A review of remote sensing applications in tropical forestry with a particular emphasis in the plantation sector

References

• Abdel-Rahman EM, Mutanga O, Adam E, Ismail R. 2014. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS J Photogramm Remote Sens. 88:48–59.
   Web of Science ®Google Scholar
• Adam EMI, Mutanga O. 2012. Estimation of high density wetland biomass: combining regression model with vegetation index developed from Worldview-2 imagery. Proceedings Volume 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV; 85310V; Edinburgh, United Kingdom.
   Google Scholar
• Adelabu S, Mutanga O, Adam E, Cho MA. 2013. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. J Appl Remote Sens. 7(1):073480.
   Web of Science ®Google Scholar
• Agustin S, Devi PAR, Sutaji D, Fahriani N. 2016. Oil palm age classification on satellite imagery using fractal-based combination. J Theor Appl Inform Technol. 89(1):18–26.
   Google Scholar
• Ahern FJ, Leckie DJ, Drieman JA. 1993. Seasonal changes in relative c-band backscatter of northern forest cover types. IEEE Trans Geosci Remote Sens. 31(3):668–680.
   Web of Science ®Google Scholar
• Aholoukpè H, Dubos B, Flori A, Deleporte P, Amadji G, Chotte JL, Blavet D. 2013. Estimating aboveground biomass of oil palm: allometric equations for estimating frond biomass. Forest Ecol Manag. 292(Suppl C):122–129.
   Google Scholar
• Ainsworth TL, Kelly JP, Lee JS. 2009. Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery. ISPRS J Photogramm Remote Sens. 64(5):464–471.
   Web of Science ®Google Scholar
• Akar Ö, Güngör O. 2015. Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey. Int J Remote Sens. 36(2):442–464.
   Web of Science ®Google Scholar
• Allen MG, II, Burkhart HE. 2015. A comparison of alternative data sources for modeling site index in loblolly pine plantations. Can J Forest Res. 45(8):1026–1033.
   Google Scholar
• Almeida-Filho R, Rosenqvist A, Shimabukuro YE, Dos Santos JR. 2005. Evaluation and perspectives of using multitemporal L-band SAR data to monitor deforestation in the Brazilian Amazônia. IEEE Geosci Remote Sens Lett. 2(4):409–412.
   Web of Science ®Google Scholar
• Ambastha KR, Jha CS. 2010. Geospatial analysis of Tamil Nadu Eastern Ghats forest types at landscape level with reference to fragmentation and species diversity. J Ind Soc Remote Sens. 38(3):453–463.
   Web of Science ®Google Scholar
• Amler E, Schmidt M, Menz G. 2015. Definitions and mapping of East African Wetlands: a review. Remote Sens. 7(5):5256–5282.
   Google Scholar
• Arnett JTTR, Coops NC, Daniels LD, Falls RW. 2015. Detecting forest damage after a low-severity fire using remote sensing at multiple scales. Int J Appl Earth Observ Geoinform. 35(PB):239–246.
   Google Scholar
• Arroyo-Mora JP, Sánchez-Azofeifa GA, Kalacska MER, Rivard B, Calvo-Alvarado JC, Janzen DH. 2005. Secondary forest detection in a neotropical dry forest landscape using Landsat 7 ETM + and IKONOS imagery. Biotropica. 37(4):497–507.
   Web of Science ®Google Scholar
• Atkinson JT, Ismail R, Robertson M. 2014. Mapping Bugweed (Solanum mauritianum) infestations in Pinus patula plantations using hyperspectral imagery and support vector machines. IEEE J Sel Top Appl Earth Observ Remote Sens. 7(1):17–28.
   Web of Science ®Google Scholar
• Avtar R, Ishii R, Kobayashi H, Fadaei H, Suzuki R, Herath S. 2013. Efficiency of multi-frequency, multi-polarized SAR data to monitor growth stages of oilpalm plants in Sarawak, Malaysia. Conference Paper presented at: International Geoscience and Remote Sensing Symposium (IGARSS).
   Google Scholar
• Avtar R, Takeuchi W, Sawada H. 2011. Assessment of cashew and rubber plants biophysical parameters based on ALOS/PALSAR data. Seisan Kenkyu. 63(4):443–446.
   Google Scholar
• Baghdadi N, Le Maire G, Bailly JS, Osé K, Nouvellon Y, Zribi M, Lemos C, Hakamada R. 2015. Evaluation of ALOS/PALSAR L-band data for the estimation of Eucalyptus plantations aboveground biomass in Brazil. IEEE J Sel Top Appl Earth Observ Remote Sens. 8(8):3802–3811.
   Web of Science ®Google Scholar
• Baig S, Qazi WA, Akhtar AM, Waqar MM, Ammar A, Gilani H, Mehmood SA. 2017. Above Ground biomass estimation of Dalbergia sissoo forest plantation from dual-polarized ALOS-2 PALSAR data. Can J Remote Sens. 43(3):297–308.
   Web of Science ®Google Scholar
• Baker JR, Mitchell PL, Cordey RA, Groom GB, Settle JJ, Stileman MR. 1994. Relationships between physical characteristics and polarimetric radar backscatter for corsican pine stands in Thetford forest, U.K. Int J Remote Sens. 15(14):2827–2849.
   Web of Science ®Google Scholar
• Barni PE, Manzi AO, Condé TM, Barbosa RI, Fearnside PM. 2016. Spatial distribution of forest biomass in Brazil’s state of Roraima, northern Amazonia. Forest Ecol Manag. 377170–181.
   Web of Science ®Google Scholar
• Basuki TM, Skidmore AK, van Laake PE, van Duren I, Hussin YA. 2012. The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass. Geocarto Int. 27(4):329–345.
   Web of Science ®Google Scholar
• Beckschäfer P. 2017. Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing. Remote Sens Environ. 196:89–100.
   Web of Science ®Google Scholar
• Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V. 1998. Recent advances in mangrove studies using remote sensing data. Mar Freshwater Res. 49(4):287–296.
   Web of Science ®Google Scholar
• Boyd DS, Danson FM. 2005. Satellite remote sensing of forest resources: three decades of research development. Prog Phys Geogr. 29(1):1–26.
   Web of Science ®Google Scholar
• Broich M, Hansen MC, Potapov P, Adusei B, Lindquist E, Stehman SV. 2011. Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia. Int J Appl Earth Observ Geoinform. 13(2):277–291.
   Web of Science ®Google Scholar
• Brown I, Mwansasu S, Westerberg LO. 2016. L-band polarimetric target decomposition of mangroves of the Rufiji delta, Tanzania. Remote Sens. 8(2):140.
   Google Scholar
• Bueso-Bello JL, Martone M, González C, Wecklich C, Schulze D, Rizzoli P, Krieger G, Zink M. 2017. TanDEM-X global forest/non-forest map generation. Conference Paper presented at: International Radar Symposium.
   Google Scholar
• Canisius F, Turral H, Mbilinyi BP. 2011. Analysis of seasonal land use in Usangu wetlands, Tanzania: an object oriented technique for multi-temporal analysis with high-resolution data. Int J Remote Sens. 32(7):1885–1900.
   Web of Science ®Google Scholar
• Cardoso GF, Souza C, Jr, Souza-Filho PWM. 2014. Using spectral analysis of Landsat-5 TM images to map coastal wetlands in the Amazon River mouth, Brazil. Wetlands Ecol Manag. 22(1):79–92.
   Web of Science ®Google Scholar
• Castel T, Guerra F, Caraglio Y, Houllier F. 2002. Retrieval biomass of a large Venezuelan pine plantation using JERS-1 SAR data. Analysis of forest structure impact on radar signature. Remote Sens Environ. 79(1):30–41.
   Web of Science ®Google Scholar
• Chantuma A, Kunarasiri A, Chantuma P. 2012. Rubber new planting in Thailand: towards the world affected on climate change. Rubber Thai J. 1(1):40–47.
   Google Scholar
• Charoenjit K, Zuddas P, Allemand P, Pattanakiat S, Pachana K. 2015. Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand. J Appl Remote Sens. 9(1):096072.
   Google Scholar
• Chen B, Cao J, Wang J, Wu Z, Tao Z, Chen J, Yang C, Xie G. 2012. Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island, China. Forest Ecol Manag. 274:222–230.
   Web of Science ®Google Scholar
• Chitpaiboon C, Prakobya A, Kruasilp J. 2013. Classification of RADARSAT-2 full polarization time-series for irrigated rice plantation and yield estimation. 34th Asian Conference on Remote Sensing.
   Google Scholar
• Chong KL, Kanniah KD, Pohl C, Tan KP. 2017. A review of remote sensing applications for oil palm studies. Geo-Spatial Inform Sci. 20(2):184–200.
   Google Scholar
• Cissell JR, Delgado AM, Sweetman BM, Steinberg MK. 2018. Monitoring mangrove forest dynamics in Campeche, Mexico, using Landsat satellite data. Remote Sens Appl: Soc Environ. 9:60–68.
   Google Scholar
• Coops NC, Goodwin N, Stone C. 2006. Predicting Sphaeropsis sapinea damage in Pinus radiata canopies using spectral indices and spectral mixture analysis. Photogramm Eng Remote Sens. 72(4):405–416.
   Web of Science ®Google Scholar
• Coops N, Stanford M, Old K, Dudzinski M, Culvenor D, Stone C. 2003. Assessment of Dothistroma needle blight of pinus radiata using airborne hyperspectral imagery. Phytopathology. 93(12):1524–1532.
   PubMed Web of Science ®Google Scholar
• Curran PJ, Dungan JL, Gholz HL. 1992. Seasonal LAI in slash pine estimated with Landsat TM. Remote Sens Environ. 39(1):3–13.
   Web of Science ®Google Scholar
• Da Cruz JP, Leite HG, Soares CPB, Campos JCC, Smit L, Nogueira GS. 2008. Growth and site index curves for Tectona grandis stands in Tangará da Serra, Matogrosso state, Brazil. Rev Árvore. 32(4):679–685.
   Web of Science ®Google Scholar
• Da Ponte E, Fleckenstein M, Leinenkugel P, Parker A, Oppelt N, Kuenzer C. 2015. Tropical forest cover dynamics for Latin America using Earth observation data: a review covering the continental, regional, and local scale. Int J Remote Sens. 36(12):3196–3242.
   Web of Science ®Google Scholar
• Dahdouh-Guebas F, Jayatissa LP, Di Nitto D, Bosire JO, Seen DL, Koedam N. 2005. How effective were mangroves as a defence against the recent tsunami? Curr Biol. 15(12):R443–R447.
   PubMed Web of Science ®Google Scholar
• De Souza Pereira FR, Kampel M, Cunha-Lignon M. 2012. Mapping of mangrove forests on the southern coast of São Paulo, Brazil, using synthetic aperture radar data from ALOS/PALSAR. Remote Sens Lett. 3(7):567–576.
   Web of Science ®Google Scholar
• Deilmai BR, Ahmad BB, Zabihi H. 2014. Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia. IOP Conf Ser: Earth Environ Sci. 20:012052.
   Google Scholar
• Demir B, Bovolo F, Bruzzone L. 2013. Sequential cascade classification of image time series by exploiting multiple pairwise change detection. International Geoscience and Remote Sensing Symposium (IGARSS).
   Google Scholar
• Dibs H, Idrees MO, Alsalhin GBA. 2017. Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery. Egypt J Remote Sens Space Sci. 20(1):21–30.
   Web of Science ®Google Scholar
• Englhart S, Keuck V, Siegert F. 2011. Aboveground biomass retrieval in tropical forests - The potential of combined X- and L-band SAR data use. Remote Sens Environ. 115(5):1260–1271.
   Web of Science ®Google Scholar
• Englhart S, Keuck V, Siegert F. 2012. Modeling aboveground biomass in tropical forests using multi-frequency SAR data-A comparison of methods. IEEE J Sel Top Appl Earth Observ Remote Sens. 5(1):298–306.
   Web of Science ®Google Scholar
• Fassnacht FE, Neumann C, Forster M, Buddenbaum H, Ghosh A, Clasen A, Joshi PK, Koch B. 2014. Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE J Sel Top Appl Earth Observ Remote Sens. 7(6):2547–2561.
   Web of Science ®Google Scholar
• Foody GM, Green RM, Lucas RM, Curran PJ, Honzak M, Do Amaral I. 1997. Observations on the relationship between SIR-C radar backscatter and the biomass of regenerating tropical forests. Int J Remote Sens. 18(3):687–694.
   Web of Science ®Google Scholar
• Foxcroft LC, Richardson DM, Rejmánek M, Pyšek P. 2010. Alien plant invasions in tropical and sub-tropical savannas: patterns, processes and prospects. Biol Invasions. 12(12):3913–3933.
   Web of Science ®Google Scholar
• Fransson JES, Israelsson H. 1999. Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR data. Int J Remote Sens. 20(1):123–137.
   Web of Science ®Google Scholar
• Gandharum L, Chen CF. 2011. Use of FORMOSAT-2 satellite imagery to classify oil palm in Indonesia. 32nd Asian Conference on Remote Sensing.
   Google Scholar
• Gerard FF, North PRJ. 1997. Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model. Remote Sens Environ. 62(1):46–62.
   Web of Science ®Google Scholar
• Ghosh MK, Kumar L, Roy C. 2016. Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests. 7(12):305.
   Web of Science ®Google Scholar
• Gómez C, White JC, Wulder MA. 2016. Optical remotely sensed time series data for land cover classification: a review. ISPRS J Photogramm Remote Sens. 116:55–72.
   Web of Science ®Google Scholar
• Guyot J, Flori A. 2002. Comparative study for detecting Rigidoporus lignosus on rubber trees. Crop Protect. 21(6):461–466.
   Web of Science ®Google Scholar
• Handavu F, Chirwa PW, Syampungani S, Mahamane L. 2017. A review of carbon dynamics and assessment methods in the miombo woodlands. South Forest. 79(2):95–102.
   Web of Science ®Google Scholar
• Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, et al. 2013. High-resolution global maps of 21st-century forest cover change. Science. 342(6160):850.
   PubMed Web of Science ®Google Scholar
• Haralick RM, Dinstein I, Shanmugam K. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. SMC-3(6):610–621.
   Web of Science ®Google Scholar
• Hawryło P, Bednarz B, Wężyk P, Szostak M. 2018. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. Eur J Remote Sens. 51(1):194–204.
   Web of Science ®Google Scholar
• Helmer EH. 2000. The landscape ecology of tropical secondary forest in montane Costa Rica. Ecosystems. 3(1):98–114.
   Web of Science ®Google Scholar
• Helmer EH, Brown S, Cohen WB. 2000. Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery. Int J Remote Sens. 21(11):2163–2183.
   Web of Science ®Google Scholar
• Hess LL, Melack JM, Affonso AG, Barbosa C, Gastil-Buhl M, Novo EMLM. 2015. Wetlands of the lowland amazon basin: extent, vegetative cover, and dual-season inundated area as mapped with JERS-1 synthetic aperture radar. Wetlands. 35(4):745–756.
   Web of Science ®Google Scholar
• Heumann BW. 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Prog Phys Geogr. 35(1):87–108.
   Web of Science ®Google Scholar
• Hoan NT, Tateishi R, Bayan A, Ngigi TG, Lan M. 2011. Improving tropical forest mapping using combination of optical and microwave data of ALOS. International Geoscience and Remote Sensing Symposium (IGARSS).
   Google Scholar
• Houghton RA, Lawrence KT, Hackler JL, Brown S. 2001. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biol. 7(7):731–746.
   Web of Science ®Google Scholar
• Huang X, Zhang L, Li P. 2007. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geosci Remote Sens Lett. 4(2):260–264.
   Web of Science ®Google Scholar
• Hudson WD. 1991. Photo interpretation of montane forests in the Dominican Republic. Photogramm Eng Remote Sens. 57(1):79–84.
   Web of Science ®Google Scholar
• Islam MA, Thenkabail PS, Kulawardhana RW, Alankara R, Gunasinghe S, Edussriya C, Gunawardana A. 2008. Semi-automated methods for mapping wetlands using Landsat ETM + and SRTM data. Int J Remote Sens. 29(24):7077–7106.
   Web of Science ®Google Scholar
• Ismail R, Mutanga O, Bob U. 2007. Forest health and vitality: the detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery. South Hemisphere Forest J. 69(1):39–47.
   Google Scholar
• Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ETA, et al. 2016. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8(1):70.
   Web of Science ®Google Scholar
• Jun C, Ban Y, Li S. 2014. China: open access to Earth land-cover map. Nature. 514(7523):434.
   PubMed Web of Science ®Google Scholar
• Kamiran N, Sarker MLR. 2014. Exploring the potential of high resolution remote sensing data for mapping vegetation and the age groups of oil palm plantation. IOP Conference Series: Earth and Environmental Science.
   Google Scholar
• Kanemasu ET. 1974. Seasonal canopy reflectance patterns of wheat, sorghum, and soybean. Remote Sens Environ. 3(1):43–47.
   Google Scholar
• Kanniah KD, Najib NEM, Vu TT. 2016. Forest cover mapping in iskandar Malaysia using satellite data. Int Arch Phototgramm Remote Sens Spatial Sci. 42(4W1):71–75.
   Google Scholar
• Khalid H, Zin ZZ, Anderson JM. 1999. Quantification of oil palm biomass and nutrient value in a mature plantation; above-ground biomass. J Oil Palm Res. 11:23–32.
   Google Scholar
• Kimball JS, McDonald KC, Running SW, Frolking SE. 2004. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sens Environ. 90(2):243–258.
   Web of Science ®Google Scholar
• Kimberley M, West G, Dean M, Knowles L. 2005. The 300 Index - a volume productivity index for radiata pine. New Zeal J Forest. 50(2):13–18.
   Google Scholar
• Kitiphaisannon J, Pattanakiat S, Navanugraha C, Sanguantham P. 2005. Expert classification for age class identification of oil palm plantation in Krabi Province, Thailand. Asian Association on Remote Sensing - 26th Asian Conference on Remote Sensing and 2nd Asian Space Conference, ACRS 2005.
   Google Scholar
• Koedsin W, Huete A. 2015. Mapping rubber tree stand age using Pléiades satellite imagery: a case study in Thalang District, Phuket, Thailand. Eng J. 19(4):45–56.
   Web of Science ®Google Scholar
• Kou W, Xiao X, Dong J, Gan S, Zhai D, Zhang G, Qin Y, Li L. 2015. Mapping deciduous rubber plantation areas and stand ages with PALSAR and landsat images. Remote Sens. 7(1):1048–1073.
   Google Scholar
• Koukoulas S, Blackburn GA. 2005. Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi-spectral remotely sensed data. Int J Remote Sens. 26(3):431–455.
   Web of Science ®Google Scholar
• Laws K. 1980. Textured image segmentation [dissertation]. Los Angeles, CA: University of Southern California.
   Google Scholar
• Ledo A, Cornulier T, Illian JB, Iida Y, Kassim AR, Burslem DFRP. 2016. Re-evaluation of individual diameter: height allometric models to improve biomass estimation of tropical trees. Ecol Appl. 26(8):2376–2380.
   Web of Science ®Google Scholar
• Lee JS, Grunes MR, Pottier E. 2001. Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR. IEEE Trans Geosci Remote Sens. 39(11):2343–2351.
   Web of Science ®Google Scholar
• Lee JSH, Wich S, Widayati A, Koh LP. 2016. Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sens Appl Soc Environ. 4:219–224.
   Google Scholar
• Li Z, Fox JM. 2012. Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data. Appl Geogr. 32(2):420–432.
   Web of Science ®Google Scholar
• Liaghat S, Ehsani R, Mansor S, Shafri HZM, Meon S, Sankaran S, Azam SHMN. 2014. Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. Int J Remote Sens. 35(10):3427–3439.
   Web of Science ®Google Scholar
• Lim MT. 1986. Biomass and productivity of 4.5 year-old Acacia mangium in Sarawak. Pertanika J Trop Agric Sci. 9(1):81–87.
   Google Scholar
• Lu D. 2006. The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens. 27(7):1297–1328.
   Web of Science ®Google Scholar
• Lucas R, Bunting P, Clewley D, Armston J, Fairfax R, Fensham R, Accad A, Kelley J, Laidlaw M, Eyre T, et al. 2010. An evaluation of the ALOS PALSAR L-band backscatter—Above ground biomass relationship Queensland, Australia: impacts of surface moisture condition and vegetation structure. IEEE J Sel Top Appl Earth Observ Remote Sens. 3(4):576–593.
   Web of Science ®Google Scholar
• Lulla K. 1983. The Landsat satellites and selected aspects of physical geography. Prog Phys Geogr. 7(1):1–45.
   Web of Science ®Google Scholar
• Man Q, Dong P, Guo H, Liu G, Shi R. 2014. Light detection and ranging and hyperspectral data for estimation of forest biomass: a review. J Appl Remote Sens. 8(1):081598.
   Google Scholar
• Mansor SA, Sarker MLR. 2015. Remote sensing technique for estimating the age of oil palm using high resolution image. 36th Asian Conference on Remote Sensing.
   Google Scholar
• Martinuzzi S, Gould WA, Vierling LA, Hudak AT, Nelson RF, Evans JS. 2013. Quantifying tropical dry forest type and succession: substantial improvement with LiDAR. Biotropica. 45(2):135–146.
   Web of Science ®Google Scholar
• McMorrow J. 1995. Relation of oil palm spectral response to stand age. Int J Remote Sens. 16(16):3203–3209.
   Web of Science ®Google Scholar
• McMorrow J. 2001. Linear regression modelling for the estimation of oil palm age from Landsat TM. Int J Remote Sens. 22(12):2243–2264.
   Web of Science ®Google Scholar
• McNeill S, Pairman D. 2005. Stand age retrieval in production forest stands in New Zealand using C- And L-band polarimetric radar. IEEE Trans Geosci Remote Sens. 43(11):2503–2515.
   Web of Science ®Google Scholar
• Miettinen J, Tan WJ, Liew SC, Kwoh LK. 2010. Identification of woody plantation species in insular southeast Asia using 50m resolution ALOS PALSAR mosaic. 31st Asian Conference on Remote Sensing 2010. ACRS 2010.
   Google Scholar
• Mitchard ETA, Saatchi SS, Woodhouse IH, Nangendo G, Ribeiro NS, Williams M, Ryan CM, Lewis SL, Feldpausch TR, Meir P. 2009. Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophys Res Lett. 36(23):L23401.
   Google Scholar
• Mitsuda Y, Yoshida S, Imada M. 2003. Development of height curves for Japanese larch in relation to GIS-derived site index. J Forest Res. 8(3):199–207.
   Google Scholar
• Mohammed CL, Rimbawanto A, Page DE. 2014. Management of Basidiomycete root- and stem-rot diseases in oil palm, rubber and tropical hardwood plantation crops. Forest Pathol. 44(6):428–446.
   Web of Science ®Google Scholar
• Mohd Zaki NA, Abd Latif Z. 2017. Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling. Geocarto Int. 32(7):701–716.
   Web of Science ®Google Scholar
• Mougin E, Proisy C, Marty G, Fromard F, Puig H, Betoulle JL, Rudant JP. 1999. Multifrequency and multipolarization radar backscattering from mangrove forests. IEEE Trans Geosci Remote Sens. 37(1):94–102.
   Web of Science ®Google Scholar
• Moura YM, Galvão LS, dos Santos JR, Roberts DA, Breunig FM. 2012. Use of MISR/Terra data to study intra- and inter-annual EVI variations in the dry season of tropical forest. Remote Sens Environ. 127:260–270.
   Web of Science ®Google Scholar
• Morel AC, Fisher JB, Malhi Y. 2013. Alos Palsar for biomass assessment on rubber and teak plantations in peninsular Malaysia. 34th Asian Conference on Remote Sensing; 2013.
   Google Scholar
• Ozesmi SL, Bauer ME. 2002. Satellite remote sensing of wetlands. Wetlands Ecol Manag. 10(5):381–402.
   Google Scholar
• Park S, Im J. 2016. Classification of croplands through fusion of optical and SAR time series data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information s, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
   Google Scholar
• Pekkarinen A, Reithmaier L, Strobl P. 2009. Pan-European forest/non-forest mapping with Landsat ETM + and CORINE Land Cover 2000 data. ISPRS J Photogramm Remote Sens. 64(2):171–183.
   Web of Science ®Google Scholar
• Pfeifer M, Kor L, Nilus R, Turner E, Cusack J, Lysenko I, Khoo M, Chey VK, Chung AC, Ewers RM. 2016. Mapping the structure of Borneo's tropical forests across a degradation gradient. Remote Sens Environ. 176:84–97.
   Web of Science ®Google Scholar
• Phiri D, Morgenroth J. 2017. Developments in Landsat land cover classification methods: a review. Remote Sens. 9(9):967.
   Web of Science ®Google Scholar
• Phua MH, Johari SA, Wong OC, Ioki K, Mahali M, Nilus R, Coomes DA, Maycock CR, Hashim M. 2017. Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests. Forest Ecol Manag. 406:163–171.
   Web of Science ®Google Scholar
• Pickell PD, Hermosilla T, Frazier RJ, Coops NC, Wulder MA. 2016. Forest recovery trends derived from Landsat time series for North American boreal forests. Int J Remote Sens. 37(1):138–149.
   Web of Science ®Google Scholar
• Pietrzykowski E, Sims N, Stone C, Pinkard L, Mohammed C. 2007. Predicting Mycosphaerella leaf disease severity in a Eucalyptus globulus plantation using digital multi-spectral imagery. South Hemisphere Forest J. 69(3):175–182.
   Google Scholar
• Pinto N, Simard M, Dubayah R. 2012. Using InSAR coherence to map stand age in a boreal forest. Remote Sens. 5(1):42–56.
   Google Scholar
• Pipkins K, Förster M, Clasen A, Schmidt T, Kleinschmit B. 2014. Comparison of feature selection methods for multitemporal tree species classification. SPIE Remote Sensing; Amsterdam, Netherlands.
   Google Scholar
• Poona NK, Ismail R. 2013. Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks. South Forest. 75(1):29–40.
   Web of Science ®Google Scholar
• Ramdani F, Rahman S, Giri C. 2018. Principal polar spectral indices for mapping mangroves forest in South East Asia: study case Indonesia. Int J Dig Earth. 2018:1–15.
   Google Scholar
• Ranganath BK, Pradeep N, Manjula VB, Gowda B, Rajanna MD, Shettigar D, Rao PPN. 2004. Detection of diseased rubber plantations using satellite remote sensing. J Indian Soc Remote Sens. 32(1):49–58.
   Google Scholar
• Razak JABA, Shariff ARBM, Ahmad NB, Sameen MI. 2017. Mapping rubber trees based on phenological analysis of Landsat time series data-sets. Geocarto Int. 1:1–24.
   Google Scholar
• Razali SM, Marin A, Nuruddin AA, Shafri HZM, Hamid HA. 2014. Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions. Sensors (Switzerland). 14(5):8259–8282.
   PubMed Web of Science ®Google Scholar
• Reiche J, Souza CM, Hoekman DH, Verbesselt J, Persaud H, Herold M. 2013. Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation. IEEE J Sel Top Appl Earth Observ Remote Sens. 6(5):2159–2173.
   Web of Science ®Google Scholar
• Reiche J, Verbesselt J, Hoekman D, Herold M. 2015. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sens Environ. 156:276–293.
   Web of Science ®Google Scholar
• Rosenqvist Å. 1996. Evaluation of JERS-1, ERS-1 and Almaz SAR backscatter for rubber and oil palm stands in West Malaysia. Int J Remote Sens. 17(16):3219–3231.
   Web of Science ®Google Scholar
• Rosenqvist Å, Milne A, Lucas R, Imhoff M, Dobson C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environ Sci Policy. 6(5):441–455.
   Web of Science ®Google Scholar
• Rosenqvist A, Shimada M, Chapman B, McDonald K, De Grandi G, Jonsson H, Williams C, Rauste Y, Nilsson M, Sango D, Matsumoto M, Sango D, et al. 2004. An overview of the JERS-1 SAR global boreal forest mapping (GBFM) project. International Geoscience and Remote Sensing Symposium (IGARSS); 2004.
   Google Scholar
• Rushayati SB, Hermawan R, Meilani R. 2017. Age-surface temperature estimation model: when will oil palm plantation reach the same surface temperature as natural forest? IOP Conf Ser: Earth Environ Sci. 54:012048.
   Google Scholar
• Sabajo CR, Le Maire G, June T, Meijide A, Roupsard O, Knohl A. 2017. Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences. 14(20):4619–4635.
   Web of Science ®Google Scholar
• Sadono R, Aritonang SA, Santosa AB, Rachman I. 2014. Modeling the top height growth and site index of eucalyptus urograndis from data inventory in north Sumatra, Indonesia [Article. Am-Euras J Sustain Agric. 8(6):35–42.
   Google Scholar
• Sakamoto T, Shibayama M, Kimura A, Takada E. 2011. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. ISPRS J Photogramm Remote Sens. 66(6):872–882.
   Web of Science ®Google Scholar
• Santoso H, Gunawan T, Jatmiko RH, Darmosarkoro W, Minasny B. 2011. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precis Agric. 12(2):233–248.
   Web of Science ®Google Scholar
• Santoso H, Tani H, Wang X. 2017. Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations. Int J Remote Sens. 38(16):4683–4699.
   Web of Science ®Google Scholar
• Saragih ES, Muhdi, Hanafiah DS. 2016. Pendugaan cadangan karbon pada tanaman karet (Hevea brasiliensis Muell. Arg.) umur 10 tahun di perkebunan rakyat desa Tarean, kecamatan Silindak, kabupaten Serdang Bedagai. Peronema For Sci J. 5(2):5–19.
   Google Scholar
• Schmullius C, Thiel C, Pathe C, Santoro M. 2015. Radar time series for land cover and forest mapping. In: Kuenzer C, Dech S, Wagner W, editors. Remote sensing time series: revealing land surface dynamics. Cham: Springer International Publishing; p. 323–356.
   Google Scholar
• Schnell S, Altrell D, Ståhl G, Kleinn C. 2015a. The contribution of trees outside forests to national tree biomass and carbon stocks—a comparative study across three continents. Environ Monit Assess. 187(1):4197.
   PubMed Web of Science ®Google Scholar
• Schnell S, Kleinn C, Ståhl G. 2015b. Monitoring trees outside forests: a review. Environ Monit Assess. 187(9):600.
   PubMed Web of Science ®Google Scholar
• Schroth G, D’Angelo SA, Teixeira WG, Haag D, Lieberei R. 2002. Conversion of secondary forest into agroforestry and monoculture plantations in Amazonia: consequences for biomass, litter and soil carbon stocks after 7 years. Forest Ecol Manag. 163(1-3):131–150.
   Web of Science ®Google Scholar
• Senf C, Pflugmacher D, van der Linden S, Hostert P. 2013. Mapping rubber plantations and natural forests in Xishuangbanna (Southwest China) using multi-spectral phenological metrics from MODIS time series. Remote Sens. 5(6):2795–2812.
   Google Scholar
• Shashikant V, Shariff ARM, Nordin L, Pradhan B. 2013. Above ground biomass estimation of oil palm tree using PALSAR. 34th Asian Conference on Remote Sensing.
   Google Scholar
• Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R, Lucas R. 2014. New global forest/non-forest maps from ALOS PALSAR data (2007-2010). Remote Sens Environ. 15513–31.
   Web of Science ®Google Scholar
• Sinha S, Jeganathan C, Sharma LK, Nathawat MS. 2015. A review of radar remote sensing for biomass estimation. Int J Environ Sci Technol. 12(5):1779–1792.
   Web of Science ®Google Scholar
• Smits PC, Dellepiane SG, Schowengerdt RA. 1999. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. Int J Remote Sens. 20(8):1461–1486.
   Web of Science ®Google Scholar
• Solberg S, Astrup R, Breidenbach J, Nilsen B, Weydahl D. 2013. Monitoring spruce volume and biomass with InSAR data from TanDEM-X. Remote Sens Environ. 13960–67.
   Web of Science ®Google Scholar
• Solberg S, Riegler G, Nonin P. 2015. Estimating forest biomass from TerraSAR-X stripmap radargrammetry. IEEE Trans Geosci Remote Sens. 53(1):154–161.
   Web of Science ®Google Scholar
• Sone K, Watanabe N, Takase M, Hosaka T, Gyokusen K. 2014. Carbon sequestration, tree biomass growth and rubber yield of PB260 clone of rubber tree (Hevea brasiliensis) in North Sumatra. J Rubber Res. 17(2):115–127.
   Web of Science ®Google Scholar
• Spanner MA, Pierce LL, Running SW, Peterson DL. 1990. The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index. Remote Sens Environ. 33(2):97–112.
   Web of Science ®Google Scholar
• Stabach JA, Dabek L, Jensen R, Wang YQ. 2009. Discrimination of dominant forest types for Matschie's tree kangaroo conservation in Papua New Guinea using high-resolution remote sensing data. Int J Remote Sens. 30(2):405–422.
   Web of Science ®Google Scholar
• Steininger MK. 1996. Tropical secondary forest regrowth in the Amazon: age, area and change estimation with Thematic Mapper data. Int J Remote Sens. 17(1):9–27.
   Web of Science ®Google Scholar
• Stone C, Carnegie A, Melville G, Smith D, Nagel M. 2013. Aerial mapping canopy damage by the aphid Essigella californica in a Pinus radiata plantation in southern New South Wales: what are the challenges? Aust Forest. 76(2):101–109.
   Web of Science ®Google Scholar
• Stone C, Coops NC. 2004. Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations. Aust J Entomol. 43(3):283–292.
   Google Scholar
• Stone C, Webster M, Osborn J, Iqbal I. 2016. Alternatives to LiDAR-derived canopy height models for softwood plantations: a review and example using photogrammetry. Aust Forest. 79(4):271–282.
   Web of Science ®Google Scholar
• Sun Z, Leinenkuge P, Guo H, Huang C, Kuenzer C. 2017. Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method. J Appl Remote Sens. 11(2):026011.
   Google Scholar
• Sutomo van Etten E, Wahab L. 2016. Proof of Acacia nilotica stand expansion in Bekol Savanna, Baluran National Park, East Java, Indonesia through remote sensing and field observations. Biodiversitas. 17(1):96–101.
   Google Scholar
• Syahrinudin S. 2005. The potential of oil palm and forest plantations for carbon sequestration on degraded land in Indonesia. Göttingen: Cuvillier Verlag.
   Google Scholar
• Takayama T, Ohki T, and Takeda T. 2017. Discrimination of peat swamp forest types with hyperspectral data. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; 2017.
   Google Scholar
• Tan KP, Kanniah KD, Cracknell AP. 2013. Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia. Int J Remote Sens. 34(20):7424–7446.
   Web of Science ®Google Scholar
• Taylor BF, Dini PW, Kidson JW. 1985. Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA-7 data. Remote Sens Environ. 18(2):177–192.
   Web of Science ®Google Scholar
• Topaloǧlu RH, Sertel E, Musaoǧlu N. 2016. Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. ISPRS Archives. 41:1055–1059.
   Google Scholar
• Tottrup C, Rasmussen MS, Samek J, Skole DL. 2007. Towards a generic approach for characterizing and mapping tropical secondary forests in the highlands of mainland Southeast Asia. Int J Remote Sens. 28(6):1263–1284.
   Web of Science ®Google Scholar
• Trisasongko BH. 2017. Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data. Eur J Remote Sens. 50(1):64–76.
   Web of Science ®Google Scholar
• Trisasongko BH, Panuju DR. 2015. Characteristics of L-band backscatter coefficients of rubber plantation and their seasonal dynamics. AIP Conference Proceedings. 1677:060006.
   Google Scholar
• Trisasongko BH, Panuju DR, Paull DJ, Jia X, Griffin AL. 2017. Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data. Int J Remote Sens. 38(11):3274–3293.
   Web of Science ®Google Scholar
• Truong TTA, Hardy GESJ, Andrew ME. 2017. Contemporary remotely sensed data products refine invasive plants risk mapping in data poor regions. Front Plant Sci. 8:770.
   PubMed Web of Science ®Google Scholar
• Tsai L. 1988. Studies on Acacia mangium in Kemasul Forest, Malaysia. I. Biomass and productivity. J Trop Ecol. 4(03):293–302.
   Google Scholar
• Vaglio Laurin G, Puletti N, Hawthorne W, Liesenberg V, Corona P, Papale D, Chen Q, Valentini R. 2016. Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sens Environ. 176:163–176.
   Web of Science ®Google Scholar
• Van Aardt JAN, Norris-Rogers M. 2008. Spectral-age interactions in managed, even-aged Eucalyptus plantations: application of discriminant analysis and classification and regression trees approaches to hyperspectral data. Int J Remote Sens. 29(6):1841–1845.
   Web of Science ®Google Scholar
• Wang C, Niu Z,Gu X, Guo Z, Cong P. 2005. Tropical forest plantation biomass estimation using RADARSAT-SAR and TM data off south China. Proceedings of the SPIE, Volume 6043, pp. 753–760.
   Google Scholar
• Watt MS, Dash JP, Bhandari S, Watt P. 2015. Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning. For Ecol Manag. 357:1–9.
   Web of Science ®Google Scholar
• Wohlfart C, Wegmann M, Leimgruber P. 2014. Mapping threatened dry deciduous dipterocarp forest in South-east Asia for conservation management. Trop Conserv Sci. 7(4):597–613.
   Web of Science ®Google Scholar
• Wulder MA, White JC, Nelson RF, Naesset E, Ørka HO, Coops NC, Hilker T, Bater CW, Gobakken T. 2012. Lidar sampling for large-area forest characterization: a review. Remote Sens Environ. 121196–209.
   Web of Science ®Google Scholar
• Zulkarnain MT, Putra AE, Widayati A. 2013. Landcover mapping of Tanjung jabung barat, Jambi using Landsat - ALOS PALSAR data fusion and object-based hierarchical classification. 34th Asian Conference on Remote Sensing 2013.
   Google Scholar