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Multi-temporal remote sensing data and spectral indices analysis for detection tropical rainforest degradation: case study in Kapuas Hulu and Sintang districts, West Kalimantan, Indonesia

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Abstract

According to the forest statistic data, the forest degradation in West Kalimantan has been increasing since 2006. The satellite remote sensing data can provide information more effective and economical input rather than direct field observation that is difficult to access. In this research, we applied a spectral index derived from remote sensing satellite data, the Normalized Difference Fraction Index (NDFI), then compared it with the widely used forest degradation index, the Normalized Burn Ratio (NBR) and the Normalized Difference Vegetation Index (NDVI), in order to have an enhanced detection of forest canopy damage caused by selective logging activities and associated forest fires in West Kalimantan, especially in Kapuas Hulu and Sintang districts. The NDFI was derived from combination of green vegetation, shadow, soil, and non-photosynthetic vegetation (NPV) fractions images from spectral mixture analysis model. The NBR and NDVI were generated from spectral reflectance values of near-infrared, shortwave infrared, and red spectrums. The satellite data used for monitoring forest degradation were Landsat-year 2006–2009 and then continued with 4-year SPOT 2009–2012. The result showed that the forest degradation was detected initially in 2008 up to 2012 in the research area. Spectral indices analysis (NDFI, NBR, NDVI) was tested and verified by ground survey data in 2012. We found that NDFI has higher accuracy (95 %) to classify the degradation forest due to logging and burning activities rather than NBR or NDVI. The forest degradation mapping also conducted using mosaic of Landsat data year 2000–2009 for whole of West Kalimantan province. This method is suitable for a forest degradation monitoring tool in tropical rainforest.

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References

  • Achard F, Hansen MC (2012) Global forest monitoring from earth observation. CRC Press, Boca Raton

    Google Scholar 

  • Adams JB, Smith MO, Gillespie AR (1993) Imaging spectroscopy: interpretation based on spectral mixture analysis. In: Pieters CM, Englert P (eds) Remote geochemical analysis: elements and mineralogical composition. Cambridge Univ. Press, New York, pp 145–166

  • Adams JB, Sabol DE, Kapos V, Almeida Filho R, Robert D, Smith MO, Gillespie AR (1995) Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens Environ 52:137–154

  • Adams JB, Gillespie AR (2006) Remote sensing of landscapes with spectral images: a physical modeling approach. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Asner GP, Keller M, Pereira R Jr, Zweede JC (2002) Remote sensing of selective logging in Amazonia: assessing limitations based on detailed field observations, Landsat ETM + and textural analysis. Remote Sens Environ 80:83–496

    Article  Google Scholar 

  • Bajracharya S (2008) Community carbon forestry: remote sensing of forest carbon and forest degradation in Nepal. Thesis, International Institute for Geo-Information Science and Earth Observation, Enschede

  • Barbosa PM, Pereira JMC, Grégoire JM (1998) Compositing criteria for burned area assessment using multitemporal low resolution satellite data. Remote Sens Environ 65:38–49

    Article  Google Scholar 

  • BPS (2012) Statistics of West Kalimantan

  • Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM + , and EO-1 ALI sensors. Remote Sens Environ 113:893–903

    Article  Google Scholar 

  • Chuvieco E, Ventura G, Pilar Martín M, Gomez I (2005) Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sens Environ 94:450–462

    Article  Google Scholar 

  • Cochrane MA, Alencar A, Schulze M, Souza C Jr, Nepstad D, Lefebvre P, Davidson E (1999) Positive feedbacks in the fire dynamic of closed canopy tropical forest. Science 284:1832–1835

    Article  Google Scholar 

  • Cocke AE, Fule PZ, Crouse JE (2005) Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int J Wildl Fire 14:189–198

    Article  Google Scholar 

  • Conese C, Bonora L (2005) Burned land mapping from remote sensing imagery. Pecora 16 “global priorities in land remote sensing”, 23–27 Oct 2005, Sioux Falls, South Dakota

  • Cox PM, Pearson D, Booth BB, Pierre F, Huntingford C, Jones CD, Luke CM (2013) Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494:341–344. doi:10.1038/nature11882

    Article  Google Scholar 

  • Edwards AJ (1999) Application of satellite and airborne image data to coastal management (seventh computer-based learning module). UNESCO, Paris

    Google Scholar 

  • Escuin S, Navarro R, Fernandez P (2008) Fire severity assessment by using NBR (normalized burn ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. Int J Remote Sens 29:1053–1073

    Article  Google Scholar 

  • Fuller DO, Fulk M (2001) Burned area in Kalimantan, Indonesia mapped with NOAA-AVHRR and Landsat TM imagery. Int J Remote Sens 22:691–697

    Article  Google Scholar 

  • Gerwing JJ (2002) Degradation of forests through logging and fire in theeastern Brazilian Amazon. For Ecol Manag 157(1):131–141

    Article  Google Scholar 

  • Heiskanen J (2006) Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. Int J Remote Sens 27(6):1135–1158

    Article  Google Scholar 

  • Hogg RV, Craig AT (1994) Introduction to mathematical statistic. Prentice Hall PTR, Englewood Cliffs

    Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Article  Google Scholar 

  • Jensen JR (1986) Introductory digital image processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Johns J, Barreto P, Uhl C (1996) Logging damage in planned and unplanned logging operation and its implications for sustainable timber production in the eastern Amazon. For Ecol Manag 89:59–77

    Article  Google Scholar 

  • Key CH, Benson NC (1999) Measuring and remote sensing of burn severity. In: Neuenschwander LF, Ryan KC (eds) Joint fire science conference and workshop, proceedings, vol II. University of Idaho and International Association of Wildland Fire

  • Lanly J (2003) Deforestation and forest degradation factors. In: XII World Forestry

  • Martín MP, Díaz-Delgado R, Chuvieco E, Ventura G (2002) Burned land mapping using NOAA-AVHRR and TERRA-MODIS. In: Viegas (ed) Conference proceedings: forest fire research and wildland fire safety. Millpress, Rotterdam

  • Miettinen J (2007) Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery. Academic dissertation. Department of Forest Resource Management Faculty of Agriculture and Forestry University of Helsinki

  • Miller JD, Yool SR (2002) Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sens Environ 82:481–496

    Article  Google Scholar 

  • Ministry of Forestry (2012) Forestry statistic. Water Catchment Management and Social Forestry Directory, Indonesia

  • Ministry of Forestry (2013) Forestry statistic. Planology Directory, Indonesia

  • Nepstad DC, Veríssimo JA, Alencar A, Nobre C, Lima E, Lefebvre P, Schlesinger P, Potter C, Moutinho P, Mendoza E, Cochrane M, Brooks V (1999) Large-scale impoverishment of Amazonian forests by logging and fire. Int Wkly J Sci Nat 398:504–508

    Google Scholar 

  • Nielsen TT, Mbow C, Kane R (2002) A statistical methodology for burned area estimation using multitemporal AVHRR data. Int J Remote Sens 23:1181–1196

    Article  Google Scholar 

  • Ott L (1992) An introduction to statistical methods and data analysis, 4th edn. California’ Duxbury Press, Belmont, 1051 pp

    Google Scholar 

  • Pinty B, Verstraete MM (1992) GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101:15–20

    Article  Google Scholar 

  • Roberts DA, Batista GT, Pereira JLG, Walker EK, Nelson BW (1998) Change identification using multitemporal Spectral Mixture Analysis: Applications in eastern Amazon. In: Luneetta RS, Elvidge CD (eds) Remote sensing change detection: environmental monitoring methods and applications, vol 9. Ann Arbor Press, Chelsea, pp 137–159

    Google Scholar 

  • Rouse JW, Hass RH, Schell JA, Deering DW (1973) Monitoring vegetation system in Great Plains with ERTS. In: Third earth resources technology satellite-1 symposium, 10–14 Dec 1973, Washington DC, NASA, pp 309–317

  • Roy DP, Giglio L, Kendall JD, Justice CO (1999) Multi-temporal active-fire based burn scar detection algorithm. Int J Remote Sens 20:1031–1038

    Article  Google Scholar 

  • Roy DP, Lewis PE, Justice CO (2002) Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach. Remote Sensing and Environment 83:263–286

    Article  Google Scholar 

  • Sá ACL, Pereira JMC, Vasconcelos MJP, Silva JMN, Ribeiro N, Awasse A (2003) Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of Northern Mozambique using MODIS imagery. Int J Remote Sens 24:1783–1796

    Article  Google Scholar 

  • Shimabukuro YE, Batista GT, Mello EMK, Moreira JC, Duarte V (1998) Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon Region. Int J Remote Sens 19(3):535–541

    Article  Google Scholar 

  • Silva JMN, Cadima JFCL, Pereira JMC, Grégoire JM (2004) Assessing the feasibility of a global model for multitemporal burned area mapping using SPOT-VEGETATION data. Int J Remote Sens 25:4889–4913

    Article  Google Scholar 

  • Sofan P, Zubaidah A, Vetrita Y, Yulianto F, Diah SKA (2011) Remote sensing application for mapping the burnt area. Internal report of National Institute of Aeronautics and Space (LAPAN) (in Bahasa Indonesia)

  • Souza C, Barreto P (2000) An alternative approach for detecting and monitoring selectively logged forests in the Amazon. Int J Remote Sens 21:173–179

    Article  Google Scholar 

  • Souza C Jr, Firestone L, Silva ML, Roberts DA (2003) Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models. Remote Sens Environ 87:494–506

    Article  Google Scholar 

  • Souza C Jr, Roberts DA, Cochrane MA (2005) Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sens Environ 98:329–343

    Article  Google Scholar 

  • Stone TA, Lefebvre PA (1998) Using multi-temporal satellite data to evaluate selective logging in Para, Brazil. Int J Remote Sens 13:2517–2526

    Article  Google Scholar 

  • Stroppiana D, Pinnock S, Pereira JMC, Grégoire JM (2002) Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia. Remote Sens Environ 82:21–37

    Article  Google Scholar 

  • Suwarsono, Yulianto F, Parwati, Suprapto T (2009) The application of MODIS data for identifying burned area based on NDVI changes in Central Kalimantan Province. J Remote Sens Digit Image Process 6:54–64 (in Bahasa Indonesia)

    Google Scholar 

  • Suwarsono, Vetrita Y, Parwati, Khomarudin R (2011) Analysis of burned area in Central Kalimantan in 2009 using Normalized Burned Ratio from Landsat 7 SLC-off. In: Proceeding of geospatial in development of region and city. Annual scientific conference. Indonesian Society for Remote Sensing (ISRS) (in Bahasa Indonesia)

  • Turner II BL, Moss RH, Skole DL (1993) Relating land use and global land-cover change: a proposal for an IGBP–HDP core project. IGBP report no. 24, HDP report no. 5. International Geosphere–Biosphere Programme, Stockholm

  • Veríssimo A, Barreto P, Mattos M, Tarifa R, Uhl C (1992) Logging impacts and prospects for sustainable forest management in an old Amazon frontier: the case of Paragominas. For Ecol Manag 55:169–199

    Article  Google Scholar 

  • Veríssimo A, Barreto P, Tarifa R, Uhl C (1995) Extraction of a high-value natural resource from Amazon: the case of mahogany. For Ecol Manag 72:39–60

    Article  Google Scholar 

  • Verolme HJH, Moussa J (1999) Addressing the underlying causes of deforestation and forest degradation—case studies, analysis and policy recommendations. Biodiversity Action Network, Washington

    Google Scholar 

  • Wu X, Furby SL, Wallace JF (2004) An approach for terrain illumination correction. In: The 12th Australian remote sensing and photogrammetry conference proceedings, Fremantle, WA

Download references

Acknowledgments

This research was conducted in Remote Sensing Applications Center—LAPAN. Thanks go to Mr. Agus Hidayat, M.Sc. (The former Director of Remote Sensing Application Center, LAPAN) and Mr. Dedi Irawadi (The Director of Remote Sensing Application Center, LAPAN) who has supported this research. The author also would like to thanks to Indonesian National Carbon Accounting (INCAS) Project in Technology and Remote Sensing Data Center who support Landsat-7 data and also SPOT-4 data.

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Authors and Affiliations

  1. Remote Sensing Applications Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Kalisari No. 8, Pekayon, Pasar Rebo, Jakarta Timur, 13710, Indonesia

    Parwati Sofan, Yenni Vetrita, Fajar Yulianto & Muhammad Rokhis Khomarudin

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  1. Parwati Sofan
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  2. Yenni Vetrita
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  3. Fajar Yulianto
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  4. Muhammad Rokhis Khomarudin
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Correspondence to Parwati Sofan.

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Sofan, P., Vetrita, Y., Yulianto, F. et al. Multi-temporal remote sensing data and spectral indices analysis for detection tropical rainforest degradation: case study in Kapuas Hulu and Sintang districts, West Kalimantan, Indonesia. Nat Hazards 80, 1279–1301 (2016). https://doi.org/10.1007/s11069-015-2023-0

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