Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+

The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R2) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.

[1]  Lijuan Liu,et al.  A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems , 2016, Int. J. Digit. Earth.

[2]  S. Frolking,et al.  Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure , 2009 .

[3]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[4]  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 .

[5]  L. Anderson,et al.  Application of remote sensing to understanding fire regimes and biomass burning emissions of the tropical Andes , 2014 .

[6]  S. Tjelmeland.  A model for the uncertainty around the yearly trawl-acoustic estimate of biomass of Barents Sea capelin, Mallotus villosus (Muller) , 2002 .

[7]  A. Bloom,et al.  Are Inventory Based and Remotely Sensed Above-Ground Biomass Estimates Consistent? , 2013, PloS one.

[8]  R. Nelson,et al.  Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway , 2013 .

[9]  Zhanyi Hu,et al.  High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field , 2017, ISPRS Int. J. Geo Inf..

[10]  Erkki Tomppo,et al.  The national forest inventory in China: history - results - international context , 2015, Forest Ecosystems.

[11]  Wolfgang Lucht,et al.  Global biomass mapping for an improved understanding of the CO2 balance—the Earth observation mission Carbon-3D , 2005 .

[12]  A. Huete,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995 .

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

[14]  Richard A. Birdsey,et al.  Inventory-based estimates of forest biomass carbon stocks in China: A comparison of three methods , 2010 .

[15]  Valerie A. Thomas,et al.  Approximating Prediction Uncertainty for Random Forest Regression Models , 2016 .

[16]  John C. Fry,et al.  2 Direct Methods and Biomass Estimation , 1990 .

[17]  Frédéric Baret,et al.  Intercalibration of vegetation indices from different sensor systems , 2003 .

[18]  Jing Chen,et al.  Landsat 8‐observed water quality and its coupled environmental factors for urban scenery lakes: A case study of West Lake , 2019, Water environment research : a research publication of the Water Environment Federation.

[19]  Hans Petersson,et al.  Quantifying the Model-Related Variability of Biomass Stock and Change Estimates in the Norwegian National Forest Inventory , 2014 .

[20]  Xin Pan,et al.  Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression , 2017, Remote. Sens..

[21]  Sandra A. Brown,et al.  Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .

[22]  Q. Qin,et al.  Satellite remote sensing of groundwater: Quantitative modelling and uncertainty reduction using 6S atmospheric simulations , 2004 .

[23]  Klaus Scipal,et al.  Ground Data are Essential for Biomass Remote Sensing Missions , 2019, Surveys in Geophysics.

[24]  Vicente Caselles,et al.  Validation of Landsat-7/ETM+ Thermal-Band Calibration and Atmospheric Correction With Ground-Based Measurements , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Alan Grainger,et al.  Achieving forest carbon information with higher certainty: A five-part plan , 2010 .

[26]  Lilian Blanc,et al.  Error propagation in biomass estimation in tropical forests , 2013 .

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

[28]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[29]  Corinne Le Quéré,et al.  Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks , 2007, Proceedings of the National Academy of Sciences.

[30]  Terje Gobakken,et al.  Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data , 2016, Carbon Balance and Management.

[31]  Alicia Palacios-Orueta,et al.  Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale , 2019, Remote. Sens..

[32]  D. Lu.  Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon , 2005 .

[33]  Richard Condit,et al.  Error propagation and scaling for tropical forest biomass estimates. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[34]  Shilong Piao,et al.  Forest biomass carbon stocks in China over the past 2 decades: Estimation based on integrated inventory and satellite data , 2005 .

[35]  Jaehoon Jung,et al.  Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor , 2015, Sensors.

[36]  Darrel L. Williams,et al.  Landsat-7 Long-Term Acquisition Plan: Development and Validation , 2006 .

[37]  David Saah,et al.  Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates , 2012 .

[38]  Albert Olioso,et al.  Uncertainty assessment of surface net radiation derived from Landsat images , 2016 .

[39]  J. Townshend,et al.  Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve? , 2015 .

[40]  G. Marland,et al.  The role of forest and bioenergy strategies in the global carbon cycle , 1996 .

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

[42]  Onisimo Mutanga,et al.  Remote Sensing of Above-Ground Biomass , 2017, Remote. Sens..

[43]  Thomas R. Crow,et al.  Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .

[44]  C. Samimi,et al.  Biomass estimation using Landsat-TM and -ETM+. Towards a regional model for Southern Africa? , 2004 .

[45]  I. Chenge,et al.  Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data , 2018 .

[46]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[47]  John P. R. O'Donnell,et al.  Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast , 2016, Remote. Sens..

[48]  Hao Wu,et al.  Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China , 2014 .

[49]  D. Roy,et al.  The MODIS Land product quality assessment approach , 2002 .

[50]  M. Nilsson,et al.  Combining national forest inventory field plots and remote sensing data for forest databases , 2008 .

[51]  A. Lugo,et al.  Biomass of Tropical Forests: A New Estimate Based on Forest Volumes , 1984, Science.

[52]  I. Woodhouse,et al.  Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem , 2017 .

[53]  J. Nichol,et al.  Improved forest biomass estimates using ALOS AVNIR-2 texture indices , 2011 .

[54]  A. Huth,et al.  High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle , 2017, Nature Communications.

[55]  Frédéric Achard,et al.  Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s , 2004 .

[56]  Y. J. Kaufman,et al.  The effect of subpixel clouds on remote sensing , 1987 .

[57]  M. Nilsson,et al.  A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory , 2017 .

[58]  Hitomi Murakami,et al.  Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers , 2014 .

[59]  Lawrence Ong,et al.  Landsat-8 Operational Land Imager Radiometric Calibration and Stability , 2014, Remote. Sens..

[60]  Gianni Bellocchi,et al.  Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance , 2017 .

[61]  M. S. Moran,et al.  Interpretation of vegetation indices derived from multi-temporal SPOT images , 1993 .

[62]  Ram K. Deo,et al.  Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA , 2018 .

[63]  M. Keller,et al.  Biomass estimation in the Tapajos National Forest, Brazil: Examination of sampling and allometric uncertainties , 2001 .

[64]  Guangxing Wang,et al.  Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images , 2014, European Journal of Forest Research.

[65]  K. Thome.  Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method , 2001 .

[66]  S. Fleck,et al.  Review of ground-based methods to measure the distribution of biomass in forest canopies , 2011, Annals of Forest Science.

[67]  Alan B. Anderson,et al.  Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression , 2002 .

[68]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[69]  K. O. Niemann,et al.  Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass , 2011 .

[70]  R. Houghton,et al.  Aboveground Forest Biomass and the Global Carbon Balance , 2005 .

[71]  Nischal Mishra,et al.  CONTINUOUS CALIBRATION IMPROVEMENT: LANDSAT 5 THROUGH LANDSAT 8. , 2016, Remote sensing of environment.

[72]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[73]  Jingyun Fang,et al.  Biomass carbon accumulation by Japan's forests from 1947 to 1995 , 2005 .

[74]  S. Vieira,et al.  Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil) , 2010 .

[75]  Onisimo Mutanga,et al.  A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[76]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[77]  B. Poulter,et al.  Predicting pan-tropical climate change induced forest stock gains and losses—implications for REDD , 2010 .

[78]  Eben N. Broadbent,et al.  Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review , 2014 .

[79]  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.

[80]  F. Bongers,et al.  Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models , 2011 .