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Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km2 of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km2 of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha- 1 (old-growth coast redwood) and 510 ± 120 Mg ha- 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha- 1 in Garcia-Mailliard and 140 ± 0.9 Mg ha- 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon. © 2010 Elsevier Inc. All rights reserved.

LIDAR, AN EMERGING REMOTE SENSING TECHNOLOGY THAT DIRECTLY MEASURES THE THREE-DIMENSIONAL DISTRIBUTION OF PLANT CANOPIES, CAN ACCURATELY ESTIMATE VEGETATION STRUCTURAL ATTRIBUTES AND SHOULD BE OF PARTICULAR INTEREST TO FOREST, LAND-SCAPE, [...]

The topographical advantage is the contribution of topographic and traffic conditions to the operational efficiency such as afforestation, cutting, logging, and transportation of forestland. The classification of topographical advantage is of great importance to evaluate the costs of afforestation and cutting. In this paper, slope, distance, and elevation were selected as three main factors influencing the topographical advantage of forestland according to the Expert Scoring method. The Analytic Hierarchy Process was used to determine the weight of each factor, and ArcGIS10.2 software was used for spatial analysis of terrain and traffic data. The topographical advantage was then divided by the equal-difference method. We applied this method to Lingnan forest farm in Xiuning County, China. Results showed that it was difficult for afforestation and cutting in Lingnan forest farm. This paper presents a quantitative method combining spatial analysis and the Analytic Hierarchy Process for the topographical advantage, which will provide a scientific basis for the cost-effective accounting of afforestation. [ABSTRACT FROM AUTHOR]

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004-2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics. [ABSTRACT FROM AUTHOR]

The Colorado Front Range has a large elevation gradient with deep seasonal snowpack in the mountains and limited snow accumulation in the foothills and plains. This study examines how the sources of annual peak flows (snowmelt, rainfall, mixed) change with the fraction of time snow persists on the ground, snow persistence (SP), and whether these sources have changed over time. Sources of peak flows for 20 gaging stations are estimated using a gridded rain and snow model forced with PRISM daily precipitation and both PRISM and TopoWx temperature. The mean snowmelt contribution to peak flow is highly correlated with SP (r2 = 0.86-0.90). Watersheds with SP < 0.3 (low snow, elevation <2000 m) are rainfall-dominated, and watersheds with SP > 0.7 (persistent snow, elevation >3100 m) are mostly snowmelt-dominated, with mixed sources between these thresholds. Rainfall runoff peak flows are possible at all elevations, but their likelihood declines with increasing SP. Rainfall runoff from an extreme storm in September 2013 produced the highest annual peaks at many stations, including some snowmelt-dominated watersheds. Regional Kendall trend tests indicate that the contributions of snowmelt to peak flows and total annual inputs have declined in the mixed source zone. These changes may affect hydrographs, as analyses confirm that snowmelt runoff generally produces more attenuated peaks than rainfall runoff. Discrimination of peak flow source is sensitive to input data and model structure for mixed rain and snowmelt events, and both observation and modeling research are needed to help understand potential runoff changes in these conditions. [ABSTRACT FROM AUTHOR]