L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

International Journal of Applied Earth Observation and Geoinformation

Volumn 52, Issue , 2016, Pages 54-64

  Naidoo, Laven ^a,b   Mathieu, Renaud ^a,b   Main, Russell ^a,b   Wessels, Konrad ^a,b   Asner, Gregory P ^c  

^a DSI CSIR Nanotechnology Innovation Centre   (South Africa)

^b UNIVERSITY OF PRETORIA   (South Africa)

^c GEOPHYSICAL LABORATORY   (United States)

Author keywords

LandSAT 5; Random Forest; SAR; Spectral vegetation indices; Textures; Woody canopy cover

Indexed keywords

ALOS; IMAGE ANALYSIS; LANDSAT; MACHINE LEARNING; PALSAR; RADAR IMAGERY; SAVANNA; SYNTHETIC APERTURE RADAR; VEGETATION COVER;

SOUTH AFRICA;

EID: 84997673899     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2016.05.006     Document Type: Article

References (49)

1. Scholes, R.J., Walker, B.H., An African Savanna: Synthesis of the Nylsvley Study. 1993, Cambridge University Press, Cambridge.
2. Sankaran, M., Ratnam, J., Hanan, N., Woody cover in African savannas: the role of resources, fire and herbivory. Glob. Ecol. Biogeogr. 17 (2008), 236–245.
3. Shackleton, C.M., Shackleton, S.E., Buiten, E., Bird, N., The importance of dry woodlands and forests in rural livelihoods and poverty alleviation in South Africa. For. Policy Econ. 9 (2007), 558–577.
4. Valentini, R., Arneth, A., Bombelli, A., Castaldi, S., Cazzolla Gatti, R., Chevallier, F., Ciais, P., Grieco, E., Hartmann, J., et al. A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities. Biogeosciences 11 (2014), 381–407.
5. Mograbi, P.J., Erasmus, B.F.N., Witkowski, E.T.F., Asner, G.P., Wessels, K.J., Mathieu, R., Knapp, D.E., Martin, R.E., Main, R., Biomass increases go under cover: woody vegetation dynamics in South African rangelands. PLoS One 10:5 (2015), 1–21.
6. Shackleton, C.M., Griffin, N.J., Banks, D.I., Mavrandonis, J.M., Shackleton, S.E., Community structure and species composition along a disturbance gradient in a communally managed South African savanna. Vegetatio 115:2 (1994), 157–167.
7. Jennings, S.B., Brown, N.D., Sheil, D., Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry 72 (1999), 59–73.
8. Edwards, D., A broad-scale structural classification of vegetation for practical purposes. Bothalia 14:3&4 (1983), 705–712.
9. Pereira, H.M., Ferrier, S., Walters, M., Geller, G.N., Jongman, R.H.G., Scholes, R.J., Bruford, M.W., Brummitt, N., Butchart, S.H.M., Cardoso, A.C., et al. Essential biodiversity variables. Science 339 (2013), 277–278.
10. Archibald, S., Scholes, R.J., Leaf green-up in a semi-arid African savannah—separating tree and grass responses to environmental cues. J. Veg. Sci. 18 (2007), 583–594.
11. Le Toan, T., Quegan, S., Davidson, M.W.J., Balzter, H., Paillou, P., Papathanassiou, K., Plummer, S., Rocca, F., Saatchi, S., Shugart, H., Ulander, L., The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens. Environ. 115 (2011), 2850–2860.
12. Sun, G., Jon Ranson, K., Guo, Z., Zhang, Z., Montesano, P., Kimes, D., Forest biomass mapping from LiDAR and radar synergies. Remote Sens. Environ. 115 (2011), 2906–2916.
13. Santoro, M., Shvidenko, A., McCallum, I., Askne, J., Schmullius, C., Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval. Remote Sens. Environ. 106 (2007), 154–172.
14. Carreiras, J.M.B., Melo, J.B., Vasconcelos, M.J., Estimating the above-ground biomass in Miombo savannah woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sens. Open Access 5 (2013), 1524–1548.
15. Naidoo, L., Mathieu, R., Main, R., Kleynhans, W., Wessels, K., Asner, G., Leblon, B., Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) synthetic aperture radar data. ISPRS J. Photogramm. Remote Sens. 105 (2015), 234–250.
16. Mitchard, E.T.A., Saatchi, S.S., Woodhouse, I.H., Nangendo, G., Ribeiro, N.S., Williams, M., Ryan, C.M., Lewis, S.L., Feldpausch, T.R., Meir, P., Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophys. Res. Lett. 36 (2009), 1–6.
17. Mathieu, R., Naidoo, L., Cho, M.A., Leblon, B., Main, R., Wessels, K., Asner, G.P., Buckley, J., Van Aardt, J., Erasmus, B.F.N., Smit, I.P.J., Toward structural assessment of semi-arid African savannahs and woodlands: the potential of multitemporal polarimetric RADARSAT-2 fine beam images. Remote Sens. Environ. 138 (2013), 215–231.
18. Hansen, M.C., Loveland, T.R., A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 122 (2012), 66–74.
19. Foody, G.M., Lucas, R.M., Curran, P.J., Honzak, M., Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery. Plant Ecol. 131 (1997), 143–154.
20. Lehmann, E.A., Wallace, J.F., Caccetta, P.A., Furby, S.L., Zdunic, K., Forest cover trends from time series Landsat data for the Australian continent. Int. J. Appl. Earth Obs. Geoinf. 21 (2013), 453–462.
21. Armston, J.D., Denham, R.J., Danaher, T.J., Scarth, P.F., Moffiet, T.N., Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. J. Appl. Remote Sens. 3 (2009), 1–28.
22. Boggs, G.S., Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas. Int. J. Appl. Earth Obs. Geoinf. 12:4 (2010), 217–224.
23. Zeidler, J., Wegmann, M., Dech, S., Spatio-temporal robustness of fractional cover upscaling: a case study in semi-arid Savannah's of Namibia and Western Zambia. Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380 S (October 25, 2012); 1-10 (2012) [10.1117/12.970623].
24. Nichol, J.E., Sarker, L.R., Improved biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE Trans. Geosci. Remote Sens. 49:3 (2011), 930–948.
25. Ju, J., Roy, D.P., Vermote, E., Masek, J., Kovalskyy, V., Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods. Remote Sens. Environ. 122 (2012), 175–184.
26. Moghaddam, M., Dungan, J.L., Acker, S., Forest variable estimation from fusion of SAR and multispectral optical data. IEEE Trans. Geosci. Remote Sens. 40:10 (2002), 2176–2187.
27. Lucas, R.M., Cronin, N., Moghaddam, M., Lee, A., Armston, J., Bunting, P., Witte, C., Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia. Remote Sens. Environ. 100:3 (2006), 388–406.
28. Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Jones, M.O., Martin, R.E., Boardman, J., Field, C.B., Carnegie airborne observatory: in-flight fusion of hyperspectral imaging and waveform LiDAR for 3D studies of ecosystems. J. Appl. Remote Sens. 1 (2007), 1–27.
29. The Vegetation of South Africa, Lesotho and Swaziland. Mucina, L., Rutherford, M.C., (eds.), 2006, South African National Biodiversity Institute, Pretoria.
30. Breiman, L., Random forests. Mach. Learn. 45:1 (2001), 5–32.
31. Wessels, K.J., Mathieu, R., Erasmus, B.F.N., Asner, G.P., Smit, I.P.J., van Aardt, J.A.N., et al. Impact of communal land use and conservation on woody vegetation structure in the Lowveld savannas of South Africa. For. Ecol. Manag. 261 (2011), 19–29.
32. Huete, A.R., Liu, H.Q., Batchily, K., van Leeuwen, W., A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens. Environ. 59:3 (1997), 440–451.
33. Sims, D.A., Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81:2–3 (2002), 337–354.
34. Goel, N.S., Qin, W., Influence of canopy architecture on various vegetation indices and LAI and FPAR: a computer simulation. Remote Sens. Rev. 10 (1994), 309–347.
35. Huete, A.R., Jackson, R.D., Soil and atmosphere influences on the spectra of partial canopies. Remote Sens. Environ. 25:1 (1988), 89–105.
36. Jordan, C.F., Derivation of leaf area index from quality of light on the forest floor. Ecology 50:4 (1969), 663–666.
37. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS. In: S.C. Fraden, E.P., Marcanti, M.A. Becker (eds.), Third ERTS-1 Symposium, 10-14 Dec. 1973, NASA SP-351, Washington D.C. NASA, 309-317 (1974).
38. Sousa, C.L., Ponzoni, F.J., Avaliacao de indices de vegetacao e de bandas TM/Landsat para estimative de volume de madeira em floresta implantada de Pinus spp. IX Simposio Brasileiro de Sensoriamento Remoto, p.11 (1998).
39. Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification. IEEE Trans. Syst., Man and Cybern. 3:6 (1973), 610–621.
40. Asner, G.P., Keller, M., Pereira, R., Zweede, J.C., Remote sensing of selective logging in Amazonia assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sens. Environ. 80 (2002), 483–496.
41. Lu, D., Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int. J. Remote Sens. 26:12 (2005), 2509–2525.
42. Urbazaev, M., Thiel, C., Mathieu, R., Naidoo, L., Levick, S.R., Smit, I.P.J., Asner, G.P., Schmullius, C., Assessment of the mapping of fractional woody cover in Southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images. Remote Sens. Environ. 166 (2015), 138–153.
43. Prasad, A.M., Iverson, L.R., Liaw, A., Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:2 (2006), 181–199.
44. Liaw, A., Wiener, M., Classification and regression by random Forest. R News 2:3 (2002), 18–22.
45. Jin, C., Xiao, X., Merbold, L., Arneth, A., Veenendaal, E., Kutsch, W.L., Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa. Remote Sens. Environ. 135 (2013), 189–201.
46. Schaaf, C.B., Gao, F., Strahler, A.H., Lucht, W., Li, X., Tsang, T., Strugnell, N.C., Zhang, X., Jin, Y., Muller, J.P., Lewis, P., et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83 (2002), 135–148.
47. Laurin, G.V., Liesenberg, V., Chen, Q., Guerriero, L., Del Frate, F., Bartolini, A., Coomes, D., Wilebore, B., Lindsell, J., Valentini, R., Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. Int. J. Appl. Earth Obs. Geoinf. 21 (2013), 7–16.
48. Lehmann, E.A., Caccetta, P., Lowell, K., Mitchell, A., Zhou, Z., Held, A., Milne, T., Tapley, I., SAR and optical remote sensing: assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system. Remote Sens. Environ. 156 (2015), 335–348.
49. Chen, X., Vierling, L., Rowell, E., DeFelice, T., Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sens. Environ. 91 (2004), 14–26.