Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H C V combined with H m e a n performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone.

[1]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[2]  C. Bacour,et al.  Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .

[3]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[4]  Gail P. Anderson,et al.  Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data , 2002, Applied Imagery Pattern Recognition Workshop, 2002. Proceedings..

[5]  Dong Liang,et al.  Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[7]  Qiang Cao,et al.  Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice , 2014 .

[8]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[9]  Xiaohuan Xi,et al.  Estimating leaf area index of maize using airborne full-waveform lidar data , 2016 .

[10]  D. Xie,et al.  LAI inversion algorithm based on directional reflectance kernels. , 2007, Journal of environmental management.

[11]  Sorin C. Popescu,et al.  Fusion of lidar and multispectral data to quantify salt marsh carbon stocks , 2014 .

[12]  Mark Sanford,et al.  Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests , 2012, Remote. Sens..

[13]  Xiangguo Lin,et al.  Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments , 2014, Remote. Sens..

[14]  C. Justice,et al.  A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data , 2010 .

[15]  Fabian Ewald Fassnacht,et al.  Forest structure modeling with combined airborne hyperspectral and LiDAR data , 2012 .

[16]  J. Bryan Blair,et al.  Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion , 2011 .

[17]  J. Eitel,et al.  LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status , 2014 .

[18]  Zheng Niu,et al.  Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Ruiliang Pu,et al.  Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  Yubin Lan,et al.  Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data , 2015, Remote. Sens..

[21]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[22]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[23]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[24]  Roberta E. Martin,et al.  Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems , 2007 .

[25]  Bani K. Mallick,et al.  Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection , 2013 .

[26]  Chaoyang Wu,et al.  Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China , 2015 .

[27]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[28]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[29]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[30]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[31]  Xiaohuan Xi,et al.  Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation , 2017 .

[32]  Renata Ribeiro do Valle Gonçalves,et al.  Coffee Crop's Biomass and Carbon Stock Estimation With Usage of High Resolution Satellites Images , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  K. Itten,et al.  Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization , 2007 .

[34]  Xiaohuan Xi,et al.  Estimating FPAR of maize canopy using airborne discrete-return LiDAR data. , 2014, Optics express.

[35]  K. Moffett,et al.  Remote Sens , 2015 .

[36]  N. Broge,et al.  Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data , 2002 .

[37]  D. Roberts,et al.  Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors , 2011 .

[38]  Maria Antonia Brovelli,et al.  Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method , 2008 .

[39]  Luis Alonso,et al.  Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC) , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[40]  Qi Chen,et al.  LiDAR Remote Sensing of Vegetation Biomass , 2013 .

[41]  C. Hladik,et al.  Salt Marsh Elevation and Habitat Mapping Using Hyperspectral and LIDAR Data , 2013 .

[42]  Xiaohuan Xi,et al.  Estimation of wetland vegetation height and leaf area index using airborne laser scanning data , 2015 .

[43]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[44]  John R. Miller,et al.  Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model , 2010 .

[45]  A. Gitelson,et al.  Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm , 2014 .

[46]  D. Civco,et al.  Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh , 2008 .

[47]  R. Dubayah,et al.  Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest , 2008 .

[48]  Fan Wenjie,et al.  Retrieving crop fractional cover and LAI based on airborne Lidar data , 2011, National Remote Sensing Bulletin.

[49]  N. Broge,et al.  Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture , 2002 .

[50]  Bernhard Höfle,et al.  Radiometric Correction of Terrestrial LiDAR Point Cloud Data for Individual Maize Plant Detection , 2014, IEEE Geoscience and Remote Sensing Letters.

[51]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[52]  Valerie A. Thomas,et al.  Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach , 2006 .