Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder, Michael A.; White, Joanne C.; Stinson, G.; Hilker, Thomas; Kurz, Werner A.; Coops, Nicholas C.; St-Onge, Benoît; Trofymow, J. A.

doi:10.1007/s10661-009-1022-6

Cited by 21 publications

(20 citation statements)

References 58 publications

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“…However, the lack of field data in remote areas and the inconsistency of data collection methods among different management units over large regions were the major constraints to obtaining reliable large-scale biomass estimation using field-based methods [12,13]. Moreover, obtaining comprehensive, spatially complete, temporally uniform, and accurate forest inventory data was usually time-consuming and labor-intensive over huge areas and field campaigns were not well suited for detecting changes because a single measurement campaign can extend over several years in most countries, especially in developing countries with large land area [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

et al. 2015

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Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA's Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

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Section: Introductionmentioning

confidence: 99%

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

et al. 2015

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“…The ongoing rise of anthropogenic C emissions has influenced ecosystem functioning [2,3], and forests should be monitored and evaluated for changes in above ground biomass, canopy cover, and other structural parameters. Forest canopy height is an important structural metric that relates directly to stand age for even aged forests, life cycle, and C sequestration potential when combined with existing allometric relationships [4,5]. Many remote sensing approaches exist to document the structural state of forests.…”

Section: Introductionmentioning

confidence: 99%

“…Many remote sensing approaches exist to document the structural state of forests. RAdio Detection And Ranging (RADAR), Light Detection And Ranging (LiDAR), and photogrammetric methods using stereo imagery, have all been used to measure forest structure and each approach has varying accuracies and implementation costs [4,6]. We evaluated two existing stereo forest canopy height model (CHM) approaches, one without ground control points (GCPs) and one with GCPs as others have shown substantial improvement in IKONOS mapping accuracy with GCPs [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR

et al. 2014

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Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard's airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R 2 > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R 2 > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R 2 > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.

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“…Most of the methods that allow for the estimation of stand biomass or volume are based on the distribution of point height values, described by descriptive statistics such as maximum, mean, standard deviation, percentiles, or proportions (Gobakken and Naesset 2005;Hollaus et al 2007;Magnussen and Boudewyn 1998). The ability of the laser beam to pass through small openings in the forest canopy allows for a threedimensional assessment of forest structure (Coops et al 2007;Falkowski et al 2009), including height measurements (Andersen et al 2006;Hollaus et al 2006;Naesset and Økland 2002), which allows for the accurate estimation of the dominant tree height (Naesset 1997;Wulder et al 2010).…”

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confidence: 99%

Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

Tompalski

Coops

White

et al. 2015

Canadian Journal of Remote Sensing

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Site productivity, an important measure of the capacity of land to produce wood biomass, is traditionally estimated by applying species-specific, locally designed models that describe the relation between stand age and dominant height. In this article, we present an approach to derive chronosequences of stand age and height estimates from remotely sensed data to develop site productivity estimates. We first utilized an annual Landsat time series to identify areas of stand replacing disturbances and to estimate the time-since-disturbance, a proxy for stand age. Airborne laser scanning data were used to provide estimates of dominant height for these stands. Nonlinear regression was used to fit a site productivity guide curve for stands aged 7 to 32 years. Existing and developed productivity models, together with remote sensing and inventory data as inputs, were used to validate the site productivity model in three different comparisons. Site productivity was overestimated by 0.70 m (RMSE = 5.55 m) relative to existing forest inventory estimates; further, 89% of remote sensing estimates were within ±1 derived site class of the forest inventory estimates. We conclude that the presented approach is suitable for estimating site productivity for young stands in areas that lack wall-to-wall forest inventory data. Résumé. Le potentiel du site, une mesure importante de la capacité des terresà produire de la biomasse de bois, est traditionnellement estimé en appliquant des modèles spécifiques d'espèces, conçus localement, qui décrivent la relation entre l'âge du peuplement et la hauteur dominante. Dans cet article, nous présentons une approche pour dériver des séquences chronologiques d'estimations de l'âge du peuplement et de la hauteurà partir de données de télédétection pourétablir des estimations du potentiel du site. Nous avons d'abord utilisé une série temporelle annuelle Landsat pour identifier les zones de perturbations menant au remplacement des peuplements et pour estimer le tempsécoulé depuis la perturbation, un estimateur pour l'âge du peuplement.Des données laser aéroportées ontété utilisées pour fournir des estimations de la hauteur dominante de ces peuplements. Une régression non linéaire aété utilisée pour obtenir une courbe de potentiel du site pour les peuplementsâgés de 7à 32 ans. Les modèles de productivité existants ainsi que des données de télédétection et d'inventaire ontété utilisés comme entrées pour valider le modèle du potentiel du siteà partir de trois comparaisons différentes. Le potentiel du site aété surestimé de 0,70 m (RMSE = 5,55 m) par rapport aux estimations existantes d'inventaires forestiers. De plus, 89% des estimations de télédétectionétaientà ±1 classe dérivée du site des estimations de l'inventaire forestier. Nous concluons que l'approche présentée est appropriée pour estimer le potentiel du site pour les jeunes peuplements dans les zones avec des données incomplètes d'inventaire forestier.

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Implications of differing input data sources and approaches upon forest carbon stock estimation

Cited by 21 publications

References 58 publications

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR

Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

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