Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

a b s t r a c t Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R 2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion. Published by Elsevier B.V.

[1]  Sylvain G. Leblanc,et al.  Biomass measurements and relationships with Landsat‐7/ETM+ and JERS‐1/SAR data over Canada's western sub‐arctic and low arctic , 2009 .

[2]  D. Lu The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[3]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[4]  R. Hall,et al.  Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .

[5]  R. Houghton,et al.  Mapping Russian forest biomass with data from satellites and forest inventories , 2007 .

[6]  Steve W. Lyon,et al.  Changes in Catchment‐Scale Recession Flow Properties in Response to Permafrost Thawing in the Yukon River Basin , 2010 .

[7]  James J. Simpson,et al.  Long-term climate patterns in Alaskan surface temperature and precipitation and their biological consequences , 2002, IEEE Trans. Geosci. Remote. Sens..

[8]  Stacy A. Drury,et al.  Fire history and fire management implications in the Yukon Flats National Wildlife Refuge, interior Alaska , 2008 .

[9]  A. Rogers,et al.  Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices , 2004 .

[10]  Janne Heiskanen,et al.  Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data , 2005 .

[11]  S. Goetz,et al.  Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ , 2008, Environmental Research Letters.

[12]  M. Flood,et al.  LiDAR remote sensing of forest structure , 2003 .

[13]  L. Monika Moskal,et al.  Fusion of LiDAR and imagery for estimating forest canopy fuels , 2010 .

[14]  Ben Bond-Lamberty,et al.  Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba , 2002 .

[15]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  Li Zhang,et al.  Integrating modelling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River Basin, Alaska , 2008, Int. J. Digit. Earth.

[17]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[18]  C. Racine,et al.  Observations of Thermokarst and Its Impact on Boreal Forests in Alaska, U.S.A. , 2000 .

[19]  Evan S. Kane,et al.  Aboveground Biomass Equations for the Trees of Interior Alaska , 2007 .

[20]  M. Dobson,et al.  The use of Imaging radars for ecological applications : A review , 1997 .

[21]  J. Randerson,et al.  Recovery of Aboveground Plant Biomass and Productivity After Fire in Mesic and Dry Black Spruce Forests of Interior Alaska , 2008, Ecosystems.

[22]  H. Balzter Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR) , 2001 .

[23]  Mats Nilsson,et al.  Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass , 2002 .

[24]  Robert J. Ross,et al.  Wood handbook : wood as an engineering material , 2010 .

[25]  J. Lacaux,et al.  Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal , 2007 .

[26]  M. D. Nelson,et al.  Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information , 2008 .

[27]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[28]  John S. Kimball,et al.  Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing , 2006 .

[29]  S. Goetz,et al.  Importance of biomass in the global carbon cycle , 2009 .

[30]  Van Wagner The Line Intersect Method in Forest Fuel Sampling , 1968 .

[31]  E. Kasischke,et al.  Fire, Global Warming, and the Carbon Balance of Boreal Forests , 1995 .

[32]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

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

[34]  Robert D. Hollister,et al.  PLANT RESPONSE TO TEMPERATURE IN NORTHERN ALASKA: IMPLICATIONS FOR PREDICTING VEGETATION CHANGE , 2005 .

[35]  Y. Ouma,et al.  A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data , 2006 .

[36]  E. Kasischke,et al.  Recent changes in the fire regime across the North American boreal region—Spatial and temporal patterns of burning across Canada and Alaska , 2006 .

[37]  W. Landman Climate change 2007: the physical science basis , 2010 .

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

[39]  Mathias Schardt,et al.  EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements , 2010 .

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

[41]  Alisa L. Gallant,et al.  Ecoregions of Alaska , 1995 .

[42]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[43]  P. Moran Notes on continuous stochastic phenomena. , 1950, Biometrika.

[44]  Mary Beth Parent,et al.  The Browning of Alaska's Boreal Forest , 2010, Remote. Sens..

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

[46]  B. Wylie,et al.  Analysis of Dynamic Thresholds for the Normalized Difference Water Index , 2009 .

[47]  Howard E. Epstein,et al.  Controls over intra-seasonal dynamics of AVHRR NDVI for the Arctic tundra in northern Alaska , 2004 .

[48]  R. Geary,et al.  The Contiguity Ratio and Statistical Mapping , 1954 .

[49]  Herman H. Shugart,et al.  Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach , 2005 .

[50]  Thuy Le Toan,et al.  Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets , 2004 .

[51]  Ramon C. Littell,et al.  SAS® System for Regression , 2001 .

[52]  M. Castillo-Santiago,et al.  Estimation of tropical forest structure from SPOT-5 satellite images , 2010 .

[53]  Shuguang Liu,et al.  Carbon Balance and Management , 2008 .

[54]  Leslie A. Viereck,et al.  The Alaska vegetation classification. , 1992 .

[55]  Michael A. Wulder,et al.  Estimating forest canopy height and terrain relief from GLAS waveform metrics , 2010 .

[56]  M. Sturm,et al.  The evidence for shrub expansion in Northern Alaska and the Pan‐Arctic , 2006 .

[57]  M. Hardisky The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies , 2008 .

[58]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[59]  Ramon C. Littell,et al.  SAS System for Regression,Third Edition , 2000 .

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

[61]  James E. McMurtrey,et al.  Temporal relationships between spectral response and agronomic variables of a corn canopy , 1981 .

[62]  Kazuhito Ichii,et al.  Satellite-Based Modeling of the Carbon Fluxes in Mature Black Spruce Forests in Alaska: A Synthesis of the Eddy Covariance Data and Satellite Remote Sensing Data , 2010 .

[63]  Giles M. Foody,et al.  Mapping the biomass of Bornean tropical rain forest from remotely sensed data , 2001 .

[64]  Yuri Shur,et al.  Observations of Thermokarst and Its Impact on Boreal Forests in Alaska, U.S.A. , 2000 .

[65]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .