Sensitivity analysis for leaf area index (LAI) estimation from CHRIS/PROBA data

Abstract

Sensitivity analyses were conducted for the retrieval of vegetation leaf area index (LAI) from multi-angular imageries in this study. Five spectral vegetation indices (VIs) were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy (CHRIS/PROBA) images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that performance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hopefully improve the performance of VIs in LAI estimation, which will require further investigation.

Keywords

CHRIS/PROBA / LAI / sensitivity / vegetation index / vegetation type

References

[1] Verrelst J, Schaepman M E, Koetz B, Kneubuhler M (2008). Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote sensing of environment, 112: 2341–2353

[2] Rautiainen M, Lang M, Mottus M, Kuusk A, Nilson T, Kuusk J, Lukk T (2008). Multi-angular reflectance properties of a hemiboreal forest: an analysis using CHRIS PROBA data. Remote sensing of environment, 112(5): 2627–2642

[3] Abdou W A, Pilorz S H, Helmlinger M C, Conel J E, Diner D J, Bruegge C J, Martonchik J V, Gatebe C K, King M D, Hobbs P V (2006). Sua pan surface bidirectional reflectance: a case study to evaluate the effect of atmospheric correction on the surface products of the Multi-angle Imaging Spectro Radiometer (MISR) during SAFARI 2000. IEEE Trans Geosci Rem Sens, 44(7): 1699–1706

[4] Baret F, Guyot G (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ, 35(2−3): 161–173

[5] Bonan G B (1995). Land-atmosphere interactions for climate system models: coupling biophysical, biogeochemical, and ecosystem dynamical processes. Remote Sens Environ, 51(1): 57–73

[6] Chen J M, Black T A (1992). Defining leaf area index for non-flat leaves. Plant Cell Environ, 15(4): 421–429

[7] Chen J M, Pavlic G, Brown L, Cihlar J, Leblac S G, White H P, Hall R J, Peddle D R, King D J, Trofymow J A, Swift E, van der Sanden J, Pellikka P K E (2002). Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high resolution satellite imagery and ground measurement. Remote Sens Environ, 80(1): 165–184

[8] Cohen W B, Maiersperger T K, Gower S T, Turner D P (2003). An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sens Environ, 84(4): 561–571

[9] Colombo R, Dario B, Dante F, Carlo M M (2003). Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens Environ, 86(1): 120–131

[10] Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C (2008). Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens Environ, 112(5): 2592–2604

[11] Duan A W (1996). Measuring leaf area index of crop colony. Irrigation and Drainage Systems, 15: 50–53

[12] Hyer E J, Goetz S J (2004). Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation: assessments from a boreal forest site. Agric Meteorol, 122(3−4): 157–174

[13] Fan W J, Xu X R, Liu X C, Yan B Y, Cui Y K (2010). Accurate LAI retrieval method based on PROBA/CHRIS data. Hydrol Earth Syst Sci, 14(8): 1499–1507

[14] Fernandes R, Butson C, Leblanc S, Latifovic R (2003). Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data. Can J Rem Sens, 29(2): 241–258

[15] Fernández N, Paruelo J M, Delibes M (2010). Ecosystem functioning of protected and altered Mediterranean environments: a remote sensing classification in Doñana, Spain. Remote Sens Environ, 114(1): 211–220

[16] Ganguly S, Schull M A, Samanta A, Shabanov N V, Milesi C, Nemani R R, Knyazikhin Y, Myneni R B (2008). Generating vegetation leaf area index earth system data record from multiple sensors. Part1: Theory. Remote Sens Environ, 112(12): 4333–4343

[17] Gonsamo A, Pellikka P (2012). The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS J Photogramm Remote Sens, 70: 15–25

[18] Gower S T, Kucharik C J, Norman J M (1999). Direct and indirect estimation of leaf area index, fAPAR and net production of terrestrial ecosystem. Remote Sens Environ, 70(1): 29–51

[19] Gu Z J, Ju W M, Liu Y B, Li D Q, Fan W L (2012). Applicability of spectral and spatial information from IKONOS-2 Imagery in retrieving Leaf Area Index of forests in the urban area of Nanjing, China. J Appl Remote Sens, 6(1): 063556-1

[20] Gu Z J, Shi X Z, Li L, Yu D S, Liu L S, Zhang W T (2011). Using multiple radiometric correction images to estimate leaf area index. Int J Remote Sens, 32(24): 9441–9454

[21] Heiskanen J (2006). Tree cover and height estimation in the Fennoscandian tundra−taiga transition zone using multiangular MISR data. Remote Sens Environ, 103(1): 97–114

[22] Houborg R, Soegaard H, Boegh E (2007). Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sens Environ, 106(1): 39–58

[23] Jin Y F, Gao F, Schaaf C B, Li X W, Strahler A H, Bruegge C J, Martonchik J V (2002). Improving MODIS surface BRDF/albedo retrieval with MISR multiangle observation. IEEE Trans Geosci Rem Sens, 40(7): 1593–1604

[24] Kaufman Y J, Tanre D (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. Geoscience and Remote Sensing, IEEE Transactions on, 30(2): 261–270

[25] McAllister D M, Valeo C A (2007). Robust new method for the remote estimation of LAI in montane and boreal forests. Int J Remote Sens, 28(8): 1891–1905

[26] Nolin A W (2004). Towards retrieval of forest cover density over snow from the Multi-angle Imaging SpectroRadiometer (MISR). Hydrol Processes, 18(18): 3623–3636

[27] Pearson R L, Miller L D (1972). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proceedings of the Eighth International symposium on Remote Sensing of the Environment, VIII: 1355–1379

[28] Pinty B, Widlowski J L, Gobron N, Verstraete M M, Diner D J (2002). Uniqueness of multiangular measurements. I: an indicator of subpixel surface heterogeneity from MISR. IEEE Trans Geosci Rem Sens, 40(7): 1560–1573

[29] Pocewicz A, Vierling L A, Lentile L B, Smith R (2007). View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest. Remote Sens Environ, 107(1−2): 322–333

[30] Qi J, Chehbouni A, Huete A R, Kerr Y H, Sorooshian S (1994). A modified soil adjusted vegetation index. Remote Sens Environ, 48(2): 119–126

[31] Rautiainen M, Stenberg P, Nilson T, Kuusk A (2004). The effect of crown shape on the reflectance of coniferous stands. Remote Sens Environ, 89(1): 41–52

[32] Richardson A J, Wiegand C L (1977). Distinguishing vegetation from soil background information. American Society for Photogrammetric and Remote Sensing, 43(12): 1541–1552

[33] Ringrose S, Matheson W, Wolski P, Huntsman-Mapila P (2003). Vegetation cover trends along the Botswana Kalahari transect. J Arid Environ, 54(2): 297–317

[34] Rouse J W, Haas R H, Schell J A, Deering D W, Harlan J C (1974). Monitoring the vernal advancement of retrogradation of natural vegetation. Greenbelt, MD: NASA/GSFC (Type III, Final Report), 371

[35] Running S W, Nemani R, Peterson D L, Band L E, Potts D F, Pierce L L, Spanner M A (1989). Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology, 70(4): 1090–1101

[36] Smith B, Knorr W, Widlowski J L, Pinty B, Gobron N (2008). Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. For Ecol Manage, 255(12): 3985–3994

[37] Vohland M, Mader S, Dorigo W (2010). Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL. Int J Appl Earth Obs Geoinf, 12(2): 71–80

[38] Wu X X, Gu Z J, Luo H, Shi X Z, Yu D S (2014). Analyzing forest effects on runoff and sediment production using Leaf Area Index. Journal of Mountain Science, 11(1): 119–130

Acknowledgements

This research was funded by the National Natural Science Foundation of China ( Grant No. 401071281), the Natural Science Foundation of Jiangsu Province (No. BK20131078), the Qing Lan Project of Jiangsu Provincial Department of Education, and the Natural Science Foundation of Jiangsu Provincial Department of Education, China (10KJD170005). The authors are very grateful to the anonymous referees for their comments given for the improvement of the manuscript.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg