1,721,145 research outputs found
Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation
No full textForest photosynthetic exchange rates at landscape scales have proven difficult to either accurately measure or estimate. Recent developments (Hall et al., 2011, 2008; Hilker et al., 2011a, 2010a) permit us to infer photosynthetic forest light use efficiency () using multi-angle measurements of photochemical reflectance index (PRI) from the CHRIS/PROBA satellite imaging spectrometer, thus completing a long sought-after capability to remotely sense the major inputs driving gross primary production GPP i.e., and absorbed photosynthetically active radiation (APAR). In this first of two companion papers we introduce the theoretical underpinnings of an innovative approach that utilizes our recent developments to produce remotely sensed and spatially explicit maps of and GPP from space, and a data assimilation approach to extend the spatially explicit maps to diurnal, daily and annual time scales. We quantify GPP using the traditional radiation-limited approach of Monteith (1972); however we apply it in an innovative way. [I] Using CHRIS/PROBA we quantify at each satellite overpass for a 625km2 area at 30m resolution. [II] We use a novel physiologically-based multivariate function of APAR, temperature and water vapor pressure deficit model (described herein) and use it to down-regulate at 30 minute intervals. [III] We use the CHRIS/PROBA images of spatial variation in , and NDVI to quantify APAR, hence produce snapshots of GPP. We use a data assimilation approach to extend and GPP to temporally continuous and spatially contiguous maps of vegetation carbon uptake. In the second part of this study (Hilker et al., 2011b) we demonstrate and validate our approach over eight different forest flux tower sites in North America
Prediction of soil properties using a process-based forest growth model to match satellite-derived estimates of leaf area index
No full textWithout better estimates of soil properties than are currently available from coarse-scale maps, it is difficult to predict forest productivity accurately across broad regions. While soil properties are not directly available from remote sensing data, they can potentially be inferred from linking these properties to observations that are readily available from satellite data, such as vegetation leaf area. We took advantage of the direct link that exists between above-ground productivity and maximum leaf area index (LAImax) to derive and map soil fertility (FR) and available soil water storage capacity (ASWC) at 1 km resolution across forested areas in western North America. Initially, we generated estimates of LAImax with a process-based growth model (3-PG), holding soil properties constant (FR = 50% of maximum, ASWC = 200 mm). To derive more realistic estimates of soil properties we inverted the model to infer FR and ASWC from iterative non-linear adjustments of the two soil properties so that model-predicted LAImax values corresponded closely with MODIS-derived observations. We parameterized 3-PG for the most widely distributed tree species in the region, Douglas-fir. The resulting maps were notably more detailed than those derived from the globally available Harmonized World Soil Database. Among 51, level III ecoregions, and the ranges in the two soil properties tended to increase in parallel with LAImax. Further improvements in the approach are envisioned by combining MODIS and LiDAR observations to extend the range and accuracy of LAImax observations
PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data
No full textWe develop herein the theoretical foundations for a new satellite concept, utilizing multi-angle, along track spectral measurements to infer photosynthesis and gross primary production, at the landscape level over time. We validate the theory using both tower and space-borne sensors. The concept, originated in Hall et al. (2008), and Hilker et al. (2008a) and is based on two principles: (1) The first derivative of the photochemical reflectance index (PRI) with respect to shadow fraction viewed by the sensor ?PRI/??s, is proportional to light-use efficiency ?. (2) This behavior can be shown both theoretically and empirically to be independent of vegetation structure and optical properties. These two principles provide the basis for a robust photosynthesis algorithm that can be applied consistently both spatially and temporally. We develop the general theoretical concept using a canopy reflectance model that incorporates a dependence of leaf reflectance on illumination strength, permitting the leaf reflectance at 531 nm to depend on the intensity of photosynthetic down-regulation. Using this model we are able to show that using PRI alone to infer ? is confounded by the shadow fraction viewed by a sensor, the PRI value in a non-down-regulated physiological state, and the sunlit canopy reflectance. We are able to demonstrate that these difficulties are mitigated by using ?PRI/??s—not PRI—as the primary measure of canopy level ?. We demonstrate our concept using tower and satellite data acquired over three years, in two distinct biomes and vegetation types to show that PRI/??s and ? are related by a single function. Building on these ideas we propose the development of a new satellite concept that can utilize a spatially and temporally robust algorithm to map photosynthesis at landscape scales and its temporal variation
Update of forest inventory data with lidar and high spatial resolution satellite imagery
No full textMost countries with significant forest resources have designed and implemented monitoring systems to inventory, at regular intervals, a range of forest stand attributes such as species composition, age, volume, biomass, and disturbance. These inventory systems are typically based upon the interpretation of air photos supplemented by ground measurements, with digital remotely sensed data often used to capture changes within inventory cycles. Light detection and ranging (lidar) and high spatial resolution digital satellite imagery (e. g., QuickBird) offer additional capacity and complementary data sources for inventory assessment, as demonstrated by this study over a 400 ha area on Vancouver Island, British Columbia, Canada. A range of lidar survey parameters were applied to update an existing forest inventory. Results indicate a strong relationship between the small-footprint lidar-derived heights and stand height as derived from aerial photographic interpretation (API) (R = 0.79, p < 0.05). In addition, there was no statistical difference (p < 0.05) between stand height as predicted from a complete lidar coverage or when sampled as a single 400 m wide transect (R = 0.89, p < 0.001). These results demonstrate the utility of lidar data, as a full coverage or sample, in combination with high spatial resolution imagery, as useful data sources for capturing forest inventory stand height and cover information
A new, automated, multiangular radiometer instrument for tower-based observations of canopy reflectance (AMSPEC II)
No full textPlant photosynthesis is critical for understanding carbon cycling at landscape and global scales. While tower-based measurements of CO2 have enhanced our knowledge of ecosystem fluxes, scaling these measurements globally is difficult. Satellite observations provide full, global coverage and hold the potential of spatially continuous measurements of ecosystem fluxes, but the requirements for modeling these fluxes from satellite-derived surface parameters are not well understood. This article describes the further development of a tower-mounted, automated, multiangular spectroradiometer system (AMSPEC II) used to study the relationships between canopy-reflectance and plant-physiological processes from multiangular observations, thereby facilitating a comprehensive modeling of the bidirectional reflectance distribution of the canopy. A Webcam permits simultaneous monitoring of phenological changes over time. Plant photosynthesis is critical for understanding carbon cycling at landscape and global scales. While tower-based measurements of CO2 have enhanced our knowledge of ecosystem fluxes, scaling these measurements globally is difficult. Satellite observations provide full, global coverage and hold the potential of spatially continuous measurements of ecosystem fluxes, but the requirements for modeling these fluxes from satellite-derived surface parameters are not well understood. This article describes the further development of a tower-mounted, automated, multiangular spectroradiometer system (AMSPEC II) used to study the relationships between canopy-reflectance and plant-physiological processes from multiangular observations, thereby facilitating a comprehensive modeling of the bidirectional reflectance distribution of the canopy. A Webcam permits simultaneous monitoring of phenological changes over time
Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations
No full textAssessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30 m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation ± 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ± 0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p < 0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage
Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat
No full textAn increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity
The role of LiDAR in sustainable forest management
No full textForest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing
Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements
No full textWe describe pragmatic and reliable methods to examine the influence of patch-scale heterogeneities on the uncertainty in long-term eddy-covariance (EC) carbon flux data and to scale between the carbon flux estimates derived from land surface optical remote sensing and directly derived from EC flux measurements on the basis of the assessment of footprint climatology. Three different aged Douglas-fir stands with EC flux towers located on Vancouver Island and part of the Fluxnet Canada Research Network were selected. Monthly, annual and interannual footprint climatologies, unweighted or weighted by carbon fluxes, were produced by a simple model based on an analytical solution of the Eulerian advection-diffusion equation. The dimensions and orientation of the flux footprint depended on the height of the measurement, surface roughness length, wind speed and direction, and atmospheric stability. The weighted footprint climatology varied with the different carbon flux components and was asymmetrically distributed around the tower, and its size and spatial structure significantly varied monthly, seasonally and inter-annually. Gross primary productivity (GPP) maps at 10-m resolution were produced using a tower-mounted multi-angular spectroradiometer, combined with the canopy structural information derived from airborne laser scanning (Lidar) data. The horizontal arrays of footprint climatology were superimposed on the 10-m-resolution GPP maps. Monthly and annual uncertainties in EC flux caused by variations in footprint climatology of the 59-year-old Douglas-fir stand were estimated to be approximately 15-20{\%} based on a comparison of GPP estimates derived from EC and remote sensing measurements, and on sensor location bias analysis. The footprint-variation-induced uncertainty in long-term EC flux measurements was mainly dependent on the site spatial heterogeneity. The bias in carbon flux estimates using spatially-explicit ecological models or tower-based remote sensing at finer scales can be estimated by comparing the footprint-weighted and EC-derived flux estimates. This bias is useful for model parameter optimizing. The optimization of parameters in remote-sensing algorithms or ecosystem models using satellite data will, in turn, increase the accuracy in the upscaled regional carbon flux estimation
Lidar calibration and validation for geometric-optical modeling with Landsat imagery
No full textThere is a paucity of detailed and timely forest inventory information available for Canada's large, remote northern boreal forests. The Canadian National Forest Inventory program has derived a limited set of attributes from a Landsat-based land cover product representing circa year 2000 conditions. Of the required inventory attributes, forest vertical structure (e.g., tree height) is critical for terrestrial biomass and carbon modeling and to date, is unavailable for these remote areas. In this study, we develop a large-area, fine-scale (25m) mapping solution to estimate tree height (mean, dominant, and Lorey's height) across Canada's northern forests by integrating lidar data (representing 0.27{\%} of the study area), and Landsat imagery (representing 100{\%} of the study area), using a geometric-optical modeling technique. First, spectral mixture analysis (SMA) was used to extract image endmembers and generate fraction images. Second, lidar data were used to calibrate the inverted geometric-optical model by adjusting the model's three key fractional inputs: sunlit crown, sunlit background, and shade fraction, based upon the SMA derived images. The heterogeneity of the study area, spanning 2.16 million ha, made it challenging to directly and accurately decompose mixed Landsat image pixels into the canopy and background fractions used for the Liâ??Strahler geometric-optical model inversion. As a result we developed a novel method to use the lidar plot data to facilitate the calculation of these fractions in an accurate and automated manner. The average estimation errors for mean, dominant, and Lorey's height were 4.9m, 4.1m, and 4.7m, respectively when compared to the lidar data, with the best result achieved using dominant tree height, where the average error was 3.5m for over 80{\%} of the forested area. Using this approach of optical remotely sensed data calibrated and validated with lidar height estimates, we generate and evaluate wall-to-wall estimates of tree height that can subsequently be used as inputs for biomass and carbon modeling
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