.-M. Pop, A.-C. Marian-Potra, and T. SomeÈ™fălean. 2021. The role of UAS–GIS in digital era governance. A systematic literature review. Sustainability 13(19): 11097. https://doi.org/10.3390/su131911097.

  • https://www.dji.com/phantom-4-pro/info#specs. Accessed: January 28, 2022.

  • https://www.fao.org/3/x5669e/x5669e00.htm#Contents. Accessed: February 26, 2022.

  • Hu, Y., Batunacun, L. Zhen, and D. Zhuang. 2019. Assessment of land-use and land-cover change in Guangxi. Scientific Reports 9(1), 2189. https://doi.org/10.1038/s41598-019-38487-w.

  • Huang, D., S. Xu, J. Sun, S. Liang, W. Song, and Z. Wang. 2017. Accuracy assessment model for classification result of remote sensing image based on spatial sampling. Journal of Applied Remote Sensing 11 (4): 48–59.

    Article  Google Scholar 

  • Jeon, S.B., P. Olofsson, and C.E. Woodcock. 2013. Land use change in New England: A reversal of the forest transition. Journal of Land Use Science 9 (1): 105–130. https://doi.org/10.1080/1747423X.2012.754962.

    Article  Google Scholar 

  • Jiang, W., L. Liu, H. Xiao, S. Zhu, W. Li, and Y. Liu. 2021. Composition and distribution of vegetation in the water level fluctuating zone of the Lantsang cascade reservoir system using UAV multispectral imagery. PLoS ONE 16 (3): e0247682. https://doi.org/10.1371/journal.pone.0247682.

    Article  Google Scholar 

  • Justice, C., C. Ivan, B. Luigi, K. Stefania, S. Wilfrid, G. Louis, P.V. Krishna, and R. David. 2013. Satellite monitoring and inventory of global vegetation fire. In Vegetation fires and global change, 261–276. A Publication of the Global Fire Monitoring Center (GFMC). Remagen: Kessel Publishing.

    Google Scholar 

  • Justice, C., G. Gutman, and K.P. Vadrevu. 2015. NASA land cover and land use change (LCLUC): An interdisciplinary research program. Journal of Environmental Management 148: 4–9.

    Article  Google Scholar 

  • Kim, C. 2016. Land use classification and land use change analysis using satellite images in Lombok Island, Indonesia. Forest Science and Technology 12 (4): 183–191. https://doi.org/10.1080/21580103.2016.1147498.

    Article  Google Scholar 

  • Krishna, K.R. 2018. Agricultural drones; A peaceful pursuit, 1–91. Waretown, New Jersey, USA: Apple Academic Press, Inc.

    Google Scholar 

  • Lasko, K., K.P. Vadrevu, V.T. Tran, E. Ellicott, T.T. Nguyen, H.Q. Bui, and C. Justice. 2017. Satellites may underestimate rice residue and associated burning emissions in Vietnam. Environmental Research Letters 12 (8): 085006.

    Article  Google Scholar 

  • Paul, B.K., C.A. Epper, D.J. Tschopp, C.T.M. Long, V. Tungani, D. Burra, L. Hok, P. Phengsavanh, and S. Douxchamps. 2022. Crop-livestock integration provides opportunities to mitigate environmental trade-offs in transitioning smallholder agricultural systems of the Greater Mekong Subregion. Agricultural Systems 195: 103285.

    Article  Google Scholar 

  • Pech, S., and K. Sunada. 2008. Population growth and natural-resources pressures in the Mekong River Basin. Ambio 37 (3): 219–224.

    Article  Google Scholar 

  • Phompila, C., V. Sihapanya, B. Chanthavong, S. Thongmanivong, P. Van Khoa, K. Noda, and K.P. Vadrevu. 2022. Agricultural use/cover changes in the Vientiane. In Remote sensing of agriculture and land cover/land use changes in South and Southeast Asian countries, 573–586. Cham: Springer.

    Google Scholar 

  • Prasad, V.K., and K.V.S. Badarinth. 2004. Land use changes and trends in human appropriation of above ground net primary production (HANPP) in India (1961–98). Geographical Journal 170 (1): 51–63.

    Article  Google Scholar 

  • Prem, N.S., and P.W. Mohan. 1996. Case studies of people's participation in watershed management in Asia; Part I: Nepal, China and India. UNDP/FAO/Netherlands.

    Google Scholar 

  • Rwanga, S.S., and J.M. Ndambuki. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences 4 (8): 611–622.

    Article  Google Scholar 

  • Sivongxay, A., R. Greiner, and S.T. Garnett. 2017. Livelihood impacts of hydropower projects on downstream communities in central Laos and mitigation measures. Water Resources and Rural Development 9: 46–55.

    Article  Google Scholar 

  • Srivastava, T. 2018. Introduction to k-nearest neighbors: A powerful machine learning algorithm (with implementation in Python & R). Available at: https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/. Accessed: January 31, 2022.

  • Temudo, M.P., J.M.C. Pereira, and D. Oom. 2020. Bio-cultural fire regions of Guinea-Bissau: Analysis combining social research and satellite remote sensing. Applied Geography 118. https://doi.org/10.1016/j.apgeog.2020.102203.

  • Tewabe, D., and T. Fentahun. 2020. Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science 6 (1): 1–11. https://doi.org/10.1080/23311843.2020.1778998.

    Article  Google Scholar 

  • Thapa, P., K.P. Vadrevu, A. Eaturu, and S. Biswas. 2021. Vegetation fire status and management in Bhutan. In Biomass burning in South and Southeast Asia, 135–157. CRC Press.

    Google Scholar 

  • Vadrevu, K., A. Heinimann, G. Gutman, and C. Justice. 2019a. Remote sensing of land use/cover changes in South and Southeast Asian Countries. International Journal of Digital Earth 12 (10): 1099–1102.

    Article  Google Scholar 

  • Vadrevu, K.P., V.K. Dadhwal, G. Gutman, and C. Justice. 2019b. Remote sensing of agriculture–South/Southeast Asia research initiative special issue. International Journal of Remote Sensing 40 (21): 8071–8075.

    Article  Google Scholar 

  • Vadrevu, K.P., A. Eaturu, S. Biswas, and C. Phompila. 2021a. An assessment of burnt area signal variations in Laos using Sentinel-1A&B datasets. In Biomass burning in South and Southeast Asia, 61–76. CRC Press.

    Google Scholar 

  • Vadrevu, K.P., A. Eaturu, and S. Biswas. 2021b. Burnt area mapping in Nainital, Uttarakhand, India, using very high-resolution PlanetScope imagery. In Biomass burning in South and Southeast Asia, 91–103. CRC Press.

    Google Scholar 

  • Vadrevu, K.P., T. Ohara, and C. Justice. 2021c. Biomass burning in South/Southeast Asia–Needs and priorities. In Biomass burning in South and Southeast Asia, 1–21. CRC Press.

    Google Scholar 

  • Vadrevu, K.P., T. Ohara, and C. Justice, eds. 2021d. Biomass burning in South and Southeast Asia: Impacts on the biosphere, vol. 2. CRC Press.

    Google Scholar 

  • Vadrevu, K.P., T. Ohara, and C. Justice, eds. 2021e. Biomass burning in South and Southeast Asia: Mapping and monitoring, vol. 1. CRC Press.

    Google Scholar 

  • Vadrevu, K.P., T. Le Toan, S.S. Ray, and C. Justice. 2022. Remote sensing of agriculture and land cover/land use changes in South and Southeast Asian Countries. Cham: Springer. https://doi.org/10.1007/978-3-030-92365-5.

  • Zhang, B., Q. Zhang, C. Feng, Q. Feng, and S. Zhang. 2017. Understanding land use and land cover dynamics from 1976 to 2014 in Yellow River Delta. Land 6 (1): 20. https://doi.org/10.3390/land6010020.

    Article  Google Scholar 

  • Zhou, Q., B. Li, and Y. Chen. 2011. Remote sensing change detection and process analysis of long-term land use change and human impacts. Ambio 40 (7): 807–818. https://doi.org/10.1007/s13280-011-0157-1.

    Article  Google Scholar 

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Acknowledgements

We appreciate the financial support from the Thailand Science Research and Innovation (TSRI), formerly Thailand Research Fund (TRF), during 2016–2019. We are very grateful to the spirit of learning and kindness of giving by 30 collaborative farmers, in particularly Mr. Natchaphol Phomkham (Kame), the leader of the farmer group during the project period.

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Authors and Affiliations

  1. Department of Highland Agriculture and Natural Resources, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

    Thaworn Onpraphai

  2. Center for Agricultural Resources System Research, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

    Attachai Jintrawet, Angkana Somsak & Suprapat Khuenjai

  3. Highland Research and Training Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

    Pong Loungmoon

  4. Upland Agriculture Research Center, National Agriculture and Forestry Research Institution, Luang Prabang, 0600, Laos

    Bounthanh Keoboualapha

  5. Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China

    Attachai Jintrawet & Jun Fan

Authors
  1. Thaworn Onpraphai
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Correspondence to Attachai Jintrawet .

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Editors and Affiliations

  1. NASA Marshall Space Flight Center, Huntsville, AL, USA

    Krishna Prasad Vadrevu

  2. Center for Environmental Science in Saitama, Kazo, Saitama, Japan

    Toshimasa Ohara

  3. University of Maryland, College Park, MD, USA

    Chris Justice

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Onpraphai, T. et al. (2023). Detecting Vegetation Regrowth After Fires in Small Watershed Settings Using Remotely Sensed Data and Local Community Participation Approach. In: Vadrevu, K.P., Ohara, T., Justice, C. (eds) Vegetation Fires and Pollution in Asia. Springer, Cham. https://doi.org/10.1007/978-3-031-29916-2_10

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