Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing

Remote Sensing of Environment

Volumn 196, Issue , 2017, Pages 89-100

  Beckschäfer, Philip ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

Best available pixel compositing; Hevea brasiliensis; Landsat time series; NDMI; Stand age; TimeSync

Indexed keywords

DECISION MAKING; DECISION SUPPORT SYSTEMS; FORESTRY; LAND USE; LATEXES; MEAN SQUARE ERROR; PIXELS; RUBBER; TIME SERIES;

COMPOSITING; HEVEA BRASILIENSIS; LANDSAT TIME SERIES; NDMI; STAND AGE; TIMESYNC;

RUBBER PLANTATIONS;

AGE STRUCTURE; DECIDUOUS TREE; DECISION MAKING; ERROR ANALYSIS; FOREST MANAGEMENT; IMAGE ANALYSIS; INDEX METHOD; LAND COVER; LAND USE PLANNING; LANDSAT THEMATIC MAPPER; MOISTURE; PIXEL; PLANTATION; TIMBER HARVESTING; TIME SERIES ANALYSIS;

CHINA; XISHUANGBANNA; YUNNAN;

HEVEA BRASILIENSIS;

EID: 85018785007     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.04.003     Document Type: Article

References (64)

1. Banskota, A., Kayastha, N., Falkowski, M.J., Wulder, M.A., Froese, R.E., White, J.C., Forest monitoring using Landsat time series data: a review. Can. J. Remote. Sens. 40 (2014), 362–384, 10.1080/07038992.2014.987376.
2. Blagodatsky, S., Xu, J., Cadisch, G., Carbon balance of rubber (Hevea brasiliensis) plantations: a review of uncertainties at plot, landscape and production level. Agric. Ecosyst. Environ. 221 (2016), 8–19, 10.1016/j.agee.2016.01.025.
3. de Blécourt, M., Brumme, R., Xu, J., Corre, M.D., Veldkamp, E., Soil carbon stocks decrease following conversion of secondary forests to rubber ( Hevea brasiliensis ) plantations. PLoS One, 8, 2013 e69357 http://dx.doi.org/10.1371/journal.pone.0069357.
4. Cao, H., Liu, J., Fu, C., Zhang, W., Wang, G., Yang, G., Luo, L., Urban expansion and its impact on the land use pattern in Xishuangbanna since the reform and opening up of China. Remote Sens., 9, 2017, 137, 10.3390/rs9020137.
5. Chen, B., Cao, J., Wang, J., Wu, Z., Tao, Z., Chen, J., Yang, C., Xie, G., Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island. China. For. Ecol. Manag. 274 (2012), 222–230, 10.1016/j.foreco.2012.01.033.
6. Chen, H., Yi, Z.-F., Schmidt-Vogt, D., Ahrends, A., Beckschäfer, P., Kleinn, C., Ranjitkar, S., Xu, J., Pushing the limits: the pattern and dynamics of rubber monoculture expansion in Xishuangbanna, SW China. PLoS One, 11, 2016, e0150062, 10.1371/journal.pone.0150062.
7. Cohen, W.B., Yang, Z., Kennedy, R., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation. Remote Sens. Environ. 114 (2010), 2911–2924, 10.1016/j.rse.2010.07.010.
8. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., Bargellini, P., 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens. Environ., The Sentinel Missions - New Opportunities for Science 120, 25–36. http://dx.doi.org/10.1016/j.rse.2011.11.026.
9. Fan, H., Fu, X., Zhang, Z., Wu, Q., Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data. Remote Sens. 7 (2015), 6041–6058, 10.3390/rs70506041.
10. FAOSTAT. [WWW Document]. URL http://faostat3.fao.org/home/E, 2015 (accessed 28.10.2016).
11. Gao, B., NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58 (1996), 257–266, 10.1016/S0034-4257(96)00067-3.
12. Goodwin, N.R., Coops, N.C., Wulder, M.A., Gillanders, S., Schroeder, T.A., Nelson, T., Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sens. Environ. 112 (2008), 3680–3689, 10.1016/j.rse.2008.05.005.
13. Goslee, S., Analyzing remote sensing data in R: the landsat package. J. Stat. Softw., 1–25, 2011.
14. Goward, S., Arvidson, T., Williams, D., Faundeen, J., Irons, J., Franks, S., Historical record of Landsat global coverage: mission operations, NSLRSDA, and International Cooperator stations. Photogramm. Eng. Remote. Sens. 72 (2006), 1155–1169, 10.14358/PERS.72.10.1155.
15. Griffiths, P., van der Linden, S., Kuemmerle, T., Hostert, P., A pixel-based Landsat compositing algorithm for large area land cover mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6 (2013), 2088–2101, 10.1109/JSTARS.2012.2228167.
16. Grogan, K., Pflugmacher, D., Hostert, P., Kennedy, R., Fensholt, R., Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series. Remote Sens. Environ. 169 (2015), 438–453, 10.1016/j.rse.2015.03.001.
17. Guardiola-Claramonte, M., Troch, P.A., Ziegler, A.D., Giambelluca, T.W., Durcik, M., Vogler, J.B., Nullet, M.A., Hydrologic effects of the expansion of rubber (Hevea brasiliensis) in a tropical catchment. Ecohydrology 3 (2010), 306–314, 10.1002/eco.110.
18. Hammond, J., Yi, Z.-F., McLellan, T., Zhao, J., Situational Analysis Report: Xishuangbanna Autonomous Dai Prefecture, Yunnan Province, China (Working Paper No. 194), ICRAF Working Paper. 2015, Kunming, China, World Agroforestry Centre East and Central Asia.
19. Hansen, M.C., Loveland, T.R., 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ., Landsat Legacy Special Issue 122, 66–74. http://dx.doi.org/10.1016/j.rse.2011.08.024.
20. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., High-resolution global maps of 21st-century Forest cover change. Science 342 (2013), 850–853, 10.1126/science.1244693.
21. Hayes, D.J., Cohen, W.B., Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data. Remote Sens. Environ. 106 (2007), 1–16, 10.1016/j.rse.2006.07.002.
22. Hayes, D.J., Cohen, W.B., Sader, S.A., Irwin, D.E., Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data. Remote Sens. Environ. 112 (2008), 735–749, 10.1016/j.rse.2007.06.003.
23. Helmer, E.H., Lefsky, M.A., Roberts, D.A., 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. J. Appl. Remote. Sens. 3, 33505–33505–31. http://dx.doi.org/10.1117/1.3082116.
24. Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sens. Environ. 158 (2015), 220–234, 10.1016/j.rse.2014.11.005.
25. Holben, B.N., Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7 (1986), 1417–1434, 10.1080/01431168608948945.
26. Holden, C.E., Woodcock, C.E., 2016. An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations. Remote Sens. Environ., Landsat 8 Science Results 185, 16–36. http://dx.doi.org/10.1016/j.rse.2016.02.052.
27. Jin, S., Sader, S.A., Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 94 (2005), 364–372, 10.1016/j.rse.2004.10.012.
28. Koedsin, W., Huete, A., Mapping rubber tree stand age using Pléiades Satellite Imagery: a case study in Talang District, Phuket. Thailand. Eng. J. 19 (2015), 45–56, 10.4186/ej.2015.19.4.45.
29. Kou, W., Xiao, X., Dong, J., Gan, S., Zhai, D., Zhang, G., Qin, Y., Li, L., Mapping deciduous rubber plantation areas and stand ages with PALSAR and Landsat images. Remote Sens. 7 (2015), 1048–1073, 10.3390/rs70101048.
30. Li, Z., Fox, J.M., Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data. Appl. Geogr. 32 (2012), 420–432, 10.1016/j.apgeog.2011.06.018.
31. Li, H., Aide, T.M., Ma, Y., Liu, W., Cao, M., Demand for rubber is causing the loss of high diversity rain forest in SW China. Biodivers. Conserv. 16 (2007), 1731–1745, 10.1007/s10531-006-9052-7.
32. Li, H., Ma, Y., Aide, T.M., Liu, W., Past, present and future land-use in Xishuangbanna, China and the implications for carbon dynamics. For. Ecol. Manag. 255 (2008), 16–24, 10.1016/j.foreco.2007.06.051.
33. Li, P., Jiang, L., Feng, Z., Cross-comparison of vegetation indices derived from Landsat-7 enhanced thematic mapper plus (ETM +) and Landsat-8 operational land imager (OLI) sensors. Remote Sens. 6 (2013), 310–329, 10.3390/rs6010310.
34. Li, P., Zhang, J., Feng, Z., Mapping rubber tree plantations using a Landsat-based phenological algorithm in Xishuangbanna, southwest China. Remote Sens. Lett. 6 (2015), 49–58, 10.1080/2150704X.2014.996678.
35. Liaw, A., Wiener, M., Classification and regression by randomForest. R News 2 (2002), 18–22.
36. Liu, X., Feng, Z., Jiang, L., Li, P., Liao, C., Yang, Y., You, Z., Rubber plantation and its relationship with topographical factors in the border region of China, Laos and Myanmar. J. Geogr. Sci. 23 (2013), 1019–1040, 10.1007/s11442-013-1060-4.
37. Liyanage, K.K., Khan, S., Mortimer, P.E., Hyde, K.D., Xu, J., Brooks, S., Ming, Z., Powdery mildew disease of rubber tree. For. Pathol. 46 (2016), 90–103, 10.1111/efp.12271.
38. Markham, B.L., Storey, J.C., Williams, D.L., Irons, J.R., Landsat sensor performance: history and current status. IEEE Trans. Geosci. Remote Sens. 42 (2004), 2691–2694, 10.1109/TGRS.2004.840720.
39. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.-K., A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3 (2006), 68–72, 10.1109/LGRS.2005.857030.
40. Nizami, S.M., Yiping, Z., Liqing, S., Zhao, W., Zhang, X., Managing carbon sinks in rubber ( Hevea Brasilensis ) plantation by changing rotation length in SW China. PLoS One, 9, 2014 e115234 http://dx.doi.org/10.1371/journal.pone.0115234.
41. Olofsson, P., Foody, G.M., Stehman, S.V., Woodcock, C.E., Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 129 (2013), 122–131, 10.1016/j.rse.2012.10.031.
42. Pflugmacher, D., Cohen, W.B., Kennedy, R.E., Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens. Environ. 122 (2012), 146–165.
43. Potapov, P., Turubanova, S., Hansen, M.C., Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sens. Environ. 115 (2011), 548–561, 10.1016/j.rse.2010.10.001.
44. Potapov, P.V., Turubanova, S.A., Hansen, M.C., Adusei, B., Broich, M., Altstatt, A., Mane, L., Justice, C.O., Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data. Remote Sens. Environ. Landsat Legacy Special Issue 122 (2012), 106–116, 10.1016/j.rse.2011.08.027.
45. Potapov, P.V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., Hansen, M.C., Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sens. Environ. 159 (2015), 28–43, 10.1016/j.rse.2014.11.027.
46. Priyadarshan, P.M., Sasikumar, S., Goncalves, P. de S., Phenological changes in Hevea brasiliensis under differential geo-climates. The Planter 77 (2001), 447–459.
47. QGIS Development Team, QGIS Geographic Information System. 2016 (Open Source Geospatial Foundation Project).
48. Qi, D., Zhou, J., Xie, G., Wu, Z., Optimizing tapping-tree density of rubber (Hevea brasiliensis) plantations in South China. Small-Scale For. 15 (2016), 61–72, 10.1007/s11842-015-9308-8.
49. Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C., Web-enabled Landsat data (WELD): Landsat ETM + composited mosaics of the conterminous United States. Remote Sens. Environ. 114 (2010), 35–49, 10.1016/j.rse.2009.08.011.
50. Ruefenacht, B., Comparison of three Landsat TM compositing methods: a case study using modeled tree canopy cover. Photogramm. Eng. Remote. Sens. 82 (2016), 199–211, 10.14358/PERS.82.3.199.
51. Senf, C., Pflugmacher, D., van der Linden, S., Hostert, P., Mapping rubber plantations and natural forests in Xishuangbanna (Southwest China) using multi-spectral Phenological metrics from MODIS time series. Remote Sens. 5 (2013), 2795–2812, 10.3390/rs5062795.
52. Suratman, M.N., Bull, G.Q., Leckie, D.G., Lemay, V.M., Marshall, P.L., Mispan, M.R., Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data. Int. For. Rev. 6 (2004), 1–12, 10.1505/ifor.6.1.1.32055.
53. Tan, Z.-H., Zhang, Y.-P., Song, Q.-H., Liu, W.-J., Deng, X.-B., Tang, J.-W., Deng, Y., Zhou, W.-J., Yang, L.-Y., Yu, G.-R., Sun, X.-M., Liang, N.-S., Rubber plantations act as water pumps in tropical China. Geophys. Res. Lett., 38, 2011 L24406 http://dx.doi.org/10.1029/2011GL050006.
54. Trisasongko, B.H., Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data. Eur. J. Remote Sens. 50 (2017), 64–76, 10.1080/22797254.2017.1274569.
55. USGS, 2016. Landsat Surface Reflectance High Level Data Products [WWW Document]. URL http://landsat.usgs.gov/CDR_LSR.php (accessed 10.5.16).
56. White, J.C., Wulder, M.A., Hobart, G.W., Luther, J.E., Hermosilla, T., Griffiths, P., Coops, N.C., Hall, R.J., Hostert, P., Dyk, A., Guindon, L., Pixel-based image compositing for large-area dense time series applications and science. Can. J. Remote. Sens. 40 (2014), 192–212, 10.1080/07038992.2014.945827.
57. Wolfe, R.E., Roy, D.P., Vermote, E., MODIS land data storage, gridding, and compositing methodology: level 2 grid. IEEE Trans. Geosci. Remote Sens. 36 (1998), 1324–1338, 10.1109/36.701082.
58. Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P., 2016. The global Landsat archive: Status, consolidation, and direction. Remote Sens. Environ., Landsat 8 Science Results 185, 271–283. http://dx.doi.org/10.1016/j.rse.2015.11.032.
59. Xu, J., Grumbine, R.E., Beckschäfer, P., Landscape transformation through the use of ecological and socioeconomic indicators in Xishuangbanna, Southwest China, Mekong Region. Ecol. Indic. 36 (2014), 749–756, 10.1016/j.ecolind.2012.08.023.
60. Yi, Z.-F., Wong, G., Cannon, C.H., Xu, J., Beckschäfer, P., Swetnam, R.D., Can carbon-trading schemes help to protect China's most diverse forest ecosystems? A case study from Xishuangbanna, Yunnan. Land Use Policy 38 (2014), 646–656, 10.1016/j.landusepol.2013.12.013.
61. Yi, Z.-F., Cannon, C.H., Chen, J., Ye, C.-X., Swetnam, R.D., Developing indicators of economic value and biodiversity loss for rubber plantations in Xishuangbanna, southwest China: a case study from Menglun township. Ecol. Indic. 36 (2014), 788–797, 10.1016/j.ecolind.2013.03.016.
62. Zhang, J., Cao, M., Tropical forest vegetation of Xishuangbanna, SW China and its secondary changes, with special reference to some problems in local nature conservation. Biol. Conserv. 73 (1995), 229–238, 10.1016/0006-3207(94)00118-A.
63. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94, 10.1016/j.rse.2011.10.028.
64. Zhu, Z., Woodcock, C.E., Olofsson, P., 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ., Landsat Legacy Special Issue 122, 75–91. http://dx.doi.org/10.1016/j.rse.2011.10.030.