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Estimating Productivity and Carbon Stock Using Phonological Indices from Satellite Remote Sensing in Indonesia

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Remote Sensing Application

Part of the book series: New Frontiers in Regional Science: Asian Perspectives ((NFRSASIPER,volume 59))

Abstract

Indonesia has the highest forest density in the world, and the productivity of its forests can potentially be maximized to minimize CO2 emissions. However, due to anthropogenic activities, phenological properties are subject to risk to ensure productivity and carbon exchange in the different forest ecosystems in Indonesia. Early prediction of carbon values could indicate a declining trend of forest quality with reference to vegetation levels. Thus, the purpose of this research is to evaluate forest productivity and carbon stock using phonological properties for different forests. The vegetation phenology was used to assess the level of forest productivity with different classifications to estimate carbon stock in six types of forest in south Sumatra using gross primary productivity (GPP) approaches. The vegetation phonologies were analyzed to develop a system dynamics model under two scenarios: first, a changing trend of normalized difference vegetation index (NDVI), and second, a changing trend of area, considering either increasing or decreasing solar radiation in both scenarios. This system was run through the geographic information system (GIS) environment to develop a database and to simulate results for future predictions. Verification was performed to test the simulation model by comparing the results with the Intergovernmental Panel on Climate Change (IPCC) reference. NDVI showed good correlations with GPP using MODIS MOD13Q1 for convertible production forest (CPF R2 = 0.97), permanent production forest, PPF (R2 = 0.99), limited production forest (LPF, R2 = 0.98), tourism recreation forest (TRF, R2 = 0.95), and wildlife reserve forest (WRF, R2 = 0.95), nature reserve forest (NRF, R2 = 0.99). The explicit differential function was used to estimate net primary productivity (NPP), which was related to the changes in area and productivity over time. Productivity and carbon stock analysis was performed via the proposal of five levels referring to Indonesian forest policy planning, considering resilience classified as high forest productivity (V1), moderate forest productivity (V2), marginal forest productivity (V3), very low forest productivity (N1), and no forest productive (N2). TRF was found to fall below the IPCC levels from 2015 to 2017, and NRF fall below the IPCC standards from 2015 to 2018. Therefore, the satellite-based remote sensing, system dynamics model can be implemented in the Indonesian forest policy system for assessing forest productivity and carbon stocks.

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Acknowledgments

Thanks to Japan Section of the Regional Science Association International to grant the copyright to include this published article, Nety Nurda, Ryozo Noguchi, Tofael Ahamed. Forest Productivity Analysis from NDVI Using Satellite Remote Sensing in South Sumatra of Indonesia, Asia Pacific Journal of Regional Sciences, 4(3), 657–690, https://doi.org/10.1007/s41685-020-00163-7 2020. Some minor modification has been conducted in this book chapter. Furthermore, we would like to thank the University of Tsukuba to support this research to forest productivity and carbon stock analysis from vegetation phenological indices using satellite remote sensing in Indonesia. We also express our sincere thanks to the Indonesian Geospatial Agency, the United States Geological Survey (USGS), and European Space Agency (ESA) for geographical and satellite data information. We also extend our special thanks to the Ministry of Environment and Forestry of Indonesia, South Sumatra Forestry Extension and National Resilience Institute of the Republic of Indonesia.

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Authors and Affiliations

  1. National Resilience Institute of the Republic of Indonesia (LEMHANNAS RI), Jakarta, Indonesia

    Nety Nurda

  2. Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan

    Ryozo Noguchi & Tofael Ahamed

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  1. Nety Nurda
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  2. Ryozo Noguchi
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  3. Tofael Ahamed
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Correspondence to Tofael Ahamed .

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Editors and Affiliations

  1. Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan

    Tofael Ahamed

Appendix (Table 9.3)

Appendix (Table 9.3)

Table 9.3 Productivity (NPP) estimation for the six classified forests
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Nurda, N., Noguchi, R., Ahamed, T. (2022). Estimating Productivity and Carbon Stock Using Phonological Indices from Satellite Remote Sensing in Indonesia. In: Ahamed, T. (eds) Remote Sensing Application. New Frontiers in Regional Science: Asian Perspectives, vol 59. Springer, Singapore. https://doi.org/10.1007/978-981-19-0213-0_9

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