Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data

Andersen, 2014, Monitoring selective logging in western Amazonia with repeat lidar flights, Remote Sens. Environ., 151, 157, 10.1016/j.rse.2013.08.049

Avitabile, 2012, Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda, Remote Sens. Environ., 117, 366, 10.1016/j.rse.2011.10.012

Chander, 2009, Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sens. Environ., 10.1016/j.rse.2009.01.007

Chen, 2016, Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference, Remote Sens. Environ., 184, 350, 10.1016/j.rse.2016.07.023

Chen, 2015, Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar, ISPRS J. Photogramm. Remote Sens., 106, 95, 10.1016/j.isprsjprs.2015.05.007

Cutler, 2012, Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions, ISPRS J. Photogramm. Remote Sens., 70, 66, 10.1016/j.isprsjprs.2012.03.011

Duncanson, 2010, Integration of GLAS and Landsat TM data for aboveground biomass estimation, Can. J. Remote Sens., 36, 129, 10.5589/m10-037

ERDAS, 2009

Fleming, 2015, Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images, Eur. J. For. Res., 134, 125, 10.1007/s10342-014-0838-y

Foody, 2003, Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions, Remote Sens. Environ., 85, 463, 10.1016/S0034-4257(03)00039-7

Kayitakire, 2006, Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery, Remote Sens. Environ., 102, 390, 10.1016/j.rse.2006.02.022

Khaleghi, 2013, Multisensor data fusion: a review of the state-of-the-art, Inf. Fusion, 14, 28, 10.1016/j.inffus.2011.08.001

Kuplich, 2005, Relating SAR image texture to the biomass of regenerating tropical forests, Int. J. Remote Sens., 26, 4829, 10.1080/01431160500239107

Labrecque, 2006, A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland, For. Ecol. Manage., 226, 129, 10.1016/j.foreco.2006.01.030

Latifi, 2012, Forest structure modeling with combined airborne hyperspectral and LiDAR data, Remote Sens. Environ., 121, 10, 10.1016/j.rse.2012.01.015

Laurin, 2014, Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data, ISPRS J. Photogramm. Remote Sens., 89, 49, 10.1016/j.isprsjprs.2014.01.001

Lee, 1994, Speckle filtering of synthetic aperture radar images: a review, Remote Sens. Rev., 8, 313, 10.1080/02757259409532206

Lu, 2005, Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon, Acta Amaz., 35, 249, 10.1590/S0044-59672005000200015

Lu, 2004, Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin, For. Ecol. Manage., 198, 149, 10.1016/j.foreco.2004.03.048

Lu, 2005, Satellite estimation of aboveground biomass and impacts of forest stand structure, Photogramm. Eng. Remote Sens., 71, 967, 10.14358/PERS.71.8.967

Lu, 2011, A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon, GIScience Remote Sens., 48, 345, 10.2747/1548-1603.48.3.345

Lu, 2012, Aboveground forest biomass with Landsat and LiDAR data and uncertainty analysis of the estimated, Int. J. For. Res., 1, 1

Lu, 2016, A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems, Int. J. Digit. Earth, 9, 63, 10.1080/17538947.2014.990526

Lu, 2005, Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon, Int. J. Remote Sens., 26, 2509, 10.1080/01431160500142145

Margolis, 2015, Combining satellite Lidar, airborne Lidar and ground plots to estimate the amount and distribution of aboveground biomass in the Boreal Forest of North America, Can. J. For. Res., 45, 838, 10.1139/cjfr-2015-0006

McRoberts, 2015, Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data, Remote Sens. Environ., 164, 36, 10.1016/j.rse.2015.02.018

Nichol, 2011, Improved biomass estimation using the texture parameters of two high-resolution optical sensors, IEEE Trans. Geosci. Remote Sens., 49, 930, 10.1109/TGRS.2010.2068574

Pohl, 1998, Multisensor image fusion in remote sensing: concepts, methods and applications, Int. J. Remote Sens., 19, 823, 10.1080/014311698215748

Qia, 2013, Biomass and carbon stocks of commonwealth forests for Central Zhejiang, For. Sci., 49, 17

Reese, 2011, C-correction of optical satellite data over alpine vegetation areas: a comparison of sampling strategies for determining the empirical c-parameter, Remote Sens. Environ., 115, 1387, 10.1016/j.rse.2011.01.019

Sarker, 2013, Forest biomass estimation from the fusion of C-band SAR and optical data using wavelet transform

Sarker, 2012, Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation, ISPRS J. Photogramm. Remote Sens., 69, 146, 10.1016/j.isprsjprs.2012.03.002

Sarker, 2013, Forest biomass estimation using texture measurements of high-resolution dual-polarization C-band SAR data, IEEE Trans. Geosci. Remote Sens., 51, 3371, 10.1109/TGRS.2012.2219872

Sun, 2015, Increasing the accuracy of mapping urban forest carbon density by combining spatial modeling and spectral unmixing analysis, Remote Sens., 7, 15114, 10.3390/rs71115114

Van Der Meer, 1997, What does multisensor image fusion add in terms of information content for visual interpretation?, Int. J. Remote Sens., 18, 445, 10.1080/014311697219187

Wang, 2009, Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images, For. Ecol. Manage., 258, 1275, 10.1016/j.foreco.2009.06.056

Xie, 2009, A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China Ecol. Modell., 220, 1810, 10.1016/j.ecolmodel.2009.04.025

Yan, 2015, Improvement of forest carbon estimation by integration of regression modeling and spectral unmixing of Landsat data, IEEE Geosci. Remote Sens. Lett., 12, 2003, 10.1109/LGRS.2015.2451091

Yuan, 2009, Study on biomass models of important commonwealth forests for Zhejiang Province, J. Zhejiang For. Technol., 29, 1

Zhang, 2013, Impacts of plot location errors on accuracy of mapping and scaling up aboveground forest carbon using sample plot and Landsat TM data, IEEE Geosci. Remote Sens. Lett., 10, 1483, 10.1109/LGRS.2013.2260719

Zhang, 2010, Multi-source remote sensing data fusion: status and trends, Int. J. Image Data Fusion, 1, 5, 10.1080/19479830903561035

Zhao, 2016, Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation, Remote Sens., 8, 469, 10.3390/rs8060469

Zheng, 2004, Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA, Remote Sens. Environ., 93, 402, 10.1016/j.rse.2004.08.008

Zhu, 2015, Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series, ISPRS J. Photogramm. Remote Sens., 102, 222, 10.1016/j.isprsjprs.2014.08.014

Zhu, 2013, Biomass conversion coefficients of Chinese fir forests of Zhejiang Province based on LULUCF greenhouse gas emission, Acta Ecol. Sin., 33, 3925, 10.5846/stxb201301230134

d'Oliveira, 2012, Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon, Remote Sens. Environ., 124, 479, 10.1016/j.rse.2012.05.014