森林碳计量方法研究进展

森林是陆地生态系统的主体,不仅是巨大的碳库而且对减缓气候变暖具有积极作用。科学有效的森林碳计量方法,有助于加深对全球碳循环过程的理解。然而,由于森林生态系统结构复杂,对森林碳计量的估算结果普遍存在精度低、不确定性高的问题。近年来,国内外发展了大量对森林碳计量进行估算的方法,主要有基于样地清查的森林植被和土壤碳估算、基于生长收获的经验模型估算、基于定量遥感雷达观测的遥感估测、基于多尺度森林生态系统网络的通量观测和陆地生态系统过程模型模拟等方法。在实际的森林碳计量中,根据不同的森林类型特征和数据获取情况,往往采取不同的碳计量方法,甚至不止一种。以生态过程模型模拟、遥感反演和数据同化技术为主要手段,基于碳通量观测数据、控制实验数据和遥感影像数据,发展多学科、多过程、多尺度的综合联网观测,充分认识森林碳循环过程中碳源/汇的时空分布特征,开展区域、洲际乃至全球尺度碳循环及其对全球变化和人类活动响应的系统性、集成性研究,以便建立高效、可靠的碳计量体系是未来林业碳计量的发展趋势。随着世界各国温室气体排放清单的编制,中国迫切需要科学的方法体系计量森林碳源/汇,提升我国在生态环境问题上的国际发言权和主导权,同时对我国森林可持续经营、生态环境保护以及美丽中国建设提供建议与支持。分析了各类森林碳计量方法的主要特征、优缺点,同时探讨了目前的森林碳计量方法存在的问题和未来的发展趋势,为不同时空尺度下森林碳计量提供参考。     

Observing terrestrial ecosystems and the carbon cycle from space

Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon–climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space‐based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.     

面向生态系统评估的多源数据融合体系

基于生态系统服务功能的生态系统评估是识别生态环境问题、开展生态系统恢复和生物多样性保护、建立生态补偿机制的重要基础,也是保障国家生态安全、推进生态文明建设的重要环节。生态系统评估涉及生态系统多个方面,需要多要素、多类型、多尺度的生态系统观测数据作为支撑。地面观测数据和遥感数据是生态系统评估的两大数据源,但是其在使用时常存在观测标准不一、观测要素不全面、时间连续性不足、尺度不匹配等问题,给生态系统评估增加了极大的不确定性。如何融合不同尺度的观测数据量化生态系统服务功能是实现生态系统准确评估的关键。为此,从观测尺度出发,阐述了地面观测数据、近地面遥感数据、机载遥感数据和卫星遥感数据的特点及其在问题,并综述了这几类数据源进行融合的常用方法,并以生产力、固碳能力、生物多样性几个关键生态参数为例介绍了“基于多源数据融合的生态系统评估技术及其应用研究”项目的多源数据融合体系。最后,总结面向生态系统评估的多源数据融合体系,并指出了该研究的未来发展方向。     

The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux

Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. “repeat lidar data”, we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.     

Spatial variability and controls over biomass stocks,carbon fluxes,and resource-use efficiencies across forest ecosystems

Key message

Stand age, water availability, and the length of the warm period are the most influencing controls of forest structure, functioning, and efficiency.

Abstract

We aimed to discern the distribution and controls of plant biomass, carbon fluxes, and resource-use efficiencies of forest ecosystems ranging from boreal to tropical forests. We analysed a global forest database containing estimates of stand biomass and carbon fluxes (400 and 111 sites, respectively) from which we calculated resource-use efficiencies (biomass production, carbon sequestration, light, and water-use efficiencies). We used the WorldClim climatic database and remote-sensing data derived from the Moderate Resolution Imaging Spectroradiometer to analyse climatic controls of ecosystem functioning. The influences of forest type, stand age, management, and nitrogen deposition were also explored. Tropical forests exhibited the largest gross carbon fluxes (photosynthesis and ecosystem respiration), but rather low net ecosystem production, which peaks in temperate forests. Stand age, water availability, and length of the warm period were the main factors controlling forest structure (biomass) and functionality (carbon fluxes and efficiencies). The interaction between temperature and precipitation was the main climatic driver of gross primary production and ecosystem respiration. The mean resource-use efficiency varied little among biomes. The spatial variability of biomass stocks and their distribution among ecosystem compartments were strongly correlated with the variability in carbon fluxes, and both were strongly controlled by climate (water availability, temperature) and stand characteristics (age, type of leaf). Gross primary production and ecosystem respiration were strongly correlated with mean annual temperature and precipitation only when precipitation and temperature were not limiting factors. Finally, our results suggest a global convergence in mean resource-use efficiencies.     

Vegetation structure and greenness in Central Africa from Modis multi-temporal data

African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.     

Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests

Floodplain forests play a crucial role in the storage of organic carbon (C_org). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of C_org stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store C_org. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha⁻¹. Despite certain difficulties in vegetation classification this method allows an indirect estimation of C_org stocks in Central European floodplains.     

土地利用变化对区域碳源汇的影响研究进展

土地利用变化对陆地生态系统碳循环有着重要的影响,既可能成为碳源,也可能是碳汇。在国内外相关研究的基础上,综述了土地利用变化对全球及区域尺度上森林、草地和农业生态系统碳循环的影响。全球范围内,森林砍伐后向草地和农田的转化发挥碳源的作用,在毁林碳排放中占主导地位,其中热带地区森林转变为农田和草场的碳排放均高于温带和北方森林。另一方面,土地利用变化可促进森林的碳贮存,如退耕还林、改善森林管理等。各区域森林生态系统通过土地利用变化贮存碳的潜力存在显著差别,热带湿润和半湿润地区具有较大的碳汇潜力,而干旱地区减少碳排放的空间相对较少。开垦活动是影响草地生态系统碳储存最主要的人类活动,草地转变为农田伴随着土壤碳的流失。森林或草场转变为农田的过程伴随着植被和土壤碳储量的减少,生态系统碳储量降低,因此它是一个碳排放的过程。伴随着城市的扩张,农田向建设用地的转化也是一个碳排放的过程。当前评估土地利用变化影响的研究方法主要有遥感观测和遥感模型、统计估算、生态系统模型以及土地利用与生态系统模型的耦合。研究方法得到不断地完善和改进的同时,还存在着一些不确定性,因此需要建立统一的观测统计方法,降低数据中的不确定性;完善土地利用与生态系统模型的耦合研究;建立多尺度土地利用变化及生态系统综合技术方法体系;开展碳减排目标下土地利用最优化布局研究。     

Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m²) towards individual grid cells (1 m²) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.     

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provide detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies.     

基于遥感降尺度估算中国森林生物量的空间分布

森林生物量是陆地生态系统重要的碳库,其大小与空间分布特征直接影响森林的碳汇潜力。基于空间降尺度技术,以中国第六次国家森林资源清查资料为基础,同时结合1∶100万植被分布图及同期的基于MODIS反演的NPP空间分布,定量估算了1 km分辨率下我国森林生物量的空间分布。结果表明:(1)降尺度技术能有效结合遥感数据的空间特征与地面详查资料的统计特征,从而较好地解决当前生物量估算的区域尺度转化问题;(2)我国森林生物量存在明显的空间分布规律,与水热条件的空间分布格局基本一致,表现为西部较低东部较高,大型山脉分布处较高;(3)我国森林生物量总量11.0 Pg,平均生物量74.8 Mg/hm2,其中高值区主要集中在东北大小兴安岭和长白山地区、新疆山区、西南横断山脉地区以及东南武夷山地区。     

Remote sensing-based assessment of ecosystem health by optimizing vigor-organization-resilience model: A case study in Fuzhou City,China

Although remote sensing technology has been used to evaluate regional ecosystem health for a long time, it is still necessary to find a suitable index system to better evaluate ecosystem health. This study aims to improve the ecosystem health measurement ability of remote sensing technology. This research was carried out in Fuzhou under the traditional Vigor-Organization-Resilience (VOR) framework by optimizing and improving the construction method of sub-indexes. Sub-indexes were constructed using spectral index analysis, landscape theoretical ecology model and spatial measurement. Three remote sensing datasets were used (1996, 2008 and 2021) to carry out remote sensing diagnosis of regional ecosystem health in the Fuzhou administrative region. The main research findings and conclusions were as follows. A new comprehensive vigor index (CVI) was developed by the principal component analysis (PCA) based on the four indicators: fractional vegetation cover (FVC), global vegetation moisture index (GVMI), vegetation temperature condition index (VTCI), normalized differential build-up and bare soil index (NDBSI). A new organizational index was constructed based on the landscape index. Four types of indexes, namely landscape heterogeneity (LH), landscape connectivity (LC), the shape characteristics of forest patches (CS) and the connectivity of forest patches (CC) were used as the main factors for calculating the organizational index. A resilience index calculation framework was proposed based on the habitat quality model. The temporal and spatial characteristics of ecosystem health were evaluated and analyzed. The regional ecosystem health value of the whole region reduced gradually, with average values of 0.3521 (1996), 0.3445 (2008) and 0.3345 (2021) respectively. The average reduction rate was 0.0007 per year (1996–2021). The proposed remote sensing diagnosis method provides a complete framework for solving the problems of measuring the dynamic evolution process and characteristics of regional ecosystem health.     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

暖温带地区3种森林群落叶面积指数和林冠开阔度的季节动态

以辽东栎(Quercus liaotungensis)为主的落叶阔叶林、华北落叶松(Larix principis-rupprechtii)林和油松(Pinus tabulaeformis)林是暖温带林区具有代表性的森林群落类型。该研究应用国内外流行的半球图方法,通过对这3种森林群落叶面积指数和林冠开阔度的测定和综合比较,分析了叶面积指数和林冠开阔度的季节动态,揭示了暖温带地区不同类型森林群落叶面积指数和林冠开阔度的特征。研究结果表明,落叶阔叶林(优势种为辽东栎、棘皮桦(Betula dahurica)和五角枫(Acer mono))和华北落叶松林两种落叶森林群落的叶面积指数值均随生长季的到来而呈现增长的趋势,最大值出现在8月;林冠开阔度值随着生长季的到来而下降,最大值出现在11月。落叶阔叶林的叶面积指数和林冠开阔度的季节动态较之华北落叶松林明显。油松是常绿树种,其群落叶面积指数和林冠开阔度的变化程度均不明显,但林冠开阔度的变化趋势也是与叶面积指数的变化趋势相反。通过计算得出叶面积指数和林冠开阔度相关显著,并且呈现指数回归的关系。此研究结果为以遥感途径获取暖温带地区叶面积指数提供了地面校正依据,为研究该地区植被林冠的异质性及其造成的影响,以及进一步对该地区林分、景观和区域尺度上碳、水分和通量等方面的模拟提供了基础数据。     

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far‐red sun‐induced chlorophyll fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even‐aged loblolly pine plantations in North Carolina (United States). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far‐red peaks was computed. This quantifies the fluorescence emission efficiencies that are more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly twofold higher red fluorescence yield than mature forest plantations, while the far‐red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intrapixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long‐term forest dynamics in response to future climatic conditions from remote sensing of SIF.     

Long-Term Increase in Aboveground Carbon Stocks Following Exclusion of Grazers and Forest Establishment in an Alpine Ecosystem

Ecosystem stores of carbon are a key component in the global carbon cycle. Many studies have examined the impact of climate change on ecosystem carbon storage, but few have investigated the impact of land-use change and herbivory. However, land-use change is a major aspect of environmental change, and livestock grazing is the most extensive land use globally. In this study, we combine a grazing exclosure experiment and a natural experiment to test the impact of grazer exclusion on vegetation dynamics and ecosystem carbon stores in the short term (12-year exclosures), and the long term (islands inaccessible to livestock), in a heavily grazed mountain region in Norway. Following long-term absence of sheep, birch forest was present. The grazing-resistant grass Nardus stricta, dominated under long-term grazing, whilst the selected grass Deschampsia flexuosa and herb species dominated the vegetation layer in the long-term absence of sheep. The established birch forest led to vegetation carbon stocks being higher on the islands (0.56 kg C m^?2 on the islands compared to 0.18 kg C m^?2 where grazed) and no difference in soil carbon stocks. In the short-term exclusion of sheep, there were minor differences in carbon stocks reflecting the longer term changes. These results show that aboveground carbon stocks are higher in the long-term absence of sheep than in the continual presence of high sheep densities, associated with a vegetation state change between tundra and forest. The reduction of herbivore populations can facilitate forest establishment and increase aboveground carbon stocks, however, the sequestration rate is low.     

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate‐change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process‐oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large‐scale remote‐sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate‐change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.     

Detecting successional changes in tropical forest structure using GatorEye drone-borne lidar

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second-growth and two old-growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human-managed tropical landscapes can now be better characterized. Drone-lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration.     

Tree age,disturbance history,and carbon stocks and fluxes in subalpine Rocky Mountain forests

Forest carbon stocks and fluxes vary with forest age, and relationships with forest age are often used to estimate fluxes for regional or national carbon inventories. Two methods are commonly used to estimate forest age: observed tree age or time since a known disturbance. To clarify the relationships between tree age, time since disturbance and forest carbon storage and cycling, we examined stands of known disturbance history in three landscapes of the southern Rocky Mountains. Our objectives were to assess the similarity between carbon stocks and fluxes for these three landscapes that differed in climate and disturbance history, characterize the relationship between observed tree age and time since disturbance and quantify the predictive capability of tree age or time since disturbance on carbon stocks and fluxes. Carbon pools and fluxes were remarkably similar across the three landscapes, despite differences in elevation, climate, species composition, disturbance history, and forest age. Observed tree age was a poor predictor of time since disturbance. Maximum tree age overestimated time since disturbance for young forests and underestimated it for older forests. Carbon pools and fluxes were related to both tree age and disturbance history, but the relationships differed between these two predictors and were generally less variable for pools than for fluxes. Using tree age in a relationship developed with time since disturbance or vice versa increases errors in estimates of carbon stocks or fluxes. Little change in most carbon stocks and fluxes occurs after the first 100 years following stand‐replacing disturbance, simplifying landscape scale estimates. We conclude that subalpine forests in the Central Rocky Mountains can be treated as a single forest type for the purpose of assessment and modeling of carbon, and that the critical period for change in carbon is < 100 years.