Distribution Of Forest Biomass Research Articles

Disentangling contributions of allometry, species composition and structure to high aboveground biomass density of high-elevation forests

The high elevation forests of the Central Himalayas include stands with an exceptional abundance of aboveground biomass, standing as some of the most carbon-rich forests in the entire Himalayan range and beyond. National or regional models, however, are inadequate in capturing this extreme concentration of biomass observed in select forest inventory plots. We aimed to identify factors contributing to the significant variability of biomass and thus, aboveground forest carbon in the high mountain forests of Nepal. By comparing the utilization of pantropical and Nepali allometric equations, we evaluated whether the elevational gradients in forest biomass were robust to changes in the allometric models used to predict plot biomass. Irrespective of the model employed, the highest biomass values were found in high elevation plots. The diverse distribution of forest biomass reinforces established size-density relationships, attributing observed variations to the interplay between tree size and density. Our results contribute valuable insights into these high biomass forests, affirming observed elevational gradients, quantifying the impact of tree species and structure, and elucidating factors influencing biomass sites concerning allometric models.

Mapping the Spatial Distribution of Aboveground Biomass in China’s Subtropical Forests Based on UAV LiDAR Data

Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these limitations, we propose an improved approach that combines three-dimensional (3D) forest structure data collected using unmanned aerial vehicle light detection and ranging (UAV LiDAR) technology with ground measurements to apply a binary allometric growth equation for estimating and mapping the spatial distribution of AGB in subtropical forests of China. Additionally, we analyze the influence of terrain factors such as elevation and slope on the distribution of forest biomass. Our results demonstrate a high accuracy in estimating tree height and diameter at breast height (DBH) using LiDAR data, with an R2 of 0.89 for tree height and 0.92 for DBH. In the study area, AGB ranges from 0.22 to 755.19 t/ha, with an average of 121.28 t/ha. High AGB values are mainly distributed in the western and central-southern parts of the study area, while low AGB values are concentrated in the northern and northeastern regions. Furthermore, we observe that AGB in the study area exhibits an increasing trend with altitude, reaching its peak at approximately 1650 m, followed by a gradual decline with further increase in altitude. Forest AGB gradually increases with slope, reaching its peak near 30°. However, AGB decreases within the 30–80° range as the slope increases. This study confirms the effectiveness of using UAV LiDAR for estimating and mapping the spatial distribution of AGB in complex terrains. This method can be widely applied in productivity, carbon sequestration, and biodiversity studies of subtropical forests.

Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management

The ecological value of tropical forests in water conservation district has been of great interest because of their rich vegetation types and higher biomass density than any other land cover types, it is urgent to evaluate the ecological value of tropical forests in water conservation district. However, the monitoring of tropical forests in water conservation district is faced with many problems, such as high forest density, complexity and diversity of the forest structure, complex topography and climate conditions, and the difficulty of access for investigators. In order to solve the above difficulties, this study combined 3D point cloud reconstruction based on Unmanned Aerial Vehicle - Structure from Motion (UAV-SfM) technology with forest type classification based on the Convolutional Neural Network (CNN) method, combined with a small amount of forest permanent sample plot survey data, to accurately evaluate the forest biomass distribution and forest biodiversity in water conservation district. The results show that the overall classification accuracy of the 20 forest types in water conservation district based on the CNN method is 0.61, the overall Kappa coefficient is 0.59, and the conditional Kappa coefficient is concentrated in the range of 0.43–0.85. The Root Mean Square Error (RMSE) of the plane measurement of UAV-SfM technology is 0.432 m, and the RMSE of the elevation measurement is 0.989 m, the effect of this UAV technology in tropical forest monitoring is superior. Using the techniques mentioned above, this study can effectively and accurately monitor and evaluate the biomass distribution and biodiversity of tropical forests in the water conservation district. Based on the precision forest ecological monitoring data, this study can develop a scientific and reasonable sustainable forest management plan for the water conservation district according to the distribution of forest biomass and biodiversity. The combination of UAV-SfM technology and the CNN method is an innovative attempt, and the integration of UAV and artificial intelligence technology solves practical problems faced by sustainable forest management. UAV and artificial intelligence will also provide an important foundation for forest ecological environment sustainability assessment research.

Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi’an, China

Investigating the spatial distribution of urban forest biomass and its potential influencing factors would provide useful insights for configuring urban greenspace. Although China is experiencing an unprecedented scale of urbanization, the spatial pattern of the urban forest biomass distribution as a critical component in the urban landscape has not been fully examined. Using the geographic detector method, this research examines the impacts of four geographical factors (GFs)—dominant tree species, forest categories, land types, and age groups—on the aboveground biomass distribution of urban forests in 1480 plots in Xi’an, China. The results indicate that (1) the aboveground biomass and four GFs show obvious heterogeneity regarding their spatial distribution in Xi’an; (2) the dominant tree species and age group which impacts the patterns of aboveground biomass are the primary GFs, with the independent q value (a statistic metric used to quantify the impacts of GFs in this study) reaching 0.595 and 0.202, respectively, while the forest category and land type were weakly linked to the spatial variation of aboveground biomass, with a q value of 0.087 and 0.076, respectively; and (3) the interactions among these four GFs also tend to contribute to the distribution pattern of aboveground biomass. The interactions between GFs achieved a larger impact than the sum of impacts that were independently obtained from the factors. Our results showed that the method of using a geographical detector is a useful tool in the urban area, and can reveal the driver pattern of aboveground biomass and provide a reference for city planning and management.

Stand Structure and Abiotic Factors Modulate Karst Forest Biomass in Southwest China

Understanding the driving factors of forest biomass are critical for further understanding the forest carbon cycle and carbon storage management in karst forests. This study aimed to investigate the distribution of forest aboveground biomass (AGB) and the effects of stand structural and abiotic factors on AGB in karst forests in Southwest China. We established a 25 ha plot and sampled all trees (≥1 cm diameter) in a subtropical mixed evergreen–deciduous broadleaf forest. We mapped the forest biomass distribution and applied a variation of partitioning analysis to examine the topographic, stand structural, and spatial factors. Furthermore, we used structural equation models (SEM) to test how these variables directly and/or indirectly affect AGB. The average AGB of the 25 ha plot was 73.92 Mg/ha, but that varied from 3.22 to 198.11 Mg/ha in the 20 m × 20 m quadrats. Topographic, stand structural, and spatial factors together explained 67.7% of the variation in AGB distribution. The structural variables (including tree density and the diameter at breast height (DBH) diversity) and topographic factors (including elevation, VDCN (vertical distance to channel network), convexity, and slope) were the most crucial driving factors of AGB in the karst forests. Structural equation models indicated that elevation, tree density, and DBH diversity directly affected AGB, and elevation also indirectly affected AGB through tree density and DBH diversity. Meanwhile, AGB was indirectly influenced by VDCN, convexity, and slope. The evaluation of stand structural and abiotic drivers of AGB provides better insights into the mechanisms that play a role in carbon storage in karst forests, which may assist in improving forest carbon management.

Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this article proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient ( $r$ ) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg $\cdot $ ha−1 to 26.8 Mg $\cdot $ ha−1. The difference tests showed that the changes were extremely significant ( $p = 0$ ). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.

MAPPING AND MODELLING ABOVEGROUND WOODY BIOMASS AND CARBON STOCK IN SAL (SHOREA ROBUSTA GAERTN. F.) FORESTS OF DOON VALLEY USING GEOSPATIAL TECHNIQUES

Abstract. Information on the quantitative and qualitative distribution of forest biomass is helpful for effective forest management. Besides its quantitative use, Biomass plays a twin role by acting as a carbon source and sinks but its long-term carbon-storing ability is of considerable importance which is helpful in lessening global warming and climate change impacts. The present study was done for mapping aboveground woody biomass (Bole) (AGWB) of Shorea robusta (Gaertn.f.) forests in Doon valley by establishing relationships between field measured data, satellite data derived variables and geostatistical techniques. Landsat 8 Operational Land Imager (OLI) data was used in preparing the forest homogeneity map (forest type and density). 55 sampling plots of 0.1 ha were laid across the Doon Valley using stratified random sampling. Correlations were established between Landsat 8 OLI derived variables and field measured data and were evaluated. Field measured biomass has got the maximum correlation with NDVI (0.7553) and it was further used for carrying out multivariate kriging (Cok) for biomass prediction map. Prediction errors for the AGWB were lowest for exponential model with RMSE = 66.445 Mg/ha, Average Standard Error = 71.07694 Mg/ha and RMSS = 0.95097. Carbon is calculated as 47% of the biomass value.AGWB was ranged from 163.381 to 750.025 Mg/ha and Carbon from 76.789 to 352.512 Mg/ha. Cokriging was found as a better alternative as compared to direct radiometric relationships for the spatial distribution of the AGWB of Shorea robusta (Gaertn.f.) forests and this study would be helpful in better forest management planning and research purposes.

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.

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.

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.

Watershed-scale forest biomass distribution in a perhumid temperate rainforest as driven by topographic, soil, and disturbance variables

Temperate rainforests are the most carbon dense forest ecosystem on the planet, with C stocks several times higher than most other forested biomes. While climatic and disturbance drivers of these C stocks are relatively well explored, the spatial distribution of those stocks at the scale of entire watersheds is less well known, particularly in perhumid rainforests where research has been minimal. This study explored biomass distributions across an entire watershed simultaneously, from ocean to glacial icefields, in Southeast Alaska. Utilizing LiDAR and ground surveys, biomass was modelled throughout the landscape and distributions are described statistically. The dominant driver of biomass distributions at this scale (controlling for elevation) was the flow of water through the landscape: areas of higher water accumulation typically had low biomass (often <10 Mg·ha–1), whereas well-drained areas supported biomass approaching 950 Mg·ha–1. This relationship was strong at all elevations; only riparian locations (typically well-drained soils) maintained high biomass at low slopes. Exposure to stand-replacing disturbances, often a dominant driver, was only a minor factor. This work emphasizes the importance of water in temperate rainforests and the potentially significant impacts of changes to biomass given changes in precipitation (both increasing and decreasing) due to global climate change.

Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data

Abstract Estimating the change in the magnitude and distribution of forest biomass is important for monitoring carbon dynamics and understanding the implications on the terrestrial carbon cycle. In this study, we assessed the capacity of multi-temporal airborne Light Detection and Ranging (LiDAR) data to estimate forest biomass dynamics at a mixed subtropical forest of southeast China, and analyzed the magnitude and spatial patterns of the above ground biomass (AGB) change over a 6-year period. To do so, we evaluated different approaches to estimate change in AGB, specifically direct (i.e., predicting biomass change using the differences in LiDAR metrics) and indirect (i.e., modeling biomass for each point in time and predicting the change as their differences) approaches. Once models were developed, the change in AGB was mapped across the study site and examined in relation to forest type and age. Our results demonstrated that the direct approach (CV-R2 = 0.63, CV-rRMSE = 25.64%) produced more accurate estimates in change of AGB than the indirect approach (CV-R2 = 0.59, CV-rRMSE = 28.35%). Canopy height metrics of delta mean height (∆ hmean) and a number of upper percentile heights (i.e., ∆ h75 and ∆ h95), calculated from the LiDAR first returns, were found to be most sensitive to changes in biomass, whereas metrics based on maximum canopy height or canopy density had less predictive power. Furthermore, differences in point cloud densities did not significantly impact the estimations of AGB and change of AGB at the plot-scale (30 × 30 m). Spatial extrapolation of the AGB change indicated that, in general, most of the forest area had an overall gain in biomass. The middle-aged stands (typically dominated by Quercus acutissima between 41 and 60 years old) show marked growth of biomass (median ∆ AGB = 2.29 Mg ha− 1 year− 1), followed by mature (typically dominated by Pinus massoniana between 31 and 60 years old) (median ∆ AGB = 1.72 Mg ha− 1 year− 1) and young stands (typically consisting of regenerated broadleaved trees younger than 40 years old) (median ∆ AGB = 1.48 Mg ha− 1 year− 1), whereas over-mature stands (typically dominated by Cunninghamia lanceolata older than 35 years) show the lowest growth (median ∆ AGB = 0.76 Mg ha− 1 year− 1). This study demonstrates benefits of using multi-temporal LiDAR data to help identify specific areas for management interventions, to enhance forest productivity, maintain biomass stocks and optimize carbon sequestration.

Long-term effects on distribution of forest biomass following different harvesting levels in the northern Rocky Mountains

With increasing public demand for more intensive biomass utilization from forests, the concerns over adverse impacts on productivity by nutrient depletion are increasing. We remeasured the 1974 site of the Forest Residues Utilization Research and Development in northwestern Montana to investigate long-term impacts of intensive biomass utilization on aspects of site productivity. The historical experiment was implemented in a western larch (Larix occidentalis Nutt.) forest at three biomass utilization levels (high, medium, and low) combined with prescribed post-harvest burning treatments (burned and unburned) under three regeneration cuttings (clearcut, group selection, and shelterwood). The experiment has two replicates and was designed as a split-plot design with an imbalanced manner. Regenerated tree height and diameter at breast height, shrub root collar diameter, and soil properties (C, N, and total organic matter) of the forest floor and mineral soil layers were measured. Regenerated tree, shrub, and total aboveground biomass and total C, N, and organic matter contents of the soil layers were calculated. Results indicated that total organic matter pools at the ecosystem level were similar across regeneration cutting treatments, and there were no differences among the utilization treatments for either aboveground biomass production or soil properties 38years after harvest. Minor differences observed among treatments seemed to originate from differences in regeneration dynamics and responses to burning treatment. Our results indicate that site productivity in this forest type was unaffected by these biomass utilization levels.

Topographic and biotic factors determine forest biomass spatial distribution in a subtropical mountain moist forest

A mechanistic understanding of factors driving forest biomass will help stewards manage carbon storage in forests. We examined the potential biotic and topographic factors in regulating subtropical forest carbon storage. We utilized data from the Badagongshan 25ha (500×500m) forest dynamics plot to examine the factors regulating the spatial variation of large trees and forest biomass. We mapped forest biomass and large tree biomass distributions and applied variation partitioning analysis to examine a suite of topographic and biotic factors related to the distributions. The average biomass of the 25ha plot is 252.7Mg/ha but varied substantially from 39.16 to 1024.53Mg/ha in the 20×20m quadrats. Overall, large tree (diameter at breast height ⩾25cm) density accounted for 71% of variation in forest biomass distribution. Variance partitioning showed that biotic, topographic and spatial factors altogether explained 64.8% and 57.5% of the variation in the distribution of forest biomass and large tree density, respectively. Fractions of variance explained by the convexity and topographic wetness index (TWI) were much larger than other topographic variables in both distributions. For biotic variables, stem density and wood specific gravity were important in predicting forest biomass and large tree density distributions. Both biomass and large tree density showed an increasing trend with increasing convexity, stem density and wood specific gravity, but decreased as TWI increased. Convexity and TWI explained more variation among topographic variables, indicating that water deficiency may play an important role in shaping the distribution of forest biomass and large tree density. In conclusion, the crucial relationship between forest biomass and large tree density distribution should attract more attention, and suggests a mechanistic control of forest carbon storage that may help provide options in forest carbon management.

Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots.

Whether the fraction of total forest biomass distributed in roots, stems, or leaves varies systematically across geographic gradients remains unknown despite its importance for understanding forest ecology and modeling global carbon cycles. It has been hypothesized that plants should maintain proportionally more biomass in the organ that acquires the most limiting resource. Accordingly, we hypothesize greater biomass distribution in roots and less in stems and foliage in increasingly arid climates and in colder environments at high latitudes. Such a strategy would increase uptake of soil water in dry conditions and of soil nutrients in cold soils, where they are at low supply and are less mobile. We use a large global biomass dataset (>6,200 forests from 61 countries, across a 40 °C gradient in mean annual temperature) to address these questions. Climate metrics involving temperature were better predictors of biomass partitioning than those involving moisture availability, because, surprisingly, fractional distribution of biomass to roots or foliage was unrelated to aridity. In contrast, in increasingly cold climates, the proportion of total forest biomass in roots was greater and in foliage was smaller for both angiosperm and gymnosperm forests. These findings support hypotheses about adaptive strategies of forest trees to temperature and provide biogeographically explicit relationships to improve ecosystem and earth system models. They also will allow, for the first time to our knowledge, representations of root carbon pools that consider biogeographic differences, which are useful for quantifying whole-ecosystem carbon stocks and cycles and for assessing the impact of climate change on forest carbon dynamics.

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.

산림 바이오매스를 산정하기 위한 위성영상의 분석

This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.