Effects of forest canopy vertical stratification on the estimation of gross primary production by remote sensing

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structureswith vertical stratifications that influence the carbon-water-energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature andVPDplay important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from -50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1-2 gC/m2/day with uncertainties of approximately -30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO2 fluxes ( Fday) and nighttime CO2 fluxes (Rn) using remote sensing and other data. Fday and Rn were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush (Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict Fday and Rn. We found that iNDVI was a strong predictor of Fday (R2= 0.79, n = 66, P \u3c 0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of Fday (R2= 0.82, n = 66, P \u3c 0.0001). Cross-validation indicated that regression tree predictions of Fday were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted Rn quite well (R2 = 0.75–0.77, n = 66) with the regression tree model being slightly more robust in cross-validation. Temporal mapping of Fday and Rn is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic Fday measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict Fday. These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models

ESTIMATION AND MODELING OF FOREST ATTRIBUTES ACROSS LARGE SPATIAL SCALES USING BIOMEBGC, HIGH-RESOLUTION IMAGERY, LIDAR DATA, AND INVENTORY DATA

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest\u27s condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling

Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO2 mixing ratio towers and chambers

Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chlleaf) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25°C (Vmax25), for the purpose of improving carbon and water flux estimation. Measurements of Chlleaf and Vmax25 were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chlleaf-based Vmax25 in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant Vmax25, leaf area index (LAI)-based Vmax25 or specific leaf area (SLA)-based Vmax25. The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8gCm−2d−1 in spring and from between 2.1–2.8 to 1.8gCm−2 d−1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8mmd−1 to 0.6mmd−1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled Vmax25 in sunlit leaves is 34% larger than in the shaded leaves of the same Chlleaf, which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for Vmax25 in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain Vmax25 and reduce the uncertainties in GPP and ET simulations

Remote sensing applications to resource problems in South Dakota

The author has identified the following significant results. Change in the vegetative structure was taking place in the Black Hills. Temporal analysis of the areal extent of open meadows was accomplished using black and white and color infrared aerial photography. A reduction of nearly 1100 hectares of open meadows was determined using photointerpretation. Techniques were developed for the management of meandering lakes, including use of LANDSAT imagery for continuous monitoring, classification of hydrophytes on low altitude CIR imagery, and planning and evaluation of improvements and multiple uses on aerial photography and photo mosaics. LANDSAT data were analyzed statistically from small and entire study scene areas to determine the effect of soils stratifications of corn signatures. Band 5 early season and band 7 later season recorded the strongest evidence of the influence of soils on corn signatures. Significant strata were determined by a multiple range test

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Characterizing forest disturbance dynamics in the humid tropics using optical and LIDAR remotely sensed data sets

Human-induced tropical deforestation and forest degradation are widely recognized as major environmental threats, negatively affecting tropical forest ecosystem services, such as biodiversity and climate regulation. To mitigate the effects of forest disturbance, particularly carbon emissions, national forest monitoring systems are being established throughout the tropics. Multiple good practice guidelines aimed at developing accurate, compatible and cost-effective monitoring systems have been issued by IPCC, UNFCCC, GFOI and other organizations. However, there is a lack of consensus in characterization of the baseline state of the forests and carbon stocks. This dissertation is focused on the improvement of the current methods of remotely-sensed forest area and carbon loss estimation. A sample-based estimation method employing Landsat-based forest type and change maps and GLAS Lidar-modeled carbon data was first prototyped for the Democratic Republic of the Congo (DRC), and then applied for the entire pan-tropical region. The DRC study found that Landsat-scale (30m) map-based forest loss assessments unadjusted for errors may lead to significant underestimation of forest aboveground carbon (AGC) loss in the environments with small-scale land cover change dynamics. This conclusion was supported by the pan-tropical study, which revealed that Landsat-based mapping omitted almost half (44%) of forest loss in Africa compared to the sample-based estimate (sample-based estimate exceeded map-based by 78%). Landsat performed well in Latin America and Southeast Asia (sample-based estimate exceeded map-based by 15% and 6% respectively), where forest dynamics are dominated by large-scale industrial forest clearings. The pan-tropical validation sample also allowed disaggregating forest cover and AGC loss by occurrence in natural- (primary and mature secondary forests, and natural woodlands) or human-managed (tree plantations, agroforestry systems, areas of subsistence agriculture with rapid tree cover rotation) forests. Pan-tropically, 58% of AGC loss came from natural forests, with proportion of natural AGC loss being the highest in Brazil (72%) and the lowest in the humid tropical Africa outside of the DRC (22%). The pan-tropical study employed a novel forest stratification for carbon estimation based on forest structural characteristics (canopy cover and height) and intactness, which aided in reducing standard errors of the sample-based estimate (SE of 4% for the pan-tropical gross forest loss area estimate). Such a stratification also allowed for the quantification of forest degradation by delineating intact and non-intact forest areas with different carbon content. This indirect approach to quantify forest degradation was advanced in the last research chapter by automating the process of intact (hinterland) forest mapping. Hinterland forests are defined as forest patches absent of and removed from disturbance in near-term history. Their utility in using spatial context to map structurally different (degraded and non-degraded) forests points a way forward for improved stratification of forest carbon stocks. Conclusions from the dissertation summarize strengths and challenges of sample-based area estimation in monitoring forest carbon stocks and the possible use of such estimates in the revision of spatially explicit maps by adjusting them to match the unbiased sample-based estimates. Hinterland forest maps, in addition to providing a valuable stratum for sample-based carbon monitoring, may serve as a baseline for the near real-time monitoring of remaining ecologically intact tropical forests