Estimation and Multiscale Transformation of Aboveground Biomass: An HGSU-Oriented Approach Based on Multisource Data

Research works on aboveground biomass (AGB) estimation have received attention for a long time. However, most existing research works were based on pixels at respective scale, and relatively few studies focused on estimation and multiscale transformation of AGB. We, therefore, developed an innovative object-oriented approach to estimate and transform AGB under multiple scales. First, AGB-correlated spectral, structural, and geographic indicators were derived from multisource data. Subsequently, multiresolution segmentation technology was performed to produce homogeneous geography and spectrum units (HGSUs) at different scales. Finally, AGB at each scale was retrieved based on HGSUs and Random Forest (RF) algorithm. Besides, the utilities of nonspectral variables in modeling were further evaluated. Results showed that the HGSU-oriented approach was effective and advantageous to achieve the AGB estimation and multiscale transformation based only on the same dataset with few user-defined parameters. Structural and geographic variables, especially soil type, vegetation species, and CHM, played important roles in modeling, while the contribution of spectral variables decreased with the increasing scale in general. The HGSUs combined multiple pieces of information such as spectra, texture, vegetation height, soil type, slope, elevation, and land use, and provided a more detailed segmentation, a faster stability speed with increasing regression trees, and a higher accuracy than those based on common image objects segmented only by spectral indicators. Results also evidenced that the RF regression model had the capability to ingest mixed data. This study supplemented the existing AGB estimation research works especially for shorter vegetation in coastal areas (relative to forests), and the proposed approach was promising in larger regional scales.

[1]  O. Mutanga,et al.  Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP , 2012 .

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

[3]  Fiona Cawkwell,et al.  Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Nandin-Erdene Tsendbazar,et al.  Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal , 2014 .

[5]  Wuming Zhang,et al.  An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation , 2016, Remote. Sens..

[6]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[7]  Paulo Mesquita,et al.  Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia , 2015 .

[8]  A. Sousa,et al.  Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images , 2017 .

[9]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

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

[11]  Dieu Tien Bui,et al.  Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran) , 2018, Remote. Sens..

[12]  Nancy F. Glenn,et al.  Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning , 2018 .

[13]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[14]  Claudia Notarnicola,et al.  Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..

[15]  H. Elsenbeer,et al.  Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis , 2008 .

[16]  John S. Oldow,et al.  Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment , 2013 .

[17]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[18]  Fausto W. Acerbi-Junior,et al.  Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[20]  Lien T. H. Pham,et al.  Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms , 2017 .

[21]  C. Woodcock,et al.  Forest biomass estimation over regional scales using multisource data , 2004 .

[22]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[23]  O. Mutanga,et al.  Estimating standing biomass in papyrus (Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression , 2014 .

[24]  Rupesh Shrestha,et al.  Aboveground Biomass Estimates of Sagebrush Using Terrestrial and Airborne LiDAR Data in a Dryland Ecosystem , 2015 .

[25]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  O. Mutanga,et al.  Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels , 2014 .

[28]  L. D. Miller,et al.  Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .

[29]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Dan Zhao,et al.  Land cover mapping and above ground biomass estimation in China , 2016, IGARSS.

[31]  S. Vincenzi,et al.  Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy , 2011 .

[32]  M. Vastaranta,et al.  Predicting individual tree attributes from airborne laser point clouds based on the random forests technique , 2011 .

[33]  Huanjun Liu,et al.  Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images , 2018, Precision Agriculture.

[34]  Ruben Van De Kerchove,et al.  Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[35]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[36]  Heather Reese,et al.  Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest , 2015, Remote. Sens..

[37]  Kitsanai Charoenjit,et al.  Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand , 2015 .

[38]  David Gwenzi,et al.  Estimating Tree Crown Area and Aboveground Biomass in Miombo Woodlands From High-Resolution RGB-Only Imagery , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  A. M. O. Sousa,et al.  Pinus pinea above ground biomass estimation with very high spatial resolution satellite images , 2017 .

[40]  Florian Hartig,et al.  Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass , 2014 .

[41]  Xiaolin Zhu,et al.  Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series , 2015 .

[42]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[43]  Bertrand Michel,et al.  Correlation and variable importance in random forests , 2013, Statistics and Computing.

[44]  Florian Hartig,et al.  Stratified aboveground forest biomass estimation by remote sensing data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[45]  Hannah M. Cooper,et al.  Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data , 2018 .

[46]  Alicia Troncoso Lora,et al.  A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables , 2015, Neurocomputing.

[47]  Shao-kun Li,et al.  Estimation of Wheat Agronomic Parameters using New Spectral Indices , 2013, PloS one.

[48]  Joseph D. White,et al.  Aboveground biomass of naturally regenerated and replanted semi-tropical shrublands derived from aerial imagery , 2016, Landscape and Ecological Engineering.

[49]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[50]  Pablo J. Zarco-Tejada,et al.  Temporal and Spatial Relationships between within-field Yield variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery , 2005 .

[51]  Kenneth B. Pierce,et al.  Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches , 2010 .

[52]  Hui Qing Liu,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Onisimo Mutanga,et al.  Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms , 2014, Sensors.

[54]  T. Kajisa,et al.  Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia , 2009, Journal of Forest Research.

[55]  Ke Wang,et al.  Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery , 2016 .

[56]  Hideki Saito,et al.  Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data , 2018, Remote. Sens..