基于机载LiDAR数据的福建柏人工林林木参数提取

准确高效地提取人工林林木参数可为估算单木材积、林分蓄积量提供关键信息。本文提出基于机载LiDAR数据的高精度单木参数提取方法,其实现过程包括数据预处理、地面滤波、单木分割和参数提取。以福建省沙县官庄国有林场的福建柏大径材人工林为试验区,采集高密度机载点云数据,对点云进行去噪、重采样等预处理。使用布料滤波算法(CSF)分离出植被点云和地面点云,并采用Delaunay三角网法将植被点云数据插值生成数字表面模型(DSM),采用反距离加权插值法将地面点云数据插值生成数字高程模型(DEM),两者作差运算获得冠层高度模型(CHM)。利用分水岭分割算法分析不同分辨率的CHM对单木分割及参数提取精度的影响。采用点云距离聚类算法对归一化植被点云进行单木分割,分析不同的距离阈值对单木分割及参数提取精度的影响。结果表明：使用分水岭分割算法处理0.3 m分辨率CHM单木分割调和值最高,达到91.1%,提取的树高精度较优,决定系数(R²)达到0.967,均方根误差(RMSE)为0.890 m;使用间距阈值为平均冠幅的点云分割算法单木分割调和值最高,达到91.3%,提取的冠幅精度较优,R     

马尾松林分结构对冠层水文效应的影响

了解不同林分结构森林的水文效应及其主要影响因素,可为林分抚育经营管理提供科学依据。本研究在三峡库区九领头林场设置14块20 m×30 m马尾松林样地,观测其林冠截留量、树干茎流量和穿透雨量,调查马尾松林分结构因子,利用Pearson相关分析、主成分分析、冗余分析等方法分析林分结构(叶面积指数、混交度、大小比数、竞争指数等)对水文效应(冠层截留率、穿透雨率、树干茎流率)的影响。研究期间(6—10月)林外降雨总量1008.4 mm,林冠截留量、穿透雨量和树干茎流量分别占总降雨的16.3%、82.3%和1.4%。Pearson相关分析结果表明:叶面积指数、胸高断面积、冠幅面积与冠层截留率呈显著正相关(P0.05),与穿透雨率呈显著负相关(P0.05),树干茎流率与树高、冠层厚度呈显著负相关(P0.05),与叶面积指数、林分密度呈显著正相关(P0.05);冗余分析结果表明:水文效应的再分配特征能被结构变量组合解释59.6%,蒙特卡罗置换检验结果表明:冠层截留率和穿透雨率主要受蓄积量这类结构组合变量的影响,蓄积量越大,冠层截留率越高,穿透雨率越低,树干茎流率主要受林分竞争状况及水平结构(R~2=0.46,P0.05)和蓄积量(R~2=0.51,P0.05)的影响。林分结构与冠层水文效应密切相关,林分生长状况越好,蓄积量越大,马尾松林涵养水源效果越好。     

近地面激光雷达点云密度对森林冠层结构参数提取准确性的影响

基于激光雷达技术获取冠层结构为森林生态学研究增加了新的维度。搭载于多旋翼无人机的近地面激光雷达相比于固定翼有人机的机载激光雷达，能够更加灵活高效地获取森林群落样地高密度点云。但在实际操作中，往往出现局部低密度点云数据，影响了冠层结构参数提取的准确性。使用4块森林动态样地的近地面激光雷达点云数据；利用航带分解方法分析各样地低密度样方成因；采用点云抽稀模拟算法计算并拟合偏差曲线，对比不同样地、参数和取样尺度间的点云密度对冠层结构参数提取准确性的影响；根据偏差曲线计算各条件下保证参数提取准确性的最低点云密度。结果发现：1)低密度区域主要受地形或(和)近地面遥感设计规划的影响。地形复杂的测区(西双版纳和古田山样地),遥感规划难度大，整体难以获取高密度点云(在30点/m²左右),容易在沟谷和高海拔处出现低密度样方。平坦测区(长白山两块样地)虽可获取高密度点云(均超过150点/m²),但欠佳的遥感规划设计导致长白山1测区北部出现1hm²低密度区域。2)冠层参数提取准确性随点云密度减少而迅速降低，呈负指数幂函数关系。这一变化趋势在不同...     

中亚热带森林单木地上生物量的机载激光雷达估测

以雪峰山武冈林场为研究对象,利用遥感数据和地面实测样地数据,研究机载激光雷达（LiDAR）估测中亚热带森林乔木层单木地上生物量的能力.利用条件随机场和最优化方法实现LiDAR点云的单木分割,以单木尺度为对象提取的植被点云空间结构、回波特征以及地形特征等作为遥感变量,采用回归模型估测乔木层地上生物量.结果表明: 针叶林、阔叶林和针阔混交林的单木识别率分别为93%、86%和60%;多元逐步回归模型的调整决定系数分别为0.83、0.81和0.74,均方根误差分别为28.22、29.79和32.31 t·hm^-2;以冠层体积、树高百分位值、坡度和回波强度值构成的模型精度明显高于以树高为因子的传统回归模型精度.以单木为对象从LiDAR点云中提取的遥感变量有助于提高森林生物量估测精度.
     

广州典型风水林碳密度特征

以广州市典型风水林为对象,对其生态系统全组分碳储量及其分配格局进行调查和估算,研究碳密度特征及其影响因素。结果显示:风水林生态系统平均碳密度为(259.17±69.67) t/hm~2,其中,植被碳密度为(194.04±54.07) t/hm~2(占74.9%)(其中以乔木层占绝对优势,达90%以上),土壤碳密度为(65.13±19.30) t/hm~2(占25.1%);植被和土壤碳密度之间呈显著正相关(P 0.05);不同优势种类的风水林碳密度差异较大,以米槠(Castanopsis carlesii (Hemsl.) Hayata.)为优势的林分碳密度最大(310.57±62.65 t/hm~2)。结果表明影响风水林碳密度的主要因子是林分胸高断面积、林分密度、土壤容重和土壤碳含量,其中,风水林碳密度与胸高断面积、土壤容重和土壤碳含量呈显著正相关,与林分密度呈显著负相关,与植物多样性无显著相关。研究结果对南亚热带林分改造和碳汇林营造具有重要科学意义。     

顾及无人机影像点云特征的绿地信息分类方法

采用无人机影像进行绿地信息分类时, 常利用影像光谱、纹理、形状等分类特征, 忽视了通过无人机影像生成点云构建的数字表面模型(Digital surface model, DSM)和数字高程模型(Digital elevation model, DEM)差异特征。基于此, 提出一种顾及无人机影像点云特征的绿地信息分类方法。方法首先基于摄影测量理论对研究区无人机影像进行空三计算, 并生成点云, 在此基础上构建DSM、DEM和数字正射影像(Digital Orthophoto Map, DOM); 然后, 利用DSM和DEM模型构建地物高度差异模型(normalized Digital Surface Model, nDSM); 最后, 利用可见光波段差异植被指数(Visible-band difference vegetation index, VDVI)对DOM进行植被与非植被分类, 并结合nDSM对植被进行分类。以昆明市呈贡区白龙潭公园为研究区进行绿地信息分类, Kappa系数精度达到0.862, 实验表明本文的方法对城市绿地调查具有实际意义。     

基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算

快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(Li DAR)点云与Landsat 8 OLI多光谱数据,借助江苏省常熟市虞山地区55块调查样地数据,首先提取并分析了87个特征变量(53个OLI特征变量,34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选,然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型),并与基于两种数据建立的综合生物量估算模型的结果进行比较,讨论预测结果及其精确性。结果表明:3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时,地上和地下生物量的估算精度均达到0.4以上,基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高,达到0.67及以上。利用分森林类型模型估算生物量,综合生物量估算模型精度(地上生物量:R2为0.88;地下生物量:R2为0.92)优于OLI生物量估算模型(地上生物量:R2为0.73;地下生物量:R2为0.81)和Li DAR生物量估算模型(地上生物量:R2为0.86;地下生物量:R2为0.83)。     

广州典型风水林碳密度特征

以广州市典型风水林为对象,对其生态系统全组分碳储量及其分配格局进行调查和估算,研究碳密度特征及其影响因素。结果显示：风水林生态系统平均碳密度为（259.17±69.67）t/hm²,其中,植被碳密度为（194.04±54.07）t/hm²（占74.9%）（其中以乔木层占绝对优势,达90%以上）,土壤碳密度为（65.13±19.30）t/hm²（占25.1%）;植被和土壤碳密度之间呈显著正相关（P<0.05）;不同优势种类的风水林碳密度差异较大,以米槠（Castanopsis carlesii（Hemsl.）Hayata.）为优势的林分碳密度最大（310.57±62.65 t/hm²）。结果表明影响风水林碳密度的主要因子是林分胸高断面积、林分密度、土壤容重和土壤碳含量,其中,风水林碳密度与胸高断面积、土壤容重和土壤碳含量呈显著正相关,与林分密度呈显著负相关,与植物多样性无显著相关。研究结果对南亚热带林分改造和碳汇林营造具有重要科学意义。     

自然与人工恢复对川西高山采伐迹地植物群落特征的影响

以天然林为对照,选取自然恢复(40年)与人工恢复(30、40和50年)下川西高山采伐迹地,研究不同恢复途径下川西高山采伐迹地的植物群落特征。结果表明: 采伐迹地经过40年的自然恢复演替成为高山绣线菊次生灌丛,人工恢复后成为川西云杉林,与天然林群落相似性分别为极不相似(0.19)和中等不相似(0.28~0.49)。自然与人工恢复采伐迹地的灌木层物种多样性均低于天然林,而草本层高于天然林。随着恢复年限的增加,人工林胸高断面积、蓄积量、径级幅度、物种多样性指数及与天然林群落的相似性均呈现增加的趋势,而林分密度逐渐减小。人工林面临林分密度较高、结构不合理、同龄纯林和林下更新差等问题。     

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

 快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R²为0.88; 地下生物量: R²为0.92)优于OLI生物量估算模型(地上生物量: R²为0.73; 地下生物量: R²为0.81)和LiDAR生物量估算模型(地上生物量: R²为0.86; 地下生物量: R²为0.83)。     

基于机载激光雷达的中亚热带常绿阔叶林林窗特征

机载激光雷达(LiDAR)是一种新型主动式遥感技术,能直接获取多尺度高精度的冠层三维结构信息,将其推广到森林干扰生态学领域,可为林窗研究提供应用支撑.以湖南中亚热带常绿阔叶林为研究对象,利用小光斑LiDAR数据进行林窗识别和几何特征估测.选择合适的分辨率和内插方法生成冠层高程模型,采用计算机图形学方法估测林窗面积、边界木高度和形状指数,并进行野外观测验证.结果表明: 林窗识别率为94.8%,主要影响因素是林窗面积和林窗形成木类型;估测的林窗面积和边界木高与野外观测值呈较强线性相关,R²值分别为0.962和0.878,其中估测的林窗面积平均比野外观测值高19.9%,估测的林窗边界木高度平均比野外观测值低9.9%;区域内林窗密度为12.8个·hm^-2,占森林面积13.3%;林窗面积、边界木高和形状指数的平均值分别为85.06 m²、15.33 m和1.71,区域内多为较小面积、边缘效应不太显著的林窗.
     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

Three-dimensional Structure of an Old-growth <Emphasis Type="Italic">Pseudotsuga-Tsuga</Emphasis> Canopy and Its Implications for Radiation Balance,Microclimate, and Gas Exchange

We describe the three-dimensional structure of an old-growth Douglas-fir/western hemlock forest in the central Cascades of southern Washington, USA. We concentrate on the vertical distribution of foliage, crowns, external surface area, wood biomass, and several components of canopy volume. In addition, we estimate the spatial variation of some aspects of structure, including the topography of the outer surface, and of microclimate, including the within-canopy transmittance of photosynthetically active radiation (PAR). The crowns of large stems, especially of Douglas-fir, dominate the structure and many aspects of spatial variation. The mean vertical profile of canopy surfaces, estimated by five methods, generally showed a single maximum in the lower to middle third of the canopy, although the height of that maximum varied by method. The stand leaf area index was around 9 m² m^–2, but also varied according to method (from 6.3 to 12.3). Because of the deep narrow crowns and numerous gaps, the outer canopy surface is extremely complex, with a surface area more than 12 times that of the ground below. The large volume included below the outer canopy surface is very porous, with spaces of several qualitatively distinct environments. Our measurements are consistent with emerging concepts about the structure of old-growth forests, where a high degree of complexity is generated by diverse structural features. These structural characteristics have implications for various ecosystem functions. The height and large volume of the stand indicate a large storage component for microclimatic variables. The high biomass influences the dynamics of those variables, retarding rates of change. The complexity of the canopy outer surface influences radiation balance, particularly in reducing short-wave reflectance. The bottom-heaviness of the foliage profile indicates much radiation absorption and gas exchange activity in the lower canopy. The high porosity contributes to flat gradients of most microclimate variables. Most stand respiration occurs within the canopy and is distributed over a broad vertical range.     

Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape

Airborne laser scanning provides continuous coverage mapping of forest canopy height and thereby is a powerful tool to scale-up above-ground biomass (AGB) estimates from stand to landscape. A critical first step is the selection of the plot variables which can be related to light detection and ranging (LiDAR) statistics. A universal approach was previously proposed which combines local and regional estimates of basal area (BA) and wood density with LiDAR-derived canopy height to map carbon at a regional scale (Asner et al. in Oecologia 168:1147–1160, 2012). Here we explore the contribution of stem diameter distribution, specific wood density and height-diameter (H–D) allometry to forest stand AGB and propose an alternative model. By applying the new model to a large tropical forest data set we show that an appropriate choice of input variables is essential to minimize prediction error of stand AGB which will propagate at larger scale. Stem number (N) and average stem cross-sectional area should be used instead of BA when scaling from tree to plot. Stand quadratic mean diameter above the census threshold diameter size should be preferred over stand mean diameter as it reduces the prediction error of stand AGB by a factor of ten. Wood density should be weighted by stem volume per species instead of BA. LiDAR-derived statistics should prove useful for estimating local H-D allometries as well as mapping N and the mean quadratic diameter above 10 cm at the landscape level. Prior stratification into forest types is likely to improve both estimation procedures significantly and is considered the foremost current challenge.     

基于无人机激光雷达遥感的亚热带常绿阔叶林群落垂直结构分析

森林植被高度与树木分布格局是植物群落重要结构特征,也是计算森林生物量分布的重要参数。传统的森林群落调查方法耗费大量人力物力难以进行较大尺度的群落结构测量,而一般的遥感影像也难以获得精确的地形信息及垂直结构。近年来激光雷达(Light Detection and Ranging,LiDAR)技术快速发展,能够较好的进行植被三维特征的提取并被广泛应用于森林生态系统检测模拟。且随着无人机低空摄影技术的发展催生的无人机激光雷达(UAV-Lidar)更增加了激光雷达的灵活性以及获取较大范围植被冠层信息的能力。而受限于激光的穿透性以及不同植被类型郁闭度的影响,该技术的应用多局限于在针叶林群落的垂直结构研究,而在常绿阔叶林的研究中应用较少。为探究现有无人机激光雷达设备及垂直结构提取分析技术应用于常绿阔叶林的可行性,利用无人机载激光雷达遥感技术对哀牢山中山湿性常绿阔叶林3块面积1hm~2的样地进行基于数字表面模型以及数字地表高程模型做差得到树冠高度模型测量的植被冠层高度、基于局部最大值法进行单木位置提取并使用Clark-Evans最近邻体分析方法进行样地内高大乔木分布格局的计算。分析结果显示,植被高度提取精度平均大于95%,与地表实测的植被高度值拟合度较高,相关系数R~2介于0.833—0.927之间;3个样地冠层高度平均值分别为18.79、19.08、17.03 m,标准差分别为8.10、7.34、7.17 m。单木探测百分比平均86.3%,用户精度以及生产者精度平均分别为75.69%和65.15%。实测得出三个样地全部高大乔木空间分布格局均为聚集分布,而激光雷达测量结果显示为随机分布或均匀分布。实验显示基于无人机激光雷达技术能够很好地提取植被冠层高度信息并能够较好地获取树木位置,但对于树木空间分布格局判定的准确性有待于进一步探索。未来研究应从多角度对激光雷达测量造成的误差原因予以分析(如环境因素),并进一步研究更为精确的单木提取以及植被高度提取方法,为通过无人机激光雷达测算森林生物量及各种生态过程提供更加精准的指标数据。     

Changes in Forest Structure and Species Composition during Secondary Forest Succession in the Bolivian Amazon1

Changes in forest structure and species diversity throughout secondary succession were studied using a chronosequence at two sites in the Bolivian Amazon. Secondary forests ranging in age from 2 to 40 years as well as mature forests were included, making a total of 14 stands. Fifty plants per forest layer (understory, subcanopy, and canopy) were sampled using the transect of variable area technique. Mean and maximum height, total stem density, basal area, and species number were calculated at the stand level. Species diversity was calculated for each stand and for each combination of forest layer and stand. A correspondence analysis was performed, and the relationship between relative abundance of the species and stand age was modeled using a set of hierarchical models. Canopy height and basal area increased with stand age, indicating that secondary forests rapidly attain a forest structure similar in many respects to mature forests. A total of 250 species were recorded of which ca 50 percent made up 87 percent of the sampled individuals. Species diversity increased with stand age and varied among the forest layers, with the lowest diversity in the canopy. The results of the correspondence analysis indicated that species composition varies with stand age, forest layer, and site. The species composition of mature forests recovered at different rates in the different forest layers, being the slowest in the canopy layer. Species showed different patterns of abundance in relation to stand age, supporting the current model of succession.     

Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City,Northwest China

Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m² (AGB), and 75.81 g/m² (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.     

Estimating tree and stand sapwood area in spatially heterogeneous southeastern Australian forests

Aims Natural and anthropogenic changes in forests can have important influences on transpiration and water production. Understanding the effects of increasing disturbances, due for example to climate change and forest harvesting, requires detailed information on how forest density and structural attributes relate to transpiration. Mean annual transpiration of eucalypt forest communities is often strongly correlated with total cross-sectional sapwood area. Our aim was to test an efficient method for estimating sapwood area at 1.3 m height (SA 1.3) in a large number of trees to understand the spatial heterogeneity of tree and stand sapwood area within and between forest communities, and develop allometric relationships that predict SA 1.3 with forest inventory data. We also apply tree competition models to determine the degree to which the relationship between SA 1.3 and tree basal area at 1.3 m height (BA 1.3) is influenced by competition.Methods We visited 25 recently harvested southeastern Australian forest sites consisting of 1379 trees and 5 Eucalyptus species to evaluate a new efficient data collection method for estimating SA 1.3 with tree taper and stump dimensions data using mixed effects models. The locations of 784 stumps within one 5-ha site were accurately mapped using an unmanned aerial vehicle (UAV), and four distance-dependent tree competition models were applied across the site to explain within-stand variation in the ratio of SA 1.3 to BA 1.3. Data from 24 additional sites, consisting of ten 15 m radial plots per site, were used to analyse within-site variation in R Ha (the ratio of stand sapwood area SA Ha to stand basal area BA Ha). The radial plots were merged within each site to evaluate between-site variations in R Ha across the landscape. For predicting SA Ha with forest inventory data, we computed the relationship between SA Ha and a new index of total stem perimeter per hectare, defined as ? B A H a N T, where N T is tree stocking density.Important findings Our 1379 measured stems represent the most comprehensive measure of sapwood area, surpassing the 757 measured stems in native eucalypt forests published in literature. The species-specific R Ha varied considerably across sites and therefore extrapolating SA Ha with spatially distributed BA Ha maps and a generalized R Ha would introduce local uncertainty. We found that the species-specific stem perimeter index was more effective at capturing variability in SA Ha across the landscape using forest composition, structure and density data (R 2 : 0.72–0.77). The strong correlation between tree SA 1.3 and BA 1.3 improved slightly using tree competition models (R 2 increased from 0.86 to 0.88). Relating SA Ha to routinely measured forest inventory attributes within permanent plots and Light Detection and Ranging (LiDAR) data may provide opportunities to map forest water use in time and space across large areas disturbed by wildfire and logging.