森林地上生物量遥感反演研究进展

森林地上生物量是反映森林生态系统状况的关键性指标之一,对全球气候变化、以及我国实现碳达峰和碳中和目标具有重要意义。遥感技术快速发展并日益成熟,已成为大区域尺度森林地上生物量反演的主要技术手段。通过系统梳理国内外相关文献资料,从数据源和反演模型两方面对森林地上生物量遥感反演研究进展进行讨论：从数据源角度,阐述分析光学遥感数据、合成孔径雷达数据、激光雷达数据等3种数据源提供的有效信息、优势及局限;从反演模型角度,结合实际应用案例讨论分析多元回归模型、机器学习算法、机理模型等3种模型的特点及适用范围。在总结现阶段利用遥感手段反演森林地上生物量存在问题的基础上,分析探讨未来森林地上生物量遥感反演的方向和热点。     

东莞市针叶类森林生物量遥感模型研究

基于 Landsat 8 影像数据,对东莞市松树林 (Pinus sp.)、杉木林 (Cunninghamia lanceolata)、针 叶混交林 3 种针叶类森林生物量进行估算,利用相关分析、主成分分析和逐步回归分析,建立针叶类森 林生物量遥感估算模型,其决定系数 (R2) 值分别为 0.880 9、 0.832 5、 0.964 0,均达显著水平。经适用性 检验,模型均达 0.05 显著水平,可用于东莞市针叶类森林生物量估算。     

基于CEBERS-WFI遥感数据的森林生物量估测方法研究

以中巴卫星CEBERS-WFI遥感数据为基础,结合东北三省的地理、气象因子及森林资源连续清查固定样地信息,构建BP人工神经网络森林生物量估测模型,对我国东北三省的森林生物量进行估测,并反演了森林生物量的空间分布图像。结果表明,基于CEBERS-WFI遥感数据的BP人工神经网络应用于森林生物量估测简单实用,是一种快捷、有效的估测方法。     

长白山林区森林生物量遥感估测模型

采用黑龙江长白山地区TM图像和143块森林资源连续清查固定样地数据及野外调查补充样地数据,选择包括各波段灰度值、不同波段灰度值之间的线性和非线性组合(包括11种植被指数)、纹理信息以及环境因子在内的75个自变量,分别采用逐步回归分析法和偏最小二乘回归法建立黑龙江长白山林区森林生物量遥感估测模型:逐步回归法采用5个自变量所建模型平均拟合精度为76.5%,均方根误差为19.12t·hm-2,样地生物量真实值与预测值相关系数为0.5434;偏最小二乘回归法采用10个自变量所建模型平均拟合精度85.8%,均方根误差9.92t·hm-2,样地生物量真实值与预测值相关系数0.8603,偏最小二乘回归法要优于逐步回归法。利用建立的偏最小二乘回归模型计算得到黑龙江长白山2007生物量等级分布图,采用29个检验样本对反演结果进行检验,计算得到29个样本的平均预测精度为83.73%。     

森林生态系统生物量估测方法的比较评价

论述了各种生物量估测方法在森林生态系统中的应用及其局限性,同时对传统的分层木法与其它生物量估测方法进行了一些比较,结果表明,回归估测法应用范围广,尤其适用于不能砍伐样木的林地。     

基于随机森林回归模型的思茅松人工林生物量遥感估测

以云南省景谷县思茅松人工林为研究对象,以研究区2005年TM影像及2006年森林资源二类调查小班空间属性数据库为信息源,在前期建立思茅松单木生物量模型基础上,在ENVI下提取9个植被指数作为备选自变量,建立研究区思茅松人工林随机森林回归遥感估测模型。结果表明:随机森林回归遥感估测模型的决定系数(R2)=0.97,均方根误差(RMSE)=4.97;模型的预估精度(P)=87.67%。利用已经训练好的随机森林估测模型,估测研究区思茅松人工林生物量为3 644 612.00t;单位面积生物量为59.90 t/hm2。研究结果可为其它典型森林类型生物量或碳储量估测提供案例分析。     

合成孔径雷达森林生物量估测研究进展

随着合成孔径雷达（SAR）遥感技术的发展,微波遥感独特的成像机理及其全天候全天时成像能力,使其在区域和全球森林生物量估测方面具有其它光学遥感数据不可替代的作用,这愈来愈受到科学家们的重视。首先就SAR对森林生物量的敏感性进行了论述,分别P、L和C3个波段及其不同极化方式总结了SAR对森林生物量的敏感性和饱和点等反应特性;然后就SAR对森林生物量的后向散射机理和影响森林后散射的其它因素进行了分析;最后总结了利用SAR进行森林生物量估测的技术路线。     

森林生态系统生物量的定量评价

文章概述了森林生物量评价的必要性和相关概念。详细介绍了近年来发展的森林生物量评价技术方法,包括实测法、模型法和遥感法,并对今后森林生物量估测技术的发展趋势进行了简要的探讨。     

浅议森林生物量的测定方法

森林生态系统最基本数量特征是通过森林生物量来表示,森林生物量的测定通常分为树木生物量测定和林分生物量测定两大类。文章主要阐述了树木生物量及林分生物量的几种测定方法。     

崂山林场林分生物量遥感反演估算研究

以Landsat TM影像和高分一号影像为数据源,结合外业实测数据,利用遥感影像和实测数据建立崂山林场生物量多元线性反演模型,比较分析不同数据源下反演出的模型精度,估测了崂山林场森林生物量。研究发现,利TM遥感影像作为数据源的崂山林场森林生物量反演模型平均精度为77.12%。高分一号遥感数据反演的生物量模型平均反演精度达到80.75%,高于TM数据源下的生物量反演模型精度。分别根据TM遥感影像和高分一号遥感影像林分生物量估测模型,估测的崂山林场2009年的林分生物量为401185.62t,2013年的林分生物量为402485.44t。     

EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements

The overall objective of this study was to combine national forest inventory data and remotely sensed data to produce pan-European maps on growing stock and above-ground woody biomass for the two species groups “broadleaves” and “conifers”. An automatic up-scaling approach making use of satellite remote sensing data and field measurement data was applied for EU-wide mapping of growing stock and above-ground biomass in forests. The approach is based on sampling and allows the direct combination of data with different measurement units such as forest inventory plot data and satellite remote sensing data. For the classification, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used. Comprehensive field measurement data from national forest inventories for 98,979 locations from 16 countries were used for which tree species and growing stock estimates were available. The classification results were evaluated by comparison with regional estimates derived independently from the classification from national forest inventories. The validation at the regional level shows a high correlation between the classification results and the field based estimates with correlation coefficient r = 0.96 for coniferous, r = 0.94 for broadleaved and r = 0.97 for total growing stock per hectare. The mean absolute error of the estimations is 25 m³/ha for coniferous, 20 m³/ha for broadleaved and 25 m³/ha for total growing stock per hectare. Biomass conversion and expansion factors were applied to convert the growing stock classification results to carbon stock in above-ground biomass. As results of the classification, coniferous and broadleaved growing stock as well as carbon stock of the above-ground biomass is mapped on a wall-to-wall basis with a spatial resolution of 500 m × 500 m per grid cell. The mapped area is 5 million km², of which 2 million km² are forests, and covers the whole European Union, the EFTA countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey.     

Indirect methods of large-scale forest biomass estimation

Forest biomass and its change over time have been measured at both local and large scales, an example for the latter being forest greenhouse gas inventories. Currently used methodologies to obtain stock change estimates for large forest areas are mostly based on forest inventory information as well as various factors, referred to as biomass factors, or biomass equations, which transform diameter, height or volume data into biomass estimates. However, while forest inventories usually apply statistically sound sampling and can provide representative estimates for large forest areas, the biomass factors or equations used are, in most cases, not representative, because they are based on local studies. Moreover, their application is controversial due to the inconsistent or inappropriate use of definitions involved. There is no standardized terminology of the various factors, and the use of terms and definitions is often confusing. The present contribution aims at systematically summarizing the main types of biomass factors (BF) and biomass equations (BE) and providing guidance on how to proceed when selecting, developing and applying proper factors or equations to be used in forest biomass estimation. The contribution builds on the guidance given by the IPCC (Good practice guidance for land use, land-use change and forestry, 2003) and suggests that proper application and reporting of biomass factors and equations and transparent and consistent reporting of forest carbon inventories are needed in both scientific literature and the greenhouse gas inventory reports of countries.

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。     

遥感技术在森林资源清查中的应用问题探讨

结合遥感技术在我国森林资源调查中应用的实际情况,对如何在一类清查、二类调查中应用遥感及如何综合应用遥感的问题进行了讨论.     

Aboveground biomass assessment in Colombia: A remote sensing approach

This paper presents a method to increase the level of detail of aboveground biomass estimates at a regional scale. Methods are based on empirical relationships while materials are based on MODIS products and field measurements; the area covers from 4° south up to 12° north of the Equator with a total of 1,139,012 km² corresponding to the continental area of Colombia. Vegetation was classified in three broad classes: grasslands, secondary forests and primary forests which have been proved to enhance biomass estimates. MOD44 vegetation continuous fields (VCFs) was used as an explanatory variable for primary and secondary forests following an exponential relationship, while MOD13A1 enhanced vegetation index (EVI) was used as explanatory variable for grasslands following a linear relationship; biomass for this vegetation class was estimated every 16 days given its large variation throughout the year. EVI–biomass relationships were established from 2001 to 2006. Vegetation maps were used to separate primary forests from secondary forest, since the latter has shown lower biomass levels. Confidence intervals of the exponential regression are larger as the biomass values increases, for this reason the uncertainty is quite high ranging from 3.7 to 25.2 millions of Mg with a mean of 16.2 million of Mg. Despite the uncertainty our biomass results are within the estimates of previous studies.     

浅谈谷歌遥感影像资料在内蒙古边境防火隔离带规划设计中的应用

随着网络和"3s"技术的快速发展,给规划设计工作注入了新的活力,基于Google Earth和Arcgis获取具有地理信息的遥感影像资料成为可能,这极大的提高了规划设计效率和质量。文章在研究了国内外免费获得遥感影像的基础上,利用ArcGIS中的栅格图像空间校正操作的思路和方法,充分发挥谷歌地图全覆盖、高分辨率和易获得的优点,加工整理出能够满足生产工作需要的遥感影像图,为规划设计遥感影像资料获取提供了思路和有效的方法。     

3S技术在江苏市级森林覆盖率监测中的应用研究

针对"绿色江苏"建设和全面建设小康社会对市级森林覆盖率指标考核的实际需要,提出以省辖市为单位,按照4 km×1 km网进行固定样地加密调查,并采用GPS对地面样地进行定位、导航,利用GIS提取样地数据、精确布点和制图,辅助运用RS进行遥感卫片判读,使用数据库功能对全省调查数据进行逻辑检查、数据处理与统计汇总,综合集成应...     

Estimation of shrub biomass by airborne LiDAR data in small forest stands

The presence of shrub vegetation is very significant in Mediterranean ecosystems. However, the difficulty involved in shrub management and the lack of information about behavior of this vegetation means that these areas are often left out of spatial planning projects. Airborne LiDAR (Light Detection And Ranging) has been used successfully in forestry to estimate dendrometric and dasometric variables that allow to characterize forest structure. In contrast, little research has focused on shrub vegetation. The objective of this study was to estimate dry biomass of shrub vegetation in 83 stands of radius 0.5 m using variables derived from LiDAR data. Dominant species was Quercus coccifera, one of the most characteristic species of the Mediterranean forests. Density of LiDAR data in the analyzed stands varied from 2 points/m² to 16 points/m², being the average 8 points/m² and the standard deviation 4.5 points/m². Under these conditions, predictions of biomass were performed calculating the mean height, the maximum height and the percentile values 80th, 90th, and 95th derived from LiDAR in concentric areas whose radius varied from 0.50 m to 3.5 m from the center of the stand. The maximum R² and the minimum RMSE for dry biomass estimations were obtained when the percentile 95th of LiDAR data was calculated in an area of radius 1.5 m, being 0.48 and 1.45 kg, respectively. For this radius, it was found that for the stands (n = 39) where the DTM is calculated with high accuracy (RMSE lower than 0.20 m) and with a high density of LiDAR data (more than 8 points/m²) the R² value was 0.73. These results show the possibility of estimating shrub biomass in small areas when the density of LiDAR data is high and errors associated to the DTM are low. These results would allow us to improve the knowledge about shrub behavior avoiding the cost of field measurements and clear cutting actions.     

Spatial partitioning of biomass and diversity in a lowland Bolivian forest: Linking field and remote sensing measurements

Large-scale inventories of forest biomass and structure are necessary for both understanding carbon dynamics and conserving biodiversity. High-resolution satellite imagery is starting to enable structural analysis of tropical forests over large areas, but we lack an understanding of how tropical forest biomass links to remote sensing. We quantified the spatial distribution of biomass and tree species diversity over 4 ha in a Bolivian lowland moist tropical forest, and then linked our field measurements to high-resolution Quickbird satellite imagery. Our field measurements showed that emergent and canopy dominant trees, being those directly visible from nadir remote sensors, comprised the highest diversity of tree species, represented 86% of all tree species found in our study plots, and contained the majority of forest biomass. Emergent trees obscured 1–15 trees with trunk diameters (at 1.3 m, diameter at breast height (DBH)) ≥20 cm, thus hiding 30–50% of forest biomass from nadir viewing. Allometric equations were developed to link remotely visible crown features to stand parameters, showing that the maximum tree crown length explains 50–70% of the individual tree biomass. We then developed correction equations to derive aboveground forest biomass, basal area, and tree density from tree crowns visible to nadir satellites. We applied an automated tree crown delineation procedure to a high-resolution panchromatic Quickbird image of our study area, which showed promise for identification of forest biomass at community scales, but which also highlighted the difficulties of remotely sensing forest structure at the individual tree level.