Quality improvement of loblolly pine (Pinus taeda) plantation inventory GIS using Shuttle Radar Topography Mission (STRM) and the National Elevation Dataset (NED)

Heo, Joon; Kim, Jin‐Woo; Pattnaik, Sitansu; Sohn, Hong Gyoo

doi:10.1016/j.foreco.2006.06.007

Cited by 9 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In forested areas, the radar elevation estimate (or phase center) is biased by radar scattering within the canopy, enabling the estimation of tree canopy height. Studies in tropical and temperate forests have shown that SRTM can be used to estimate forest height and biomass [ Kellndorfer et al , 2004; Gillespie et al , 2006; Heo et al , 2006; Simard et al , 2006]. Because mangroves grow in tidally inundated areas, underlying topography is negligible and forest structure can be accurately measured on large scales using SRTM elevation data.…”

Section: Introductionmentioning

confidence: 99%

Landscape‐scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data

et al. 2008

View full text Add to dashboard Cite

[1] Mangroves are salt tolerant plants that grow within the intertidal zone along tropical and subtropical coasts. They are important barriers for mitigating coastal disturbances, provide habitat for over 1300 animal species and are one of the most productive ecosystems. Mozambique's mangroves extend along 2700 km and cover one of the largest areas in Africa. The purpose of this study was to determine the countrywide mean tree height spatial distribution and biomass of Mozambique's mangrove forests using Landsat ETM+ and Shuttle Radar Topography Mission (SRTM) data. The SRTM data were calibrated using the Landsat derived land-cover map and height calibration equations. Stand-specific canopy height-biomass allometric equations developed from field measurements and published height-biomass equations were used to calculate aboveground biomass of the mangrove forests on a landscape scale. The results showed that mangrove forests covered a total of 2909 km 2 in Mozambique, a 27% smaller area than previously estimated. The SRTM calibration indicated that average tree heights changed with geographical settings. Even though the coast of Mozambique spans across 16 degrees latitude, we did not find a relationship between latitude and biomass. These results confirm that geological setting has a greater influence than latitude alone on mangrove production. The total mangrove dry aboveground biomass in Mozambique was 23.6 million tons and the total carbon was 11.8 million tons.

show abstract

Section: Introductionmentioning

confidence: 99%

Landscape‐scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data

et al. 2008

View full text Add to dashboard Cite

show abstract

“…In a dense canopy forest, the scattering phase center height generally falls somewhere between the canopy top and the ground surface. Kellndorfer et al [2004], Simard et al [2006], and Heo et al [2006] have all shown that it is possible to retrieve forest height information from SRTM. Sarabandi and Lin [2000] showed that the equivalent scattering phase center height for a vegetated forest stand depends on forest characteristics such as soil moisture, tree density, and tree types as well as the InSAR parameters such as frequency, polarization, and incidence angle.…”

Section: Datamentioning

confidence: 99%

Regional distribution of forest height and biomass from multisensor data fusion

Saatchi

Heath

et al. 2010

J. Geophys. Res.

View full text Add to dashboard Cite

[1] Elevation data acquired from radar interferometry at C-band from SRTM are used in data fusion techniques to estimate regional scale forest height and aboveground live biomass (AGLB) over the state of Maine. Two fusion techniques have been developed to perform post-processing and parameter estimations from four data sets: 1 arc sec National Elevation Data (NED), SRTM derived elevation (30 m), Landsat Enhanced Thematic Mapper (ETM) bands (30 m), derived vegetation index (VI) and NLCD2001 land cover map. The first fusion algorithm corrects for missing or erroneous NED data using an iterative interpolation approach and produces distribution of scattering phase centers from SRTM-NED in three dominant forest types of evergreen conifers, deciduous, and mixed stands. The second fusion technique integrates the USDA Forest Service, Forest Inventory and Analysis (FIA) ground-based plot data to develop an algorithm to transform the scattering phase centers into mean forest height and aboveground biomass. Height estimates over evergreen (R 2 = 0.86, P < 0.001; RMSE = 1.1 m) and mixed forests (R 2 = 0.93, P < 0.001, RMSE = 0.8 m) produced the best results. Estimates over deciduous forests were less accurate because of the winter acquisition of SRTM data and loss of scattering phase center from tree-surface interaction. We used two methods to estimate AGLB; algorithms based on direct estimation from the scattering phase center produced higher precision (R 2 = 0.79, RMSE = 25 Mg/ha) than those estimated from forest height (R 2 = 0.25, RMSE = 66 Mg/ha). We discuss sources of uncertainty and implications of the results in the context of mapping regional and continental scale forest biomass distribution.

show abstract

“…Satellite-observation based methods have been widely used for the estimation of aboveground forest carbon stocks, due to the better availability, broad coverage and finer temporal resolution they offer compared to conventional field surveying (Heo et al, 2006;Hyyppä et al, 2000;Tomppo and Halme, 2004 Air-and space-borne remote sensing platforms provide continuous spatial information over large areas in contrast to field inventory where information is generally limited to plots or small areas (Petrokofsky et al, 2012). Remote sensing methods have been used for land cover discrimination to monitor reforestation and/or deforestation and to estimate aboveground forest carbon stocks.…”

Section: Introductionmentioning

confidence: 99%

Modeling the uncertainty of estimating forest carbon stocks in China

Yue¹,

Wang²,

Du³

et al. 2015

Preprint

View full text Add to dashboard Cite

Abstract. Earth surface systems are controlled by a combination of global and local factors, which cannot be understood without accounting for both the local and global components. The system dynamics cannot be recovered from the global or local controls alone. Ground forest inventory is able to accurately estimate forest carbon stocks at sample plots, but these sample plots are too sparse to support the spatial simulation of carbon stocks with required accuracy. Satellite observation is an important source of global information for the simulation of carbon stocks. Satellite remote-sensing can supply spatially continuous information about the surface of forest carbon stocks, which is impossible from ground-based investigations, but their description has considerable uncertainty. In this paper, we validated the Lund-Potsdam-Jena dynamic global vegetation model (LPJ), the Kriging method for spatial interpolation of ground sample plots and a satellite-observation-based approach as well as an approach for fusing the ground sample plots with satellite observations and an assimilation method for incorporating the ground sample plots into LPJ. The validation results indicated that both the data fusion and data assimilation approaches reduced the uncertainty of estimating carbon stocks. The data fusion had the lowest uncertainty by using an existing method for high accuracy surface modeling to fuse the ground sample plots with the satellite observations (HASM-SOA). The estimates produced with HASM-SOA were 26.1 and 28.4 % more accurate than the satellite-based approach and spatial interpolation of the sample plots, respectively. Forest carbon stocks of 7.08 Pg were estimated for China during the period from 2004 to 2008, an increase of 2.24 Pg from 1984 to 2008, using the preferred HASM-SOA method.

show abstract

Quality improvement of loblolly pine (Pinus taeda) plantation inventory GIS using Shuttle Radar Topography Mission (STRM) and the National Elevation Dataset (NED)

Cited by 9 publications

References 11 publications

Landscape‐scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data

Landscape‐scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data

Regional distribution of forest height and biomass from multisensor data fusion

Modeling the uncertainty of estimating forest carbon stocks in China

Contact Info

Product

Resources

About