ICESat-2 ATL08地形和冠层高度产品精度评估

ICESat-2 ATL08地形及冠层高度产品在大范围的森林资源调查研究中的性能还需进一步探究。为此，文章首先对35个生态网络观测站的ATL08地形及冠层高度产品进行整体的精度评估；其次，验证其在不同生物群落中的精度并对单站精度进行定量评价；最后，分析该产品在多种误差因素条件下的性能。结果表明：地形和冠层高度产品的整体RMSE分别为3.23 m和6.58 m;地形高度产品在低矮或稀疏的植被区精度较高，在高大或茂密的森林区精度较低，且其精度随坡度、植被覆盖度及冠层高度的增加而降低；冠层高度产品在温带阔叶林和混交林地区的%RMSE为34.78%,在沙漠和干旱灌木丛地区的%RMSE为127.47%。另外，相比地形高度产品，冠层高度产品受地物覆盖类型、冠层高度和植被覆盖度的影响更严重且影响机制较为复杂。     

低山丘陵区多源数字高程模型误差分析北大核心CSCD

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

低山丘陵区多源数字高程模型误差分析

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI

波形激光雷达（Light Detection And Ranging, LiDAR）已经大量用于森林叶面积指数（Leaf Area Index, LAI）估算,但是波形LiDAR数据估算森林LAI易受地形影响。地形坡度引起的波形展宽使得地面回波和植被冠层回波信息混合在一起,难以得到准确的地面回波和冠层回波,进而影响到LAI估算精度。为了估算不同地形坡度条件下的LAI,本文采用一种坡度自适应的方法处理机载LVIS和星载GLAS波形数据。通过坡度自适应的方法得到地面波峰位置,基于高度阈值来区分地面回波和冠层回波,进而得到能量比值用于LAI估算。基于LVIS和GLAS数据,估算了不同森林站点的LAI,并利用实测LAI数据进行检验。结果表明：利用波形LiDAR数据可以估算森林LAI,坡度自适应方法可以改善地形的影响,提高LAI估算精度。对于机载LVIS,估算新英格兰森林LAI精度为R²=0.77和RMSE=0.21;对于星载GLAS,估算塞罕坝森林LAI精度为R²=0.81和RMSE=0.28。无论机载还是星载数据,该方法都有着较高的精度,对于复杂地形估算LAI具有一定潜力。     

基于三维激光扫描技术的马尾松冠层孔隙度研究

三维激光扫描技术具有扫描快、对树木无损害、高度还原树木原形等优势，为三维样木重构、树木冠层结构研究及森林资源连年监测研究提供精确的数据。以马尾松为对象，利用三维激光扫描仪获取30株单木点云数据，运用体素化、平面投影和凸包算法等，计算单木冠层孔隙度。结合分层理论，通过与树木生长参数(冠幅、冠体积和冠高度)的相关性分析，构建全冠与不同分层方式提取的冠层孔隙度建立多元线性回归模型，以决定系数(R²)、均方根误差(RMSE)、相对分析误差(RPD)和总体精度(TA)确定冠层孔隙度提取的最佳体素边长和最佳分层方式。结果表明：冠层孔隙度提取的最佳的分层方式为冠层形态三分层(R²为0.74)；冠层形态三分层、冠层形态三分层五分层结合、冠层高度三等分层提取的冠层孔隙度与树木生长参数的影响最为稳定；根据冠层形态分层提取的孔隙度适用于冠层形态差异较大，而冠层形态较一致时，采用冠层高度三等分层是较为合适，精度较高。     

基于遥感的海南本岛植被覆盖度时空变化及其地形效应研究北大核心CSCD

植被覆盖在维持生态系统结构稳定和防治水土流失等方面发挥着重要的作用,海南自1988年建省以来迅速发展,导致海南岛植被覆盖也产生了巨大的变化.为揭示海南本岛地形因子对植被覆盖度的影响以及为海南本岛进一步制定合理的生态环保策略提供依据,基于1988、1998、2008、2017和2020年Landsat-TM/OLI多光谱影像,以海南本岛为研究区域,采用归一化植被指数和像元二分模型进行植被覆盖度提取,通过线性趋势分析海南本岛近30a植被覆盖变化特征.并结合30 m_DEM获取的海、坡度和坡向数据,来进一步探讨海南本岛植被覆盖度在不同地形因子条件下的空间分布特征.结果表明:①1988～2020年海南岛平均植被覆盖度介于0.58～0.88之间,整体呈先下降后上升趋势;②海南岛高植被覆盖主要分布于海南岛中部、西南部和东南部地区,低植被覆盖主要出现在居住区、沿海地区等人为干扰因素较高的地区;③海南岛各等级植被覆盖均随海拔的增加而不断降低,在海拔小于100 m的区域分布面积最大;坡度为0～5°时植被覆盖面积达到最大值,随着坡度的增加,植被覆盖面积呈减少趋势;各等级植被覆盖在阴坡和阳坡的分布面积变化差异不大,主要以高植被覆盖类型为主.     

不同覆盖度下小麦农田土壤对NDVI影响模拟分析

生长于不同土壤类型背景条件下的相同长势小麦农田遥感像元尺度的归一化植被指数(NDVI)有很大差异,也一直困扰着利用NDVI进行小麦长势有效监测和精确评价。拟定小麦冠层光谱不变即小麦冠层NDVI为一常数条件下,选择反射率差异较大的我国9种典型土壤类型作为土壤背景,由小麦冠层和土壤背景的不同线性混合比模拟计算遥感像元尺度上的植被覆盖度,研究不同土壤类型背景对小麦农田NDVI信息的影响。研究结果表明:同一土壤类型背景条件下,随着植被覆盖度逐渐增加,小麦农田NDVI总体表现为增长的趋势,反之亦然;不同类型土壤背景对小麦农田NDVI造成很大差异,当植被覆盖度大于25%时,随着植被覆盖度的增加对小麦农田NDVI影响差异性逐渐减小;不同类型土壤背景也导致小麦农田NDVI对植被覆盖度的敏感性有明显差异,较低反射率土壤背景条件下的敏感性随着植被覆盖度增长呈现曲线下降的趋势,较高反射率土壤背景条件下敏感性随着植被覆盖度的增长而单调增加,为不同类型土壤背景的各小麦生长期遥感NDVI信息估算频次选择提供依据。     

南京冬季典型植被光谱特征分析

利用FieldSpec4便携式地物光谱仪和ASD积分球,于2014年7月和12月对研究区6种典型植被进行光谱数据采集与处理,分析植被冠层和落叶的光谱特征及其变化规律,同时分析坡度因素、测量方法对植被光谱反射率的影响。结果表明:不同季节常绿植被光谱存在差异,不同植被光谱反射率的季节变化也不同。冬季常绿植被具有相似的光谱特征,但是不同植被类型之间也存在明显的差异。冬季植被冠层光谱呈现出先降低后稳定的特点;植被落叶层光谱由于受叶片色素、含水量、土壤背景等因素的影响,在衰老腐化的过程中并未出现明显的规律性变化。一定坡度范围内,植被光谱反射率随坡度的增大而升高。不同的测量方法获取的植被光谱反射率不同,但是光谱变化规律相同。     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

基于HJ-1高光谱数据的植被覆盖度估测方法研究

植被覆盖度是衡量地表植被状况的一个重要参数,在水文、生态等方面有重要意义,同时,也是影响土壤侵蚀与水土流失的主要因子,是评价土地荒漠化最有效的指标。以环境一号(HJ-1)小卫星上搭载的新型传感器HSI获取的高光谱数据为数据源,通过选择合适的植被指数建立了植被覆盖度反演模型——像元二分模型。然后运用该模型提取了新疆石河子地区的植被覆盖度信息。通过与地面样方数据进行交互比较,对HJ-1/HSI数据反演植被覆盖度的精度进行了评价。研究结果表明,HJ-1/HSI数据能够得到较高精度的植被覆盖度反演结果,在植被动态及全球变化研究领域具有潜在应用价值。     

Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry

The challenge to retrieve canopy height from large-footprint satellite lidar waveforms over mountainous areas is formidable given the complex interaction of terrain and vegetation. This study explores the potential of GLAS (Geoscience Laser Altimeter System) for retrieving maximum canopy height over mountainous areas in the Pacific Coast region, including two conifers sites of tall and closed canopy and one broadleaf woodland site of shorter and sparse canopy. Both direct methods and statistical models are developed and tested using spatially extensive coincident airborne lidar data. The major findings include: 1) the direct methods tend to overestimate the canopy height and are complicated by the identification of waveform signal start and terrain ground elevation, 2) the exploratory data analysis indicates that the edge-extent linear regression models have better generalizability than the edge-extent nonlinear models at the inter-site level, 3) the inter-site level test with mixed-effects models reveals that the edge-extent linear models have statistically-justified generalizability between the two conifer sites but not between the conifer and woodland sites, 4) the intra-site level test indicates that the edge-extent linear models have statistically-justified generalizability across different vegetation community types within any given site; this, combined with 3), unveils that the statistical modeling of maximum canopy height over large areas with edge-extent linear models only need to consider broad vegetation differences (such as woodlands versus conifer forests instead of different vegetation communities within woodlands or conifer forests), and 5) the simulations indicate that the errors and uncertainty in canopy height estimation can be significantly reduced by decreasing the footprint size. It is recommended that the footprint size of the next-generation satellite lidar systems be at least 10 m or so if we want to achieve meter-level accuracy of maximum canopy height estimation using direct and statistical methods.     

提高森林覆盖区积雪提取精度的方法研究——以玛纳斯河流域为例

森林覆盖区积雪的提取精度很低,由于植被冠层的遮挡,冠层下的积雪很难被提取出来。基于Landsat 8OLI数据,针对玛纳斯河流域下游有大面积森林覆盖的特点,通过传统的积雪指数法,结合NDVI数据的积雪指数法和面向对象图像特征法分别提取积雪面积。结果表明:1传统的NDSI和S3积雪指数法无法较好地提取出森林覆盖下的积雪,提取精度分别为85.23%和87.54%。这两种方法适用于空间尺度较大、植被覆盖面积较大的区域,并不适合所选研究区;2结合NDVI数据后的NDSI、S3积雪指数模型能大大提高森林覆盖下的积雪面积,提取精度分别达到91.47%和90.60%。在影像空间分辨率较高,流域尺度较小,林区覆盖较多的情况下可采用此方法提取积雪;3随着海拔的升高,地形阴影影响逐渐增大,NDVI辅助积雪指数方法提取林区覆盖下积雪面积逐渐减小。因此采用光谱、纹理和空间信息结合的面向对象图像特征方法提取积雪,能够较好地识别出受地形影响下的雪像元,精度达到89.75%,可以满足实际应用的需求。     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.     

The importance of data type,laser spot density and modelling method for vegetation height mapping in continental China

Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.     

Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning

Height and intensity information derived from Airborne Laser Scanning (ALS) was used to obtain a quantitative vertical stratification of vegetation in a multi-layered Mediterranean ecosystem. A new methodology for the separation of different vegetation strata was implemented using supervised classification of a two-dimensional feature space spanned by ALS return height (terrain corrected) and intensity. The classification was carried out using Gaussian mixture models tuned on a control plot. The approach was validated using extensive field measurements from treated plots, ranging from single vegetation strata to a more complex multi-layered ecosystem. Plot-level canopy profiles derived from ALS and from a geometric reconstruction based on field measurements were in very good agreement, with correlation coefficients ranging from 0.73 (for complex, 3-layered) to 0.96 (simple, single-layered). In addition, it was possible to derive plot-level information on layer height, vertical extent and coverage with absolute accuracies of some decimetres (simple plots) to a meter (complex plots) for both height and vertical extent and about 10 to 15% for layer coverage. The approach was then used to derive maps of the layer height, vertical extent and percentage of ground cover for a larger area, and classification accuracy was evaluated on a per-pixel basis. The method performed best for single-layered plots or dominant layers on multi-layered plots, obtaining an overall accuracy of 80 to 90%. For subdominant layers in the more complex plots, accuracies obtained were as low as 48%.Our results demonstrate the possibility of deriving qualitative (presence and absence of specific vegetation layers) and quantitative, physical data (height, vertical extent and ground cover) describing the vertical structure of complex multi-layered forest ecosystems using ALS-based height and intensity information.     

Validation of surface height from shuttle radar topography mission using shuttle laser altimeter

Spaceborne Interferometric SAR (InSAR) technology used in the Shuttle Radar Topography Mission (SRTM) and spaceborne lidar such as Shuttle Laser Altimeter-02 (SLA-02) are two promising technologies for providing global scale digital elevation models (DEMs). Each type of these systems has limitations that affect the accuracy or extent of coverage. These systems are complementary in developing DEM data. In this study, surface height measured independently by SRTM and SLA-02 was cross-validated. SLA data was first verified by field observations, and examinations of individual lidar waveforms. The geolocation accuracy of the SLA height data sets was examined by checking the correlation between the SLA surface height with SRTM height at 90 m resolution, while shifting the SLA ground track within its specified horizontal errors. It was found that the heights from the two instruments were highly correlated along the SLA ground track, and shifting the positions did not improve the correlation significantly. Absolute surface heights from SRTM and SLA referenced to the same horizontal and vertical datum (World Geodetic System (WGS) 84 Ellipsoid) were compared. The effects of forest cover and surface slope on the height difference were also examined. After removing the forest effect on SRTM height, the mean height difference with SLA-02 was near zero. It can be further inferred from the standard deviation of the height differences that the absolute accuracy of SRTM height at low vegetation area is better than the SRTM mission specifications (16 m). The SRTM height bias caused by forest cover needs to be further examined using future spaceborne lidar (e.g. GLAS) data.     

Technical note: Canopy height models and airborne lasers to estimate forest biomass: Two problems

Three ground datasets were used to simulate the canopy height characteristics of tropical forests in Costa Rica for the purposes of forest biomass estimation. The canopy height models (CHMs) were used in conjunction with airborne laser data. Gross biomass estimation errors on the order of 50-90% arose in two of the three analyses. The characteristics of the datasets and the biomass estimation procedure are reviewed to identify sources of error. In one dataset, the width of the fixed-area ground plots were small enough (5 m) that significant portions of the overstory canopy above the plots were not accounted for in the ground samples. The use of mapped stand data from thin ground plots resulted in inaccurate CHMs, which in turn lead to gross overestimates of forest biomass (about 90% larger than the ground reference value). CHMs generated using mensuration data collected on thin, fixed-area plots may significantly underestimate the average canopy height and crown closure actually found on that plot. This underestimation problem is directly related to plot width. Below a critical threshold, the thinner the plot, the greater the underestimation bias. In the second dataset, it is believed that lower-than-normal rainfalls at the beginning and end of the wet season may have produced a forest canopy with reduced leaf area. The airborne laser pulses penetrated further into the canopy, resulting in airborne laser estimates of forest biomass which grossly underestimated reference values by about 50%. Changing canopy conditions (e.g. leaf loss due to drought, insect defoliation, storm damage) can affect the accuracy of a CHM. Sources of error are reported in order to forewarn those researchers who produce and utilize canopy height models.     

Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation.     

Boosted decision tree classifications of land cover over Turkey integrating MODIS,climate and topographic data

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP). Twelve land cover maps were generated for Turkey using boosted decision trees (BDTs) based on the stepwise addition of 14 explanatory variables derived from a time series of 16-day MODIS composites between 2000 and 2006 (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and four spectral bands) and ancillary climate and topographic data (minimum and maximum air temperature, precipitation, potential evapotranspiration, aspect, elevation, distance to sea and slope) at 500-m resolution. Evaluation of the 12 BDTs indicated that the BDT built as a function of all the MODIS and climate variables, aspect and elevation produced the highest degree of overall classification accuracy (79.8%) and kappa statistic (0.76) followed by the BDTs that additionally included distance to sea (DtS), and both DtS and slope. Based on an independent validation dataset derived from a pre-existing national forest map and Landsat images of Turkey, the highest overall accuracy (64.7%) and kappa coefficient (0.58) among the 12 land cover maps was achieved by using MODIS-derived NDVI time series only, followed by NDVI and EVI time series combined; NDVI, EVI and four MODIS spectral bands; and the combination of all MODIS and climate data, aspect, elevation and distance to sea, respectively. The largest improvements in producer's accuracies were observed for grasslands (+50%), barrenlands (+46%) and mixed forests (+39%) and in user's accuracies for grasslands (+53%), shrublands (+30%) and mixed forests (+28%), in relation to the lowest producer's accuracy. The results of this study indicate that BDTs can increase the accuracy of land cover classifications at the national scale.     

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.