Retrieval of forest structural parameters using LiDAR remote sensing

In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.     

Regional assessment of boreal forest productivity using an ecological process model and remote sensing parameter maps

An ecological process model (BIOME-BGC) was used to assess boreal forest regional net primary production (NPP) and response to short-term, year-to-year weather fluctuations based on spatially explicit, land cover and biomass maps derived by radar remote sensing, as well as soil, terrain and daily weather information. Simulations were conducted at a 30-m spatial resolution, over a 1205 km(2) portion of the BOREAS Southern Study Area of central Saskatchewan, Canada, over a 3-year period (1994-1996). Simulations of NPP for the study region were spatially and temporally complex, averaging 2.2 (+/- 0.6), 1.8 (+/- 0.5) and 1.7 (+/- 0.5) Mg C ha(-1) year(-1) for 1994, 1995 and 1996, respectively. Spatial variability of NPP was strongly controlled by the amount of aboveground biomass, particularly photosynthetic leaf area, whereas biophysical differences between broadleaf deciduous and evergreen coniferous vegetation were of secondary importance. Simulations of NPP were strongly sensitive to year-to-year variations in seasonal weather patterns, which influenced the timing of spring thaw and deciduous bud-burst. Reductions in annual NPP of approximately 17 and 22% for 1995 and 1996, respectively, were attributed to 3- and 5-week delays in spring thaw relative to 1994. Boreal forest stands with greater proportions of deciduous vegetation were more sensitive to the timing of spring thaw than evergreen coniferous stands. Similar relationships were found by comparing simulated snow depth records with 10-year records of aboveground NPP measurements obtained from biomass harvest plots within the BOREAS region. These results highlight the importance of sub-grid scale land cover complexity in controlling boreal forest regional productivity, the dynamic response of the biome to short-term interannual climate variations, and the potential implications of climate change and other large-scale disturbances.     

LOSS ASSESSMENT OF BURNED FOREST USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION TECHNIQUES

After forest fire,It is very needd to locate fire position and assess the loss of forestresources.In this paper,a method of burned forest assessment with satellite remote sensing dataand over-laying techniques is discussed and used in the assessment of the burned forest in Malinforest Farm after the large forest fire of May,1987.     

遥感在森林生物量监测的应用

森林生物量研究对监测生态系统有着重要作用,随着遥感技术的发展,动态地估测大区域乃至全球的森林生物量成为可能.文章就遥感数据源和估测方法,分析总结了传统方法和遥感方法的森林生物量估测.     

The progress of operational forest fire monitoring with infrared remote sensing

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring (FFM) with satellite- and drone-mounted infrared remote sensing (IRRS). This review includes four types of FFM-relevant IRRS algorithms: bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAA-AVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI, MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems (known as forest fire products), and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires (including small and short-lived fires) and forest-fire early warning.     

遥感估测森林可燃物载量的研究进展

对采用遥感图像估测森林可燃物载量的方法进行综述.首先将现有方法根据对像元载量的分配方法,分成直接分配法和间接分配法2种.直接分配法分成聚类分析法和判别分析法;间接分配法分为简单植被特征法、林分模型法和综合因子约束法3种.然后对各方法的优缺点进行评价,指出现有方法整体准确率不高的不足,并分析产生误差的3个来源:1) 从遥感图像判读中间特征所产生的误差;2) 从中间特征到可燃物载量之间的误差;3) 使用可燃物模型所产生的误差.据此提出改进现有方法、提高估测准确率的3个思路:1) 使用新图像,如更高分辨率遥感图像、雷达图像或混合图像;2) 选择更合适的中间特征以及它们与可燃物载量的关系模型;3) 使用连续变量来描述可燃物载量.     

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

Abstract

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.     

Estimation of gases emitted by forest fires based on remote sensing data

Forest fire, an important agent for change in many forest ecosystems, plays an important role in atmospheric chemical cycles and the carbon cycle. The primary emissions from forest fire, CO₂, CO, CH₄, long-chained hydrocarbons and volatile organic oxides, however, have not been well quantified. Quantifying the carbonaceous gas emissions of forest fires is a critical part to better understand the significance of forest fire in calculating carbon balance and forecasting climate change. This study uses images from Enhanced Thematic Mapper Plus (ETM+) on the Earth-observing satellite LANDSAT-7 for the year 2005 to estimate the total gases emitted by the 2006 Kanduhe forest fire in the Daxing’an Mountains. Our results suggest that the fire emitted approximately 149,187.66 t CO₂, 21,187.70 t CO, 1925.41 t C _x H _y , 470.76 t NO and 658.77 t SO₂. In addition, the gases emitted from larch forests were significantly higher than from both broadleaf-needle leaf mixed forests and broadleaf mixed forests.     

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.     

Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps

Large-scale information on habitat suitability is indispensable for planning management actions to further endangered species with large-spatial requirements. So far, remote sensing based habitat variables mostly included environmental and land cover data derived from passive sensors, but lacked information on vegetation structure. This is a serious constraint for the management of endangered species with specific structural requirements. Light detection and ranging (LiDAR), in contrast to passive remote sensing techniques, may bridge this gap in structural information at the landscape scale. We investigated the potential of LiDAR data to quantify habitat suitability for capercaillie (Tetrao urogallus), an endangered forest grouse in Central Europe, in a forest reserve of 17.7 km². We used continuous variables of horizontal and vertical stand structure from first and last pulse LiDAR data and presence–absence information from field work to model habitat suitability with generalized linear models (GLM). The two final habitat suitability models explained the observed presence–absence pattern moderately well (AUC of 0.71 and 0.77) with horizontal structure explaining better than vertical structure. Relative tree canopy cover was the most important variable with intermediate values indicating highest habitat suitability. As such, LiDAR allowed us to translate the results from habitat modeling at the landscape scale to effective management recommendations at the local scale at a level of detail that hitherto was unavailable for large areas. LiDAR thus enabled us to integrate individual habitat preferences at the scale of entire populations and thus offers great potential for effective habitat monitoring and management of endangered species.     

估测森林蓄积量的遥感因子选择研究

为提高森林蓄积量的估测精度,选择多重相关性小的遥感因子组合,运用残差平方和法,对湖南省新化县曹家镇20个一类调查样地对应的SPOT5影像的9个遥感因子组合进行了多重相关性研究。结果表明:除遥感因子FSP2外,其余因子对森林蓄积量的估测都有重要作用。通过方差扩大因子对各遥感因子的多重相关性诊断表明:剔除FSP2后,各遥感因子间的多重相关性大幅减小。     

An assessment of Hawaiian dry forest condition with fine resolution remote sensing

Remote sensing offers the potential to spatially map forest cover quickly and reliably for inventory purposes. We developed a new image analysis approach using an integrated methodology of “object-based” image classification techniques and field-based measurements to quantify forest cover in a degraded dry forest ecosystem on the leeward side of the Island of Hawaii. This new approach explicitly recognized the transitional areas between tree crowns and tree shades (tree shadows) as a unique class and fully utilized them for the quantification of canopy cover. Object-oriented classification of Ikonos-2 satellite images allowed delineation of tree shades and crowns and the transitional areas between them from objects with similar reflectance and size that were surrounding the trees. These included patches of fountain (Pennisetum setaceum) and kikuyu (Pennisetum clandestinum) grass, lava outcrops and lava–grass mixtures. Crown-shade transitions were clearly differentiated in spite of their wide range of spectral values and reflectance similarities with areas of lava–grass mixture. Segments representing tree shades and dark lava outcrops were also classified into their respective classes even if they were contiguous. The image estimates of canopy cover using the tree shade plus transition classes were linearly related with field estimates of canopy cover (R² = 0.86 and slope = 0.976). Based on this relationship, dry forest cover throughout the 2627-ha area was estimated at 7.7 ± 1.9%. An immediate application of this new approach is to select and delineate areas with higher canopy cover in order to concentrate ecological restoration and conservation efforts.     

Uncertainty assessment of large-scale forest growth predictions based on a transition-matrix model in Catalonia

Key message

When predicting forest growth at a regional or national level, uncertainty arises from the sampling and the prediction model. Using a transition-matrix model, we made predictions for the whole Catalonian forest over an 11-year interval. It turned out that the sampling was the major source of uncertainty and accounted for at least 60 % of the total uncertainty.

Context

With the development of new policies to mitigate global warming and to protect biodiversity, there is a growing interest in large-scale forest growth models. Their predictions are affected by many sources of uncertainty such as the sampling error, errors in the estimates of the model parameters, and residual errors. Quantifying the total uncertainty of those predictions helps to evaluate the risk of making a wrong decision.

Aims

In this paper, we quantified the contribution of the sampling error and the model-related errors to the total uncertainty of predictions from a large-scale growth model in Catalonia.

Methods

The model was based on a transition-matrix approach and predicted tree frequencies by species group and 5-cm diameter class over an 11-year time step. Using Monte Carlo techniques, we propagated the sampling error and the model-related errors to quantify their contribution to the total uncertainty.

Results

The sampling variance accounted for at least 60 % of the total variance in smaller diameter classes, with this percentage increasing up to 90 % in larger diameter classes.

Conclusion

Among the few possible options to reduce sampling uncertainty, we suggest improving the variance–covariance estimator of the predictions in order to better account for the multivariate framework and the changing plot size.

     

基于Landsat8 OLI遥感影像的森林蓄积量估测模型研究

本次试验以湖南省湘潭县为研究区,提取Landsat8 OLI影像数据的56个遥感因子作为候选因子,结合皮尔逊相关系数和主成分分析两种方法对变量进行降维,构建多元线性回归模型(MLR)、误差反向传播神经网络(BP-ANN)、K最近邻模型(KNN)和随机森林模型(RF)进行蓄积量反演,并采用决定系数(R2)、均方根误差(R...     

浅谈遥感在我国森林资源监测中的应用现状

目前我国森林资源监测由单一化逐步向综合化转变,其表现为监测内容的日益丰富。传统的森林资源监测重点主要在森林的蓄积、面积上。现在,监测内容已经扩展到森林生态系统的各个方面,林业部门除了有国家森林资源连续清查监测体系外,还有森林火灾监测、森林病虫害监测、荒漠化监测、湿地监测、珍稀野生动物资源监测等。为了提高森林资源监测的效率和精度,普遍采用了遥感技术。本文主要介绍了遥感技术在森林资源各监测内容中的应用现状,并对遥感技术的发展趋势作出了展望。     

卫星遥感数据在森林采伐监测中的应用

分别采用SPOT5、TM5影像前后两期多光谱遥感影像的波谱特征变化,检测森林资源变化信息,确定变化类型,以计算机自动识别对森林资源变化(减少)的区域(伐区)进行信息提取,并在此基础上进行室内人工预判读;结合采伐证、伐区作业设计、二类调查材料,进行补充判读,得出森林采伐图斑。古丈TM5(30 m分辨率)的面积正判率为96.3%;古丈SPOT5(10 m分辨率)的面积正判率为96.9%。实证分析表明,使用中、高分辨率卫星遥感数据能对森林采伐进行监测,结合辅助材料后能显著提高森林采伐监测精度。     

Validation of plantation transpiration in south-eastern Australia estimated using the 3PG+ forest growth model

Forest plantations for wood production are an increasingly important land use in southern Australia, and there are potentially important hydrologic consequences of what is mostly a change in land use from agriculture to silviculture. An ability to predict, with some degree of accuracy, the impact of plantation expansion on surface water and groundwater resources is essential. A validated process-based modelling approach, integrating the many interacting environmental and management factors which may influence plantation growth and transpiration, can be used for this purpose. The 3PG forest growth model has been evaluated for a number of species from widely differing climate and site conditions. While growth predictions have been validated, little attention has been given to testing the accuracy of the transpiration predictions or the model's representation of the water balance. We enhanced the 3PG forest growth model (known as 3PG+) and then integrated it into the Catchment Analysis Tool (CAT), so that it now interfaces with a more detailed multi-layered, daily time step representation of the soil water balance. Simulated transpiration using 3PG+ in CAT was compared with field measurements in 30 plots (across 15 sites) representing 5 common plantation species (Eucalyptus globulus, E. nitens, E. grandis, E. regnans and Pinus radiata) across ages 2–31 years. Mean daily plot transpiration during the measurement periods ranged between 0.4 and 4.2 mm day⁻¹ (average 2.0 mm day⁻¹). Simulated mean daily plot transpiration using 3PG+ in CAT for Eucalyptus was good (coefficient of efficiency = 0.80; R² = 0.81). While the model tended to slightly under-predict transpiration at higher measured rates (>3.5 mm day⁻¹), predictions at monthly timescales had acceptable accuracy. The integration of 3PG+ into CAT resulted in an improvement in accuracy and applicability of CAT, and provides for the spatial application of 3PG+ across diverse and mixed land use catchments for investigation into carbon and water movement in forest systems.     

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries,as they require vast numbers of field samples for upscaling.Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m), Landsat(30 m) and Google Earth very high resolution(GEVHR) Quickbird(0.65 m) images. For the local scale(Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination(R~2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m) and accuracy(R2= 0.76 and 0.7) with minimal error(RMSE = 64 and38 tons ha-1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD ?) monitoring reporting and verification processes.