Spatial Distribution Of Forest Biomass Research Articles

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory (FRI) data, and SPOT Vegetation (SPOT-VGT) normalized difference vegetation index (NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China (with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg (1 Pg = 1015 g), with an average of 77.2 Mg ha−1, during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map.

Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest (RF) and support vector machine were adopted to estimate above ground biomass (AGB) using widely used Landsat imagery in the region, especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.

Spatial distribution of forest biomass in Brazil’s state of Roraima, northern Amazonia

Forest biomass is an important variable for calculating carbon stocks and greenhouse gas emissions from deforestation and forest fires in Brazilian Amazonia. Its spatial distribution has caused controversy due to disagreements over the application of different calculation methodologies. Standardized networks of forest surveys provide an alternative to solve this problem. This study models the spatial distribution and original total stock of forest biomass (Aboveground+Belowground+Fine and coarse litter) in Brazil’s state of Roraima, taking advantage of data from georeferenced forest surveys in the region. Commercial volume (bole volume) from surveys was expanded to total biomass. Kriging techniques were used to model the spatial distribution of biomass stocks and generate a benchmark map. All results were associated with phytophysiognomic groups, climatic regions and land uses (protected areas; agricultural use). We estimate forest in the state of Roraima to have an original biomass stock of 6.32×109Mg. Forest biomasses in areas with shorter dry seasons were higher as compared to forests in regions with longer dry seasons. The original vegetation in protected areas, independent of phytophysiognomic group, has higher biomass compared to areas currently under agricultural use. Protected areas support 65.8% of Roraima’s stock of forest biomass, indicating an important potential role in REDD projects for conservation of forest carbon. Information on spatial distribution of biomass stocks at a more refined scale is needed to reduce uncertainties about the regional character of carbon pools in Amazonia.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

The Interpolation Method for Estimating the Above-Ground Biomass Using Terrestrial-Based Inventory

This paper examined several methods for interpolating biomass on logged-over dry land forest using terrestrial-based forest inventory in Labanan, East Kalimantan and Lamandau, Kota Wringing Barat, Central Kalimantan. The plot-distances examined was 1,000−1,050 m for Labanan and 1,000−899m for Lawanda. The main objective of this study was to obtain the best interpolation method having the most accurate prediction on spatial distribution of forest biomass for dry land forest. Two main interpolation methods were examined: (1) deterministic approach using the IDW method and (2) geo-statistics approach using Kriging with spherical, circular, linear, exponential, and Gaussian models. The study results at both sites consistently showed that the IDW method was better than the Kriging method for estimating the spatial distribution of biomass. The validation results using chi-square test showed that the IDW interpolation provided accurate biomass estimation. Using the percentage of mean deviation value (MD(%)), it was also recognized that the IDWs with power parameter (p) of 2 provided relatively low value , i.e., only 15% for Labanan, East Kalimantan Province and 17% for Lamandau, Kota Wringing Barat Central Kalimantan Province. In general, IDW interpolation method provided better results than the Kriging, where the Kriging method provided MD(%) of about 27% and 21% for Lamandau and Labanan sites, respectively.

Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China

Quantifying the spatial pattern of large-scale forest biomass can provide a general picture of the carbon stocks within a region and is of great scientific and political importance. The combination of the advantages of remote sensing data and field survey data can reduce uncertainty as well as demonstrate the spatial distribution of forest biomass. In this study, the seventh national forest inventory statistics (for the period 2004–2008) and the spatially explicit MODIS Land Cover Type product (MCD12C1) were used together to quantitatively estimate the spatially-explicit distribution of forest biomass in China (with a resolution of 0.05°, ~5600 m). Our study demonstrated that the calibrated forest cover proportion maps allow proportionate downscaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map. The total stock of forest biomass in China was 11.9 Pg with an average of 76.3 Mg ha−1 during the study period; the high values were located in mountain ranges in northeast, southwest and southeast China and were strongly correlated with forest age and forest density.

Methodology for the large scale assessment of the technical power potential of forest biomass: Application to the province of New Brunswick, Canada

In this study, a methodology is presented for the large scale assessment of forest biomass available over a territory with multiple land ownerships and to determine the technical power potential of this biomass for the commercial cogeneration of heat and power in industrial-sized combined heat and power plants (CHP). The forest biomass assessment of merchantable stem-wood is based on Annual Allowable Cut (AAC) data, while the biomass assessments of the other tree components are estimated using biomass allometric equations and biomass expansion factors. An application of the methodology is made to the province of New Brunswick, Canada. Results show that the total annual potential harvest of forest biomass in the study area is 15.5 Mt green weight (GT) at harvest from which approximately 63% would come from merchantable wood, 27% from residual biomass and 10% from bark. In term of electric and thermal power potential, the findings indicate that if all the forest biomass harvested annually in the province was to be used as fuel input in dedicated CHP plants, a total of 1.2 GW of electricity and 3 GW of thermal heat could be produced. In regards to the provincial spatial distribution of forest biomass, results from a cumulative perspective show that 22% of all forest biomass would come from a 25 km radius of the CHP plant sites identified in this study, 73% within a 50 km radius and 100% within a 125 km radius.

Landscape-scale variation in the structure and biomass of the hill dipterocarp forest of Sumatra: Implications for carbon stock assessments

One of the first steps in estimating the potential for reducing emissions from deforestation and forest degradation (REDD) initiatives is the proper estimation of the carbon components. There are still considerable uncertainties about carbon stocks in tropical rain forest, coming essentially from poor knowledge of the quantity and spatial distribution of forest biomass at the landscape level.We evaluate the influence of site and topography on forest structure, biomass and carbon stock over a forested landscape in Sumatra. Sixteen sites were selected across a landscape of ca. 1000km2, and within each site, a network of small 0.1ha plots was laid down perpendicular to the major topographical gradient. Structural parameters for trees with diameter bigger or equal to 10cm and major life forms were recorded for each plot. The total sampled area was 70.2ha.The above-ground biomass (AGBM) in the 16 sites ranged from 271±19 to 478±38Mgha−1, with a mean of 361±7Mgha−1. This value is similar to the global ‘tropical wet forest’ estimate of Keith et al. (2009), but 23% higher than the biome default value given in the IPCC (2006) guidelines. Plots on acid tuff were the most distinctive in having fewer and smaller trees, with a resulting low AGBM. The number of stems was low, but the average tree diameter was larger on the nutrient-rich young volcanic andesitic soils. Landscape-scale variability of forest structure and dynamics was considerable. Within sites, there was little variation of mean height or mean diameter of the trees, but significant differences in stem density, basal area and AGBM between land facets. Both topography and geology appeared to affect the dynamics and the biomass of the forest.At the same time, our findings confirm the need for caution in extrapolating data from small plots to larger spatial scales when estimating carbon stocks for a given forest type or a region. Our results for C stock values for Sumatra lowland and hill dipterocarp forests (135–240Mgha−1, with a mean of 180Mgha−1) showed higher variability in C stock values at landscape level than generally reported in the literature. Based on our results, stratified sampling is recommended for biomass and carbon estimation. Within an error range of 6–8% of the AGBM, a minimum area of 4–6ha should be sufficient to estimate biomass with satisfactory accuracy at the landscape scale. Networks of several small plots across landscape are preferable to fewer larger plots, providing that the sampling is representative of the land facets in the area.

Distribution of aboveground live biomass in the Amazon basin

AbstractThe amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land‐cover and land‐use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site‐specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old‐growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300 Mg ha−1 here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300 Mg ha−1. Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200 Mg ha−1. The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and belowground biomass, is 86 Pg C with ±20% uncertainty.

The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates

The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land-use change. We compared several estimates of forest biomass for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. We asked three questions. First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? Amazonian forests (including dead and below-ground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modeling of forest recovery following observed stand-replacing disturbances (the approach used in this research), and estimation of aboveground biomass from airborne or satellite-based instruments sensitive to the vertical structure plant canopies.

The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates

AbstractThe amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land‐use change. In the present contribution several estimates of forest biomass are compared for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. Three questions were posed: First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? The answer to the first two questions is that estimates of biomass for Brazil's Amazonian forests (including dead and belowground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modelling of forest recovery following observed stand‐replacing disturbances, and estimation of aboveground biomass from airborne or satellite‐based instruments sensitive to the vertical structure plant canopies.