Mapping of the spatial distribution of carbon storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest in Sagada,Mt. Province,Philippines

The objective of this study is to map the spatial distribution of the aboveground biomass (AGB, tC/ha) storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest of Sagada, Mt. Province, Philippines by integrating Landsat image and the forest cover map. The data was obtained from 66 plots that were established in the different Benguet pine stands in Sagada. The AGB was estimated using the Digital Numbers (DN) and Normalized Difference Vegetation Index (NDVI) values (with filter and with no filter). The estimated aboveground biomass (AGB) density of the Benguet pine was determined to be 249.66 tonnes/ha corresponding to 112.35 tonnesC/ha.     

Spatial variation and prediction of forest biomass in a heterogeneous landscape

Large areas assessments of forest biomass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sampled forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest plots (one plot per 2.85 km2) and measured an array of ecological (vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history) and physiographic drivers (elevation, potential soil moisture and solar radiation, proximity to the coast) of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 km. Regression tree (RT) models showed that both physiographic and ecological factors influenced biomass distribution. Across randomly selected sample densities (sample size 112 to 280), ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had a positive effect on biomass, indicating the importance of topographically-mediated energy and moisture on plant growth and biomass accumulation. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study demonstrates how statistical and geospatial modeling can be used to discriminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.     

应用中分辨率遥感数据和地理信息系统改进森林材积和生物量评估策略(英文)

应用遥感技术、地理信息系统和野外观测数据,评估了热带森林环境下地上生物量和木材蓄积量。用于模拟森林属性的这些数据具有地理特异性和高度的不确定性,因此,这方面需要开展更多的研究工作。选取了16个试样地带1460个样地,测定树木胸径及其他用于评估生物量的其他森林属性。本实验在印尼加里曼丹东部的热带雨林开展。应用现有的胸径-生物量公式来评估地上生物量密度。估测值在研究区修正的GIS地图上重叠显示,计算各种地被物的生物量密度。用样品数据子集表达遥感方法来形成地上生物量和材积线性方程模型。皮尔森相关统计检验采用ETM条带反射率、植被指数、图像变化图层、主成分分析条带、缨帽变换、灰度共生矩阵纹理特征和DEM数据作为预报值。在显著的遥感数据中形成了两个线性模型。为了分析每块地被物总的生物量和材积量,对2000年到2003年卫星ETM图进行了预处理、最大似然估计法分类和主体分析过滤。遥感方法获得的结果表明:材积量为(158±16)m3·hm-2,地上生物量为(168±15)t·hm-2;而野外测定和地理信息系统估计的结果分别是材积量为(157±92)m3·hm-2、地上生物量为(167±94)t·hm-2。用多个瞬间ETM数据评估了从2000年到2003年间的生物量丰富度动态,结果发现这一时期总生物量呈略微的下降趋势。遥感技术评估的生物量丰富度低于地理信息系统和野外测定的结果。前一种测定方法估计2000年和2003年总生物量分别是10.47Gt和10.3Gt,而后一种则估计11.9Gt和11.6Gt。还发现,灰度共生矩阵纹理特征与材积量和生物量之间存在较强的相关性。图7表9参43。     

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.     

Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil)

Live aboveground biomass (AGB) is an important source of uncertainty in the carbon balance from the tropical regions in part due scarcity of reliable estimates of live AGB and its variation across landscapes and forest types. Studies of forest structure and biomass stocks of Neotropical forests are biased toward Amazonian and Central American sites. In particular, standardized estimates of aboveground biomass stocks for the Brazilian Atlantic forest are rarely available. Notwithstanding the role of environmental variables that control the distribution and abundance of biomass in tropical lowland forests has been the subject of considerable research, the effect of short, steep elevational gradients on tropical forest structure and carbon dynamics is not well known. In order to evaluate forest structure and live AGB variation along an elevational gradient (0–1100 m a.s.l.) of coastal Atlantic Forest in SE Brazil, we carried out a standard census of woody stems ≥4.8 cm dbh in 13 1-ha permanent plots established on four different sites in 2006–2007. Live AGB ranged from 166.3 Mg ha⁻¹ (bootstrapped 95% CI: 144.4,187.0) to 283.2 Mg ha⁻¹ (bootstrapped 95% CI: 253.0,325.2) and increased with elevation. We found that local-scale topographic variation associated with elevation influences the distribution of trees >50 cm dbh and total live AGB. Across all elevations, we found more stems (64–75%) with limited crown illumination but the largest proportion of the live AGB (68–85%) was stored in stems with highly illuminated or fully exposed crowns. Topography, disturbance and associated changes in light and nutrient supply probably control biomass distribution along this short but representative elevational gradient. Our findings also showed that intact Atlantic forest sites stored substantial amounts of carbon aboveground. The live tree AGB of the stands was found to be lower than Central Amazonian forests, but within the range of Neotropical forests, in particular when compared to Central American forests. Our comparative data suggests that differences in live tree AGB among Neotropical forests are probably related to the heterogeneous distribution of large and medium-sized diameter trees within forests and how the live biomass is partitioned among those size classes, in accordance with general trends found by previous studies. In addition, the elevational variation in live AGB stocks suggests a large spatial variability over coastal Atlantic forests in Brazil, clearly indicating that it is important to consider regional differences in biomass stocks for evaluating the role of this threatened tropical biome in the global carbon cycle.     

Biomass estimations in forests of different disturbance history in the Atlantic Forest of Rio de Janeiro,Brazil

Tropical forests are large reservoirs of biomass and there is a need for information on existing carbon stocks in these ecosystems and especially the effects of logging on these stocks. Reliable estimates of aboveground biomass stocks within the Atlantic Forest are rarely available. Past human disturbance is an important factor affecting forest structure variation and biomass accumulation among tropical forest ecosystems. To support the efforts of improving the quality of estimations of the current and future biomass carbon storage capacity of this disturbed forest region we tested a non-experimental small scale approach to compare the aboveground tree biomass (AGB) of forest sites. Three sites with known disturbance histories have been investigated: complete cut down, selective logging and conservation since 70 years. The woody plant community (dbh ≥ 10 cm) was censused and canopy openness in conjunction with leaf area index has been obtained by hemispherical photographs at each site. Estimates of aboveground tree biomass have been carried out using an allometric equation for moist tropical forests already applied for the study area. Additionally, a FAO standard equation has been employed for crosschecking our results. We identified significant differences in recent AGB of the three compared forest sites. With 313 (±48 Mg ha⁻¹) the highest AGB-values have been found in the preserved forest area within a National Park, followed by 297 (±83) Mg ha⁻¹ at the former clear cut site. Lowest AGB has been calculated for the area with past selective logging: 204 (±38) Mg ha⁻¹. Values calculated with the FAO standard equation showed the same trend but at a lower AGB level. Our results based an a small scale approach suggest that biomass productivity can recover in a forest which was completely cleared 60 years ago to reach AGB values up to a level that almost represents the situation in a preserved forest. Selective logging may slow down AGB accumulation and the effect is measurable after several decades.     

Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Conversion of tropical forests to oil palm plantations in Malaysia and Indonesia has resulted in large-scale environmental degradation, loss of biodiversity and significant carbon emissions. For both countries to participate in the United Nation’s REDD (Reduced Emission from Deforestation and Degradation) mechanism, assessment of forest carbon stocks, including the estimated loss in carbon from conversion to plantation, is needed. In this study, we use a combination of field and remote sensing data to quantify both the magnitude and the geographical distribution of carbon stock in forests and timber plantations, in Sabah, Malaysia, which has been the site of significant expansion of oil palm cultivation over the last two decades. Forest structure data from 129 ha of research and inventory plots were used at different spatial scales to discriminate forest biomass across degradation levels. Field data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) imagery to both discriminate oil palm plantation from forest stands, with an accuracy of 97.0% (κ = 0.64) and predict AGB using regression analysis of HV-polarized PALSAR data (R² = 0.63, p < .001). Direct estimation of AGB from simple regression models was sensitive to both environmental conditions and forest structure. Precipitation effect on the backscatter data changed the HV prediction of AGB significantly (R² = 0.21, p < .001), and scattering from large leaves of mature palm trees significantly impeded the use of a single HV-based model for predicting AGB in palm oil plantations. Multi-temporal SAR data and algorithms based on forest types are suggested to improve the ability of a sensor similar to ALOS PALSAR for accurately mapping and monitoring forest biomass, now that the ALOS PALSAR sensor is no longer operational.     

Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests: II. An integrated system for modelling forest growth and scenario assessment within the wet tropics bioregion

Regional model analyses of forest growth are critical for capturing global aspects of tropical rainforest carbon exchange. This research presents the development of a multi-model approach for assessing forest growth and biomass accumulation within the wet tropics bioregion (WTB) based on 10 years of available data and existing model parameter sets. The Tropical Rainforest Growth (TRG) model system employs the 3-PG and 3-PGS models to account for both old-growth rainforest and forest regeneration from seedlings in response to human-induced and natural disturbances. Above-ground biomass (AGB) stocks of the mature forest throughout the WTB for 2000 were estimated to be ∼202 t C/ha. Replacement of areas of old-growth with commercial timber plantations decreased overall AGB stocks to approximately 146 t C/ha. However, plantation carbon accumulation rates were higher than the mature rainforest, representing their potential to accumulate more biomass over a longer analysis time period. As tropical cyclones may significantly alter the carbon stocks of old-growth rainforests, the effect of tropical cyclone Rona on the WTB was assessed. The cyclone had a minimal impact on total AGB stocks within the region, yet these systems are an important factor to be considered in carbon and forest regeneration modelling activities in the tropics. The TRG system is an advanced modelling tool providing a rapid process-based assessment of biomass stocks and accumulation dynamics within Australia's tropical rainforest bioregion and has the potential for application in tropical forest ecosystems at both national and international levels.     

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 = 10~(15) 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 fineresolution biomass map.     

A Nondestructive,Remote Sensing-Based Estimation of the Economic Value of Aboveground Temperate Forest Biomass (Case Study: Hyrcanian Forests,Nowshahr-Iran)

ABSTRACT

Background: Traditional field-based methods of measurement of biomass and carbon storage face difficulty in collecting time-consuming and expensive, suggests the use of remote sensing-based techniques. It estimates the economic value of the aboveground biomass (AGB) using satellite remote sensing across the Hyrcanian forests of Iran. Methods: The Landsat-8 OLI sensor data were combined with field-based allometric information of 186 circular sample plots. The AGB was calculated at the plot level using the collected data and specific volumetric mass for species in the studied area. It was followed by calculating the carbon storage using a 50% carbon coefficient and the photosynthesis equation at the forest parcel level. Model results using the random forest and support vector machines. The carbon sequestration value was calculated with USD 25.3 as a shadow value of carbon in 2014 and using the replacement cost approach. Results: The highest performances achieved by RF for biomass, carbon storage and the carbon storage value (Iranian Rials of 0.67% and 16%, respectively). The value was derived once at the plot level of 12.22 million IRR (370.43 USD) per ha. In addition, at the parcel level, which resulted in an estimated value of 12.87 million IRR (390.24 USD) per ha.     

基于仿真大光斑激光雷达和多层感知器的森林地上生物量估算模型构建

森林是全球重要的陆地生态系统,各国普遍采用地面样地调查的方法评估其资源量和生物量。随着激光雷达技术的发展,采用星载大光斑激光雷达估算大区域森林地上生物量将成为另一种选择。为探索利用大光斑激光雷达估算森林地上生物量的方法,提出了一种基于仿真大光斑激光雷达和多层感知器的森林地上生物量估算模型。比较仿真大光斑激光雷达波形参数13种组合拟合森林地上生物量的效果后,认为多层感知器的估测精度高于多元线性回归。与样地实测地上生物量相比,多元线性回归估测结果的偏差范围为-34.96~23.28t/hm²,多层感知器估测结果的偏差范围更小,为-19.09~20.19t/hm²。因此,多层感知器估测森林地上生物量的效果优于多元线性回归。     

Forest biomass estimated from MODIS and FIA data in the Lake States: MN, WI and MI, USA

This study linked the Moderate Resolution Imaging Spectrometerand USDA Forest Service, Forest Inventory and Analysis (FIA)data through empirical models established using high-resolutionLandsat Enhanced Thematic Mapper Plus observations to estimateaboveground biomass (AGB) in three Lake States in the north-centralUSA. While means obtained from larger sample sizes in FIA datasetscan be used as reference numbers over large scales, remote sensing(RS)-based observations have the ability to reflect spatialvariation of properties of interest within a given area. Thus,combining two national on-going datasets may improve our abilityto accurately estimate ecological properties across large scales.Using standard and consistent data sources can reduce uncertaintyand provide more comparable results at both temporal and spatialdimensions. We estimated total forest AGB in the region was1479 T_g (10¹² g, dry weight) in 2001 with mean AGB value of95 mg ha^–1 ranging from 4 to 411 mg ha^–1 (within95 per cent percentiles). Mixed forests featured 66 per centof the total AGB while deciduous and evergreen forests contained32 and 2 per cent of the total AGB, respectively, at 1-km pixelresolution. Spatially, AGB values increased from north-westto south-east in general. The RS-based estimates indicated agreater range in AGB variations than the FIA data. Deciduousforests were more variable (both in absolute and relative terms)than evergreen forests. The standard deviation of AGB for deciduousforests was 137 mg ha^–1, or a coefficient of variationof 92 per cent, that for evergreen forests was 24 mg ha^–1,or a coefficient of variation of 37 per cent.     

Species-specific allometric equations for improving aboveground biomass estimates of dry deciduous woodland ecosystems

Allometric equations are important for quantifying biomass and carbon storage in terrestrial forest ecosystems.However,equations for dry deciduous woodland ecosystems,an important carbon sink in the lowland areas of Ethiopia have not as yet been developed.This study attempts to develop and evaluate species-specific allometric equations for predicting aboveground biomass(AGB)of dominant woody species based on data from destructive sampling for Combretum collinum,Combretum molle,Combretum harotomannianum,Terminalia laxiflora and mixed-species.Diameter at breast height ranged from 5 to 30 cm.Two empirical equations were developed using DBH(Eq.1)and height(Eq.2).Equation 2 gave better AGB estimations than Eq.1.The inclusion of both DBH and H were the best estimate biometric variables for AGB.Further,the equations were evaluated and compared with common generic allometric equations.The result showed that our allometric equations are appropriate for estimating AGB.The development and application of empirical species-specific allometric equations is crucial to improve biomass and carbon stock estimation for dry woodland ecosystems.     

基于Landsat TM数据估算雷竹林地上生物量

结合Landsat TM遥感数据和雷竹林样地调查数据,采用偏最小二乘回归法(PLS)建立雷竹林地上生物量估算模型,利用该模型估算临安市雷竹林地上部分生物量。结果表明:雷竹单株地上部分生物量与胸径及雷竹林地上部分生物量与株数之间都呈极显著相关(P<0.01);通过PLS-Bootstrap法筛选自变量能够提高模型精度;模型预测的雷竹林地上生物量均方根误差为3.45t·hm-2,满足大范围估算的精度要求;临安市雷竹林地上生物量为13～25t·hm-2,均值为19.52t·hm-2。     

Effect of species grouping and site variables on aboveground biomass models for lowland tropical forests of the Indo-Malay region

Key message

This study assessed the effect of ecological variables on tree allometry and provides more accurate aboveground biomass (AGB) models through the involvement of large samples representing major islands, biogeographical zones and various succession and degradation levels of natural lowland forests in the Indo-Malay region. The only additional variable that significantly and largely contributed to explaining AGB variation is grouping based on wood-density classes.

Context

There is a need for an AGB equation at tree level for the lowland tropical forests of the Indo-Malay region. In this respect, the influence of geographical, climatic and ecological gradients needs to be assessed.

Aims

The overall aim of this research is to provide a regional-scale analysis of allometric models for tree AGB of lowland tropical forests in the Indo-Malay region.

Methods

A dataset of 1300 harvested trees (5 cm ≤ trunk diameter ≤ 172 cm) was collected from a wide range of succession and degradation levels of natural lowland forests through direct measurement and an intensive literature search of principally grey publications. We performed ANCOVA to assess possible irregular datasets from the 43 study sites. After ANCOVA, a 1201-tree dataset was selected for the development of allometric equations. We tested whether the variables related to climate, geographical region and species grouping affected tree allometry in the lowland forest of the Indo-Malay region.

Results

Climatic and major taxon-based variables were not significant in explaining AGB variations. Biogeographical zone was a significant variable explaining AGB variation, but it made only a minor contribution on the accuracy of AGB models. The biogeographical effect on AGB variation is more indirect than its effect on species and stand characteristics. In contrast, the integration of wood-density classes improved the models significantly.

Conclusion

Our AGB models outperformed existing local models and will be useful for improving the accuracy on the estimation of greenhouse gas emissions from deforestation and forest degradation in tropical forests. However, more samples of large trees are required to improve our understanding of biomass distribution across various forest types and along geographical and elevation gradients.

     

Variation in vegetation structure and soil properties related to land use history of old-growth and secondary tropical dry forests in northwestern Mexico

The objective of this study was to compare the vegetation structure and soil properties among old-growth tropical dry forests representing three categories of grazing intensity by cattle (light, moderate and heavy grazing) and a category of 20–30-yr-old secondary forest experiencing occasional grazing in a locality in northwestern Mexico. Within each forest type, three 0.1 ha plots located in different grazing ranges (“potreros”) were used as replicates. All woody plants (stem ≥ 2.0 cm diameter at 1.30 m height, DBH) were identified and measured in each plot. Mean basal area and above-ground biomass (AGB) were significantly higher in the moderately grazed old-growth forest. Species density was significantly lower in the secondary forest, where a leguminous tree species was dominant. Accumulation of AGB after 20–30 yr of secondary forest recovery accounted for 43% of the old-growth forest AGB. Soil properties varied among forest categories but did not follow a consistent pattern: mean total N and organic matter content were highest in the old-growth forest with moderate grazing; cation exchange capacity (CEC) was similar among the three old-growth forests categories, but it was significantly lower in the secondary forest compared to the old-growth forest with low grazing. Canonical Correspondence Analysis showed that CEC was highly correlated with the actual species distribution in the study area, especially with Acacia cochliacantha the dominant species of the secondary forest category. Resprouting capacity of the persisting species in the old-growth forests experiencing chronic disturbance could have contributed to the maintenance of some of the structural characteristics of a mature forest. Tropical secondary forests seem to accumulate AGB relatively fast, reflecting their potential for carbon storage and provision for other ecosystem services; therefore, they deserve urgent protection measures.     

森林地上生物量合成孔径雷达技术反演研究综述

森林地上生物量是森林获取能量的重要体现,准确掌握其动态变化对了解森林生长过程、实现生态系统的有效修复具有重要意义。合成孔径雷达技术（SAR）具有全天时、全天候的特点,在森林地上生物量（AGB）反演中极具潜力。星载SAR技术的发展,使得SAR数据源日益丰富,利用极化SAR技术、干涉SAR技术、极化干涉SAR技术、层析SAR技术、多频SAR技术可以实现对森林不同维度的观测,从而提供森林不同维度的信息,进而提高采用SAR技术进行森林AGB反演的能力。文中介绍星载SAR传感器及可获取SAR数据的现状,在此基础上重点阐述基于后向散射信息、极化信息、干涉信息、极化干涉信息、层析信息、多频SAR信息在森林AGB反演中的现状及存在问题,展望了SAR技术在森林AGB反演中的发展趋势。     

Spatially explicit assessment of carbon stocks of a managed forest area in eastern Germany

The Kyoto-protocol permits the accounting of changes in forest carbon stocks due to forestry. Therefore, forest owners are interested in a reproducible quantification of carbon stocks at the level of forest management units and the impact of management to these stocks or their changes. We calculated the carbon stocks in tree biomass and the organic layer including their uncertainties for several forest management units (Tharandt forest, Eastern Germany, 5,500 ha) spatially explicit at the scale of individual stands by using standard forest data sources. Additionally, soil carbon stocks along a catena were quantified. Finally, carbon stocks of spruce and beech dominated stands were compared and effects of thinning intensity and site conditions were assessed. We combined forest inventory and data of site conditions by using the spatial unions of the shapes (i.e., polygons) in the stand map and the site map. Area weighted means of carbon (C) stocks reached 10.0 kg/m2 in tree biomass, 3.0 kg/m2 in the organic layer and 7.3 kg/m2 in mineral soil. Spatially explicit error propagation yielded a precision of the relative error of carbon stocks at the total studied area of 1% for tree biomass, 45% for the organic layer, and 20% for mineral soil. Mature beech dominated stands at the Tharandt forest had higher tree biomass carbon stocks (13.4 kg/m2) and lower organic layer carbon stocks (1.8 kg/m2) compared to stands dominated by spruce (11.6, 3.0 kg/m2). The difference of tree biomass stocks was mainly due to differences in thinning intensity. The additional effect of site conditions on tree carbon stocks was very small. We conclude that the spatially explicit combination of stand scale inventory data with data on site conditions is suited to quantify carbon stocks in tree biomass and organic layer at operational scale.     

Assessment of tree diversity and above-ground biomass in coffee agroforest dominated tropical landscape of India's Central Western Ghats

The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richness,composition and above-ground biomass(AGB) of trees,shrubs and herbs from 96 sample plots of 0.1 ha. Totals of83 species from 26 families were recorded across the landuses. Tree species richness, diversity and composition were significantly higher in evergreen forest(EGF) than in other land-uses. Similarly, stem density and basal area were greater in EGF compared to other land-uses. Detrended correspondence analysis(DCA) yielded three distinct groups along the land-use intensities and rainfall gradient on the first and second axes, respectively. The first DCA axis accounted for 45% and second axis for 35% of the total variation in species composition. Together the first two axes accounted for over 2/3 of the variation in species composition across land-use types. Across the land-uses,AGB ranged from 58.6 Mg ha-1 in rubber plantation to327.3 Mg ha-1 in evergreen forest. Our results showed that species diversity and AGB were negatively impacted bythe land-use changes. We found that coffee agroforests resembled natural forest and mixed species plantation in terms of tree diversity and biomass production, suggesting that traditional coffee farms can help to protect tree species, sustain smallholder production and offer opportunities for conservation of biodiversity and climate change mitigation.     

Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data

Protected areas in Nigeria are important ecosystems for carbon storage. The aim of this study was to estimate and map tree aboveground biomass (TAGB) and carbon (TAGC) within a tropical forest in Nigeria. Stepwise regression analysis was implemented to develop models for predicting TAGB in the forest stand, by integrating field TAGB data with Landsat 8 OLI data. Spectral variables used in the analysis include spectral bands, vegetation indices, tasseled cap indices and principal components. Model validation was performed using independent sample plots. The results showed that incorporating more than one category of spectral variables improved the prediction of TAGB. The best-fit model was applied to map the spatial distribution of TAGB and TAGC. The TAGC was estimated as 52.3% of TAGB, based on the average carbon content of tree species derived in this study. Average TAGB and TAGC estimates for the forest stand were 373.1 ± 165.4 t ha^?1 and 194 ± 82.7 t ha^?1, respectively. Reliable estimates of TAGB and TAGC for the forest reserve were obtained. This study provides important information required to manage the forest stand for optimal carbon sequestration.