Forest Volume Research Articles

Вплив природних факторів на динаміку букового (fagus sylvatica L.) пралісу Українських карпат

Міжнародні дослідження 15-річної динаміки букового пралісу проведено на 10-гектарному стаціонарному дослідному об'єкті в Угольському масиві Карпатського біосферного заповідника. Проаналізовано вплив вітровалу 2007 р. та потепління клімату на структурні елементи екосистеми пралісу. Виявлено достовірні зміни окремих показників пралісу внаслідок сильного впливу вітру: найбільші коливання характерні для густоти дерев – після втрати 10 % (у 2007 р.) вже в 2010 р. густота збільшилася на 6, а в 2015 р. – на 59 %; істотно зріс і запас мертвої деревини (відповідно на 41 та 6 %); відносне зменшення найбільшим було для кількості підросту (на 19 та 14 %); зменшення запасу деревини було вже меншим (на 7 і 4 %); зменшення суми площ поперечного перетину було неістотним (на 4 і 2 %). Аналіз реакції букового пралісу на сильний вплив вітру підтверджує гіпотезу про максимальну стійкість таких лісових екосистем до стихійних явищ, а ідентифіковані кількісні зміни дали змогу розробити принципову схему сильного впливу вітру на праліс. Дискусійним залишається питання збільшення приросту за діаметром під впливом потепління клімату – у 2010 і в 2015 рр. воно було достовірним для більшості ступенів товщини, але на його динаміку вплинув також і вітровал через зрідження намету.

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Abstract: Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m3 ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m3 ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.

Forest stem volume estimation using C-band interferometric SAR coherence data of the ERS-1 mission 3-days repeat-interval phase

Interferometric synthetic aperture radar (InSAR) coherence datasets from the 3-days phase of the European Remote Sensing Satellite (ERS-1) mission during the winter months of 1992 have been analyzed to assess the capability to retrieve forest stem volume in boreal forests. For three test sites in Sweden and Finland, coherence decreased for increasing repeat-pass interval from 3- to 12-days due to increasing temporal decorrelation. Overall strong decorrelation was observed for images acquired under unfrozen conditions or thawing conditions. Coherence was best preserved under stable frozen conditions and, in addition, short perpendicular component of the interferometric baseline (i.e., <300 m). Stem volume was estimated with the Interferometric Water Cloud Model (IWCM) and multi-temporal combination of single-image estimates. For the test site of Tuusula, Finland, data from eight 3-days image pairs yielded a relative RMSE of 28.2% for stem volumes ranging between 3 and 482 m3/ha. For the 6-days repeat-pass interval, the relative RMSE was only slightly worse (32.4%). For longer repeat-pass intervals, the relative RMSE was above 50%. This study suggests that C-band short-term coherence has potential to support retrieval of forest biomass but the prospects of Sentinel-1 coherence to estimate biomass appear to be limited to the 6-days coherence in areas characterized by stable frozen, or likewise stable dry, conditions.

Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning

Modern cut-to-length harvesters are recording information about each harvested tree, and with accurate positioning, this information can be used as field reference data, replacing manually measured reference data. In the present study, models developed from accurately positioned harvester data were compared with a reference model. A set of ∼55 000 accurately positioned trees was used as the basis for a division into 792 reference plots of 400 m2 each. A set of manually measured field plots was used for validation. Regression models were developed based on the relationship between airborne laser scanning data and the reference plot volumes. Separate models were developed for two strata: medium and high site productivity. Several modelling methods were compared, including nonparametric models; at the plot level, predictions for the validation dataset yielded RMSEs of 32%–60% for the medium productivity stratum and 19%–22% for the high productivity stratum. A reference model was fitted to the manually measured validation data in each stratum, and RMSEs of 45% and 25% were obtained for the medium and high productivity strata, respectively. The results show that the models based on the harvester data yield prediction errors at the same level as the reference model.

Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantation forests in New Zealand with RapidEye and LiDAR

In New Zealand, 30% of plantation forests are small-scale (<1000 ha) and knowledge of these forests, especially those less than 100 ha, is limited. These forests are expected to comprise more than 40% of the total harvest volume by 2020, so it is critical to understand the small-scale forest resource in order to plan effectively for marketing, harvesting, logistics and transport capacity. A remote sensing solution to small-scale forest description is necessary because conducting a comprehensive ground-based survey of those patchy forests is impractical. However, the utility of remote sensing prediction techniques for application in small-scale forests is unknown. This research evaluated two parametric models (multiple linear regression and seemingly unrelated regression) and two non-parametric models (k-Nearest Neighbour and Random Forest) models to predict stand variables (mean top height, basal area, volume and stand age) using model inputs including RapidEye-derived metrics and LiDAR-derived metrics. LiDAR-derived metrics were better at predicting all forest stand variables relative to RapidEye metrics. Combining LiDAR metrics with RapidEye metrics did not improve variable prediction results (on average 0.2% reduction in RMSE). Non-parametric models and parametric models performed similarly. Of all approaches tested in this study, multiple linear regression (MLR) using LiDAR-derived metrics was deemed to be the best performing modelling approach for predicting stand variables for small-scale plantation forests in New Zealand. MLR predicted mean top height (MTH) with a root-mean-square-error (RMSE) of 1.81 m, basal area (BA) with an RMSE of 9.92 m2 ha−1, stand volume with an RMSE of 94.93 m3 ha−1 and age with an RMSE of 2.17 years.

Dynamic Monitoring of Forest Volumes by a Feature Extraction Method

In this article, in order to improve tree volume calculation method, a measurement method based on tree information point feature extraction is proposed, the method based on image processing and binocular vision, according to the measurement result of information point change and tree growth model, achieve through the distance change of information point to study the tree volume change. The visual measurement method is compared with the traditional method, the feasibility and accuracy of the method are proven. From the results, tree volume changes through the information point feature extraction and the traditional breast diameter measurement is very similar, the maximal percentage increase is 2.570% and 2.546%, the minimum percentage increase is 0.092% and 0.068%, which shows that volume change is consistent with the results, confirmed the tree volume change scheme of visual measurement is feasible and the result is reliable, which can reduce the impact of environmental change in the manual measurement.

Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes

Abstract. Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new lidar-derived index, the large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations in forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne light detection and ranging (lidar) and ground inventory data in nine undisturbed old-growth Neotropical forests, of which four had plots large enough (1 ha) to calibrate our model. We found that the LCA for trees greater than 27 m (∼ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = −0.63 Mg ha−1). Unlike other lidar-derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear and remains unique across forest types. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter greater than 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality and other types of tropical forest disturbance and dynamics.

Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications

Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, particularly for large areas, but no thresholds that justify ignoring them have been reported. The objectives of the study were threefold: (i) to compare analytical and bootstrap estimators of model parameter covariances as the primary factors affecting prediction covariance; (ii) to estimate the contribution of residual variance to overall variance; and (iii) to estimate thresholds for residual spatial correlation that justify ignoring this component. Five datasets were used, three from Europe, one from Africa, and one from North America. The dependent variable was either forest volume or biomass and the independent variables were either Landsat satellite image bands or airborne laser scanning metrics. Three conclusions were noteworthy: (i) analytical estimators of the model parameter covariances tended to be biased; (ii) the effects of residual variance were mostly negligible; and (iii) the effects of spatial correlation on residual covariance vary by multiple factors but decrease with increasing study area size. For study areas greater than 75 km2 in size, residual covariance could generally be ignored.

Climate differentiates forest structure across a residential macrosystem

The extent of urban ecological homogenization depends on how humans build, inhabit, and manage cities. Morphological and socio-economic facets of neighborhoods can drive the homogenization of urban forest cover, thus affecting ecological and hydrological processes, and ecosystem services. Recent evidence, however, suggests that the same biophysical drivers differentiating composition and structure of natural forests can further counteract the homogenization of urban forests. We hypothesize that climate can differentiate forest structure across residential macrosystems at regional-to-continental spatial scales. To test this hypothesis, forest structure (tree and shrub cover and volume) was measured using LiDAR data and multispectral imagery across a residential macrosystem composed 1.4 million residential parcels contained in 9 cities and 1503 neighborhoods. Cities were selected along an evapotranspiration (ET) gradient in the conterminous United States, ranging from the colder continental climate of Fargo, North Dakota (ET = 464.43 mm) to the hotter subtropical climate of Tallahassee, Florida (ET = 1000.47 mm). The relative effects of climate, urban morphology, and socio-economic variables on residential forest structure were assessed by using generalized linear models. Climate differentiated forest structure of the residential macrosystem as hypothesized. Average forest cover doubled along the ET gradient (0.39–0.78 m2 m−2), whereas average forest volume had a threefold increase (2.50–8.12 m3 m−2). Forest volume across neighborhoods increased exponentially with forest cover. Urban morphology had a greater effect in homogenizing forest structure on residential parcels compared to socio-economics. Climate and urban morphology variables best predicted residential forest structure, whereas socio-economic variables had the lowest predictive power. Results indicate that climate can differentiate forest structure across residential macrosystems and may counteract the homogenizing effects of urban morphology and socio-economic drivers at city-wide scales. This resonates with recent empirical work suggesting the existence of complex multi-scalar mechanisms that regulate ecological homogenization and ecosystem convergence among cities. The study initiates high-resolution assessments of forest structure across entire urban macrosystems and breaks new ground for research on the ecological and hydrological significance of urban vegetation at subcontinental scale.

Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data

With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity (WSG) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree (WSGBase) or from global repositories such as Dryad (WSGDryad) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG (WWSG) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSGDryad, diameter at breast height (DBH) and species stem morphology (Sm) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSGBase and WSGDryad yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSGBase and WSGDryad gave an average simulated bias of 9% (S.D. = ±7) and 3% (S.D. = ±7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = ±8). We also observed that the stand-level bias obtained with WSGBase and WSGDryad decreased with total plot size and plot area. The systematic bias induced by WSGBase and WSGDryad for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations.

カラマツ及びトドマツ人工林における持続可能な伐採量の推定 : 北海道全域を対象として(会員研究発表論文)

カラマツ及びトドマツ人工林における持続可能な伐採量の推定 : 北海道全域を対象として(会員研究発表論文)

Reprint of: Why did the 1980s' reform of collective forestland tenure in southern China fail?

Both the reform of forestland tenure and timber market liberalization are important for forest resources management worldwide. This paper employs a unique provincial level panel dataset of 8 National Forest Resource Inventories and related price and investment data to estimate the effects on the forest of China's reforms of collective forestland tenure and timber prices in the 1980s. The system Generalized Method of Moments with robustness (controlling for endogeneity) and fixed effects models were used to identify the determinants of these reforms for the collective forest region of southern China. Our empirical results indicate that these two reforms jointly caused deforestation and forest degradation. As much as 13.5% of the total forest area was lost, and 14.9% of the forest volume was removed. Deforestation in the 1980s was followed with widespread negative long-term impacts on forest growth and afforested area. These policy failures suggest the important lesson that a well-conceived framework for monitoring and regulation needs to be in place for successfully implemented reforms.

中国人工林经营发展战略与对策:从追求木材产量的单一目标经营转向提升生态系统服务质量和效益的多目标经营

PDF HTML阅读 XML下载 导出引用 引用提醒 中国人工林经营发展战略与对策:从追求木材产量的单一目标经营转向提升生态系统服务质量和效益的多目标经营 DOI: 10.5846/stxb201712072201 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家国际科技合作专项项目（2015DFA31440）；国家林业公益性行业科研专项（201404201） Development strategy and management countermeasures of planted forests in China: transforming from timber-centered single objective management towards multi-purpose management for enhancing quality and benefits of ecosystem services Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:人工林是全球森林资源的重要组成部分，在木材生产、环境改善、景观建设和减缓气候变化等方面扮演着愈来愈重要的角色。尽管我国人工林面积和蓄积不断增长，但是，人工林存在质量较差、结构不尽合理、生产力不高、生态功能较弱和生态稳定性下降等问题。人工林生态系统服务功能难以满足经济社会日益增长和新时期人们对美好生活向往的多方面需求。面对未来人工林面积继续增加受到适宜发展空间的严重制约和气候变化带来的现实和潜在的影响，亟需改变和调整我国人工林经营的发展战略和对策，将从人工林面积扩张、蓄积增加转变为人工林生态系统服务的质量和效益提升。如何提高人工林生态系统服务的质量和效益，创建健康稳定、高生产力和高碳汇的人工林生态系统，既能提供高产优质木材，又能够发挥固碳减排、生物多样性保护、水源涵养和水土保持等多种生态功能，以满足经济社会发展对森林的多种新需求和林业应对气候变化的新任务，亟需探索适合我国新时期人工林生态系统可持续经营的理论和多目标经营范式。在深入分析国际和我国人工林发展历程、变化趋势、面临问题的基础上，充分汲取和借鉴国际人工林经营的理念、经验和实践成果，并结合我国现阶段人工林发展实际情况，探索人工林生态系统服务质量与效益提升的发展思路和实践途径，从多层次、多尺度定位面向生态系统服务的人工林经营对策，倡导并实施人工林生态系统适应性经营，实现人工林生态系统服务的多目标权衡与协同，为我国人工林经营的战略转变和对策创新提供决策参考与未来展望。 Abstract:As an important component of global forest resources, planted forests are playing an increasingly crucial role in timber production, environmental improvement, landscape rehabilitation and climate change mitigation. Although China has been experiencing continuous growth in the area and standing volume of planted forests, ecosystem services and functions of planted forests are still far from adequacy in meeting growing needs of people's better life expectation and rapid social-economic development. The planted forests are facing problems including poor stand quality, irrational stand structure, low productivity, and degraded ecological function. Considering the confined space available for further expansion of replanting area and the actual and potential impacts from climate change, it is necessary to adjust China's current plantation development strategy from area expansion to quality and benefits improvement, There is a need to develop an appropriate strategy and practice approach for achieving multi-purpose management such as carbon sequestration, biodiversity protection, eco-environment improvement, in response to the new demands for building a beautiful China, and the new task of forestry's role in climate change mitigation. In this paper, we discuss the possible ways and new ideas on how to upgrade China's management strategy of planted forests by analyzing the development history, future trends and existing problems of global planted forests. Useful international experiences, concepts and good practices are well incorporated into China's actual conditions and special feathers of plantation management. Some recommendations and future prospective on strategic transformation and technical innovation for enhancing the quality and benefits of multiple ecosystem services of planted forests in China were also proposed. Adaptive ecosystem management of planted forests is highly advocated under a changing environment, and the trade-offs and synergy of multiple ecosystem services of planted forests should be well managed and coordinated at multiple scales and levels. 参考文献 相似文献 引证文献

Developing a Forest Management Plan (DFMP) for Gatsibo District in the Eastern Province of Rwanda

In support of conservation of its fragile land resource, the government of Rwanda has proposed the management of all forests in the country under a specific management plan. This assignment sought to develop a management plan for the public productive forests of Gatsibo District in Eastern province of Rwanda. Data was collected from 1468 plots, proportionally allocated by size to the 375 forest stands. Information about the forest (qualitative data) was recorded and then forest measurements (inventory) done in a concentric cycle of 9.77 m and 3.99 m radius and all data recorded in a digital format using the survey CTO platform. The data was summarized and analyzed in a harmonized forest management tool for Rwanda comprising of six interlinked excel files based on Rwanda’s silvicultural regimes and treatments. This analysis resulted to planning for silvicultural activities in each forest up to the year 2070. Results show that the public plantation forests of Gatsibo are poorly stocked mainly due to poor management and the best stocked forest had a basal area of only 13.3 m2/ha. This implied low forest volumes for each of the wood requirements; timber wood, service wood and energy wood. Eucalyptus forests are the most common but are poorly stocked compared to the Pinus patula forests which are even aged by plantation. A projection of stocks shows that the forests can be sustained with an average wood volume of 73 m3/ha, to provide harvests yearly and reduce the wood supply and demand gap in the district. A viability analysis indicates that some forests are viable for leasing with good profits in the short term (10 years) and full term planning (40 years). However, some forests may not be commercially viable due to their current stocks and agro bioclimatic conditions, and these would be rehabilitated for ecosystem services. This plan supports the restoration of the forests of Gatsibo district through provision of specific guidelines for the management of the forests.

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m3/ha) for scene acquired in frozen conditions. An RMSE of 40.8 m3/ha (42.9%, R2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m3/ha (35.8%, R2 = 0.77) compared to 38.8–51.6 m3/ha (40.8–54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m3/ha (34%, R2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.

Floristic, structural, and allometric equations to estimate arboreal volume and biomass in a cerradão site

This objective of this study was to characterize the floristic, structural, and ecological groups and to estimate the arboreal volume and biomass of a cerradão site in Palmas, Tocantins, Brazil. A forest inventory was conducted on 10.15 ha of the study area. Plots of 400-m2 were used for systematic sampling. All standing trees (dead or alive) with a breast-height diameter (DHB) greater than 5 cm were identified and measured. Floristic diversity and horizontal structure were assessed using the Shannon and importance value indices, respectively. Forest vertical structure was classified into three stratata and the tree species were categorized into ecological groups. Ninety tree volumes were rigorously cubed and weighed. Fresh- and dry biomass were sampled and estimated. Mathematical models were applied and adjusted to estimate tree volume and biomass. It was observed that the species Myrcia splendens and Emmotum nitens and the families Fabaceae and Chrysobalanaceae were dominant in our study site. The pioneer (613 individuals ha-1) and climax (530 individuals ha-1) tree species group predominated. The floristic diversity index was estimated as 3.35 nats ind- 1. The vertical structure analysis indicated fewer individuals in the superior stratum (13%) compared to the medium (63%) and inferior (24%) stratum. The Schumacher and Hall model showed better results with regard to estimated forest production. Forest volume and biomass estimates were 126.71 m³ ha-1 and 61.67 Mg ha-1, respectively. The studied cerradão area had high floristic diversity and climax species predominated. Since this cerradão is in close proximity to the Amazon biome, its volume and biomass stocks were higher than those estimated for other cerradão and forest formations within the Cerrado biome.

Combination of PolInSAR and LiDAR Techniques for Forest Height Estimation

Forests are simplified as homogeneous volumes constituted of randomly uniform particles, characterized by a constant extinction coefficient in the random volume over ground (RVoG) model, which has been extensively applied to polarimetric synthetic aperture radar (SAR) interferometry for forest height estimation. This letter takes into account the heterogeneous vertical structure reflected by the vertically varying extinction coefficient curve in the forest volume layer, and modifies the RVoG model to make it be more suitable for height inversion of forests with complex structures. For this purpose, the normalized extinction coefficient curve is fit by large-footprint light detection and ranging full waveform data using the Gaussian function. Finally, the varying extinction RVoG model is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system. The results are compared with in situ measurements, which indicate that the varying extinction RVoG model can obtain more accurate results for forest height inversion.

Improved Characterization of Forest Transmissivity Within the L-MEB Model Using Multisensor SAR Data

This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L- and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.

Why did the 1980s' reform of collective forestland tenure in southern China fail?

Both the reform of forestland tenure and timber market liberalization are important for forest resources management worldwide. This paper employs a unique provincial level panel dataset of 8 National Forest Resource Inventories and related price and investment data to estimate the effects on the forest of China's reforms of collective forestland tenure and timber prices in the 1980s. The system Generalized Method of Moments with robustness (controlling for endogeneity) and fixed effects models were used to identify the determinants of these reforms for the collective forest region of southern China. Our empirical results indicate that these two reforms jointly caused deforestation and forest degradation. As much as 13.5% of the total forest area was lost, and 14.9% of the forest volume was removed. Deforestation in the 1980s was followed with widespread negative long-term impacts on forest growth and afforested area. These policy failures suggest the important lesson that a well-conceived framework for monitoring and regulation needs to be in place for successfully implemented reforms.

A statistical analysis of spatiotemporal variations and determinant factors of forest carbon storage under China’s Natural Forest Protection Program

The Natural Forest Protection Program (NFPP) is one of the key ecological forestry programs in China. It not only facilitates the improvement of forest ecological quality in NFPP areas, but also plays a significant role in increasing the carbon storage of forest ecosystems. The program covers 17 provinces, autonomous regions, and municipalities with correspondingly diverse forest resources and environments, ecological features, engineering measures and forest management regimes, all of which affect regional carbon storage. In this study, volume of timber harvest, tending area, pest-infested forest, fire-damaged forest, reforestation, and average annual precipitation, and temperature were evaluated as factors that influence carbon storage. We developed a vector autoregression model for these seven indicators and we studied the dominant factors of carbon storage in the areas covered by NFPP. Timber harvest was the dominant factor influencing carbon storage in the Yellow and Yangtze River basins. Reforestation contributed most to carbon storage in the state-owned forest region in Xinjiang. In state-owned forest regions of Heilongjiang and Jilin Provinces, the dominant factors were forest fires and forest cultivation, respectively. For the enhancement of carbon sequestration capacity, a longer rotation period and a smaller timber harvest are recommended for the Yellow and Yangtze River basins. Trees should be planted in state-owned forests in Xinjiang. Forest fires should be prevented in state-owned forests in Heilongjiang, and greater forest tending efforts should be made in the state-owned forests in Jilin.