A hybrid approach of remote sensing for mapping vegetation biodiversity in a tropical rainforest
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Abstract. Wardhana W, Widyatmanti W, Soraya E, Soeprijadi D, Larasati B, Umarhadi DA, Hutomo YHT, Idris F, Wirabuana PYAP. 2020. A hybrid approach of remote sensing for mapping vegetation biodiversity in a tropical rainforest. Biodiversitas 21: 3946-3953. Vegetation biodiversity is one of the most important indicators to evaluate the sustainability of tropical rainforest. It is commonly described by three essential variables, i.e. richness, heterogeneity, and evenness. That information is frequently collected from periodic forest inventory using terrestrial method. However, this effort needs a long-time consuming, high cost, and almost impossible to implement in the area of tropical rainforest with hard accessibility. This study investigates the potential of remote sensing as an alternative method for mapping vegetation biodiversity in a tropical rainforest. A hybrid approach of remote sensing using medium and high-resolution images was developed to recognize the attributes of vegetation biodiversity by considering three parameters derived from remote sensing data, including canopy density (C), crown diameter (D), and tree density (N). The use of a medium resolution image aimed to categorize vegetation density using Modified Soil-Adjusted Value Index (MSAVI) while a high-resolution image was utilized to acquire a more detailed spectrum for determining C, D, and N in every class of vegetation density. The relationship between C, D, N, and richness, heterogeneity, evenness was explained using hierarchical cluster analysis. Our study discovered the attributes of vegetation biodiversity in a tropical rainforest could be potentially recognized by combining C, D, and N as predictor variables.
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References
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Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1): 2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Castillo-Santiago MA, Ricker M, De Jong BHJ. 2010. Estimation of tropical forest structure from spot-5 satellite images. International Journal of Remote Sensing 31(10): 2767–2782. https://doi.org/10.1080/01431160903095460
Dash J, Pont D, Watt MS, Dash J, Pont D, Brownlie R, … Pearse G. 2016. Remote sensing for precision forestry. New Zealand Journal of Forestry 60(4): 15–24.
Du L, Zhou T, Zou Z, Zhao X, Huang K, Wu H. 2014. Mapping forest biomass using remote sensing and national forest inventory in China. Forests 5(6): 1267–1283. https://doi.org/10.3390/f5061267
Fadaei H, Sakai T, Yoshimura T, Kazuyuki M. 2010. Estimation of tree density with high-resolution imagery in the zarbin forest of North Iran (Cupressus sempervirence var. horzontalis). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38(8): 679–684.
Ghahramany L, Fatehi P, Ghazanfari H. 2012. Estimation of Basal Area in West Oak Forests of Iran Using Remote Sensing Imagery. International Journal of Geosciences 3(2): 398–403. https://doi.org/10.4236/ijg.2012.32044
Häme T, Kilpi J, Ahola HA, Rauste Y, Antropov O, Rautiainen M, … Bounpone S. 2013. Improved mapping of tropical forests with optical and sar imagery, part i: Forest cover and accuracy assessment using multi-resolution data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(1): 74–91. https://doi.org/10.1109/JSTARS.2013.2241019
Jusoff K, Ibrahim K. 2009. Hyperspectral remote sensing for tropical rain forest. American Journal of Applied Sciences 6(12): 2001–2005. https://doi.org/10.3844/ajassp.2009.2001.2005
Khaine I, Woo SY, Kwak M, Lee SH, Je SM, You H, … Kim J. 2018. Factors affecting natural regeneration of tropical forests across a precipitation gradient in Myanmar. Forests 9(3): 1–17. https://doi.org/10.3390/f9030143
Kim SR, Lee WK, Kwak DA, Biging GS, Gong P, Lee JH, Cho HK. 2011. Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors 11(2): 1943–1958. https://doi.org/10.3390/s110201943
Li G, Lu D, Moran E, Hetrick S. 2011. Land-cover classification in moist tropical region Brazil with Landsat TM imagery. International Journal of Remote Sensing 32(23): 8207–8230. https://doi.org/10.1080/01431161.2010.532831.
Liu C, Xing Y, Duanmu J, Tian X. 2018. Evaluating different methods for estimating diameter at breast height from terrestrial laser scanning. Remote Sensing 10(4): 1–20. https://doi.org/10.3390/rs10040513
Liu S, Wei X, Li D, Lu D. 2017. Examining forest disturbance and recovery in the subtropical forest region of Zhejiang Province using landsat time-series data. Remote Sensing 9(5): 1–16. https://doi.org/10.3390/rs9050479
Mauya EW, Hansen EH, Gobakken T, Bollandsås OM, Malimbwi RE, Næsset E. 2015. Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance and Management 10(1): 1–14. https://doi.org/10.1186/s13021-015-0021-x
Meng J, Li S, Wang W, Liu Q, Xie S, Ma W. 2016. Mapping forest health using spectral and textural information extracted from SPOT-5 satellite images. Remote Sensing 8(9): 1–20. https://doi.org/10.3390/rs8090719
Miettinen J, Stibig HJ, Achard F. 2014. Remote sensing of forest degradation in Southeast Asia-Aiming for a regional view through 5-30 m satellite data. Global Ecology and Conservation 2: 24–36. https://doi.org/10.1016/j.gecco.2014.07.007
Naidu MT, Kumar OA. 2016. Tree diversity, stand structure, and community composition of tropical forests in Eastern Ghats of Andhra Pradesh, India. Journal of Asia-Pacific Biodiversity 9(3): 328–334. https://doi.org/10.1016/j.japb.2016.03.019
Noorian N, Shataee-Jouibary S, Mohammadi J. 2016. Assessment of different remote sensing data for forest structural attributes estimation in the Hyrcanian forests. Forest Systems 25(3): 1-11. https://doi.org/10.5424/fs/2016253-08682
Pocock MJO, Newson SE, Henderson IG, Peyton J, Sutherland WJ, Noble DG, … Roy DB. 2015. Developing and enhancing biodiversity monitoring programmes: A collaborative assessment of priorities. Journal of Applied Ecology 52(3): 686–695. https://doi.org/10.1111/1365-2664.12423
Sibona E, Vitali A, Meloni F, Caffo L, Dotta A, Lingua E, … Garbarino M. 2017. Direct measurement of tree height provides different results on the assessment of LiDAR accuracy. Forests 8(1): 1–12. https://doi.org/10.3390/f8010007
Wagner FH, Ferreira MP, Sanchez A, Hirye MCM, Zortea M, Gloor E, … Aragão LEOC. 2018. Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing 145(9): 362–377. https://doi.org/10.1016/j.isprsjprs.2018.09.013
Xie Z, Chen Y, Lu D, Li G, Chen E. 2019. Classification of land cover, forest, and tree species classes with Ziyuan-3 multispectral and stereo data. Remote Sensing 11(2): 1–27. https://doi.org/10.3390/rs11020164
Zhang S, Chen H, Fu Y, Niu H, Yang Y, Zhang B. 2019. Fractional vegetation cover estimation of different vegetation types in the Qaidam Basin. Sustainability 11(3): 1–17. https://doi.org/10.3390/su11030864
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