scholarly journals Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

2015 ◽  
Vol 7 (4) ◽  
pp. 3526-3547 ◽  
Author(s):  
Gregory Asner ◽  
Roberta Martin
Keyword(s):  
Remote Sensing ◽  
Forest Canopies

scholarly journals THERMAL REMOTE SENSING WITH UAV-BASED WORKFLOWS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 41-46 ◽  
Author(s):  
R. Boesch
Keyword(s):  
Remote Sensing ◽  
Plant Stress ◽  
Bundle Adjustment ◽  
Natural Environments ◽  
Oblique View ◽  
Forest Canopies ◽  
Low Contrast ◽  
Ground Control Points ◽  
Thermal Cameras ◽  
Camera Orientation

Climate change will have a significant influence on vegetation health and growth. Predictions of higher mean summer temperatures and prolonged summer draughts may pose a threat to agriculture areas and forest canopies. Rising canopy temperatures can be an indicator of plant stress because of the closure of stomata and a decrease in the transpiration rate. <br><br> Thermal cameras are available for decades, but still often used for single image analysis, only in oblique view manner or with visual evaluations of video sequences. <br><br> Therefore remote sensing using a thermal camera can be an important data source to understand transpiration processes. <br><br> Photogrammetric workflows allow to process thermal images similar to RGB data. But low spatial resolution of thermal cameras, significant optical distortion and typically low contrast require an adapted workflow. Temperature distribution in forest canopies is typically completely unknown and less distinct than for urban or industrial areas, where metal constructions and surfaces yield high contrast and sharp edge information. <br><br> The aim of this paper is to investigate the influence of interior camera orientation, tie point matching and ground control points on the resulting accuracy of bundle adjustment and dense cloud generation with a typically used photogrammetric workflow for UAVbased thermal imagery in natural environments.

Remote sensing detection of droughts in Amazonian forest canopies

2010 ◽  
Vol 187 (3) ◽  
pp. 733-750 ◽  
Author(s):  
Liana O. Anderson ◽  
Yadvinder Malhi ◽  
Luiz E. O. C. Aragão ◽  
Richard Ladle ◽  
Egidio Arai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopies ◽  
Amazonian Forest

A plot-based method for rapid estimation of forest canopy chemistry

2001 ◽  
Vol 31 (3) ◽  
pp. 549-555 ◽  
Author(s):  
Marie-Louise Smith ◽  
Mary E Martin
Keyword(s):  
Remote Sensing ◽  
Rapid Method ◽  
Forest Canopy ◽  
Forest Stand ◽  
Litter Fall ◽  
Forest Canopies ◽  
Rapid Estimation ◽  
Simple Leaf ◽  
Leaf Level ◽  
Fractional Distribution

In this study we present a rapid method to scale the leaf-level chemistry of forest stands to the whole-canopy level. The method combines simple leaf-level measurements of mass and chemistry with a camera-based technique to estimate the fractional distribution of species' foliage area in a forest canopy. Results using this methodology for the estimation of whole-canopy N concentration (g/100 g) are presented and are shown to be comparable with those derived directly from litter fall collection. The ability to efficiently scale leaf-level traits to whole forest canopies enhances our ability to examine key relationships associated with these traits at various levels from the leaf to the forest stand and, with remote sensing technologies, to larger landscapes.

Analyses of Forest Foliage II: Measurement of Carbon Fraction and Nitrogen Content by End-Member Analysis

1994 ◽  
Vol 2 (1) ◽  
pp. 15-23 ◽  
Author(s):  
John D. Aber ◽  
Katherine L. Bolster ◽  
Stephen D. Newman ◽  
Margaret Soulia ◽  
Mary E. Martin
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Chemical Constituents ◽  
High Spectral Resolution ◽  
Statistical Techniques ◽  
Mixing Models ◽  
Difference Spectra ◽  
Forest Canopies

Chemical constituents of forest canopies are accepted as key indicators of ecosystem state and function. Remote sensing of these parameters offers the potential for rapid and accurate assessment of rates of key biogeochemical processes over large regions. NASA's Accelerated Canopy Chemistry Program was established to test the accuracy and generality of high-spectral resolution remote sensing in the measurement of lignin, cellulose and nitrogen concentrations in forest canopies. One of the most straightforward methods for extracting constituent concentration information from spectra is through simple linear mixing models of pre-determined end-member or pure compound spectra. While there are ample reasons to expect that linear mixing models will not work in foliar and whole-canopy samples, end-member analysis might still provide important information in support of standard and emerging statistical techniques, such as linear regression and partial least squares regression on first and second difference spectra, by indicating which parts of the spectrum should contain information on the concentrations of important constituents. The purpose of this paper is to present the results of two approaches to determining the value of end-member spectra for estimating constituent concentrations in foliage of temperate zone forest species. The first examines the spectral changes accompanying each step in the chemical proximate analysis method used to determine concentrations in the laboratory, and from them to infer the spectra of the fractions removed at each step. The second is the combination of known materials which approximate these same fractions in foliage into well-mixed samples to determine whether a simple linear mixing model can be used to predict the spectrum of the resulting mixture. Results confirm that the combination of linear mixing models and end-member analysis is not an appropriate technique for obtaining quantitative estimates of constituent concentrations for the major components of foliage of native woody plants, nor do we expect that more detailed analyses of plant ultrastructure or foliar spectra will correct the deficiencies identified. However, these results do suggest that the wet chemical procedures used to extract different carbon fractions produce consistent results with regard to the location of spectral features associated with the compounds removed at each step, and that the spectra of the cellulose and lignin isolated by this technique are very similar to those from pure materials. This in turn suggests that statistical techniques such as linear regression on first and second difference spectra and partial least squares methods which allow or correct for non-linear mixing, should be successful.

scholarly journals Assessing L-Band GNSS-Reflectometry and Imaging Radar for Detecting Sub-Canopy Inundation Dynamics in a Tropical Wetlands Complex

2018 ◽  
Vol 10 (9) ◽  
pp. 1431 ◽  
Author(s):  
Katherine Jensen ◽  
Kyle McDonald ◽  
Erika Podest ◽  
Nereida Rodriguez-Alvarez ◽  
Viviana Horna ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Time Series Data ◽  
Leading Edge ◽  
Satellite System ◽  
Series Data ◽  
Tropical Wetlands ◽  
Forest Canopies ◽  
Imaging Radar ◽  
L Band

Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth’s surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined.

scholarly journals Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models

2018 ◽  
Vol 10 (11) ◽  
pp. 1814 ◽  
Author(s):  
Shudi Zuo ◽  
Shaoqing Dai ◽  
Xiaodong Song ◽  
Chengdong Xu ◽  
Yilan Liao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Surface Temperature ◽  
Statistical Models ◽  
Land Surface ◽  
Urban Forest ◽  
Meteorological Data ◽  
Rapid Urbanization ◽  
Forest Canopies ◽  
Cold Spots

The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

scholarly journals Modeling the View Angle Dependence of Gap Fractions in Forest Canopies: Implications for Mapping Fractional Snow Cover Using Optical Remote Sensing

2008 ◽  
Vol 9 (5) ◽  
pp. 1005-1019 ◽  
Author(s):  
Jicheng Liu ◽  
Curtis E. Woodcock ◽  
Rae A. Melloh ◽  
Robert E. Davis ◽  
Ceretha McKenzie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Forest Canopy ◽  
Forest Cover ◽  
Airborne Lidar ◽  
Analysis Model ◽  
Forest Canopies ◽  
Fractional Snow Cover ◽  
Go Model

Abstract Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of view zenith angle. However, the GO model tends to underestimate the VGF compared with estimates derived from hemispherical photos, particularly at high view angles. The use of a more complicated geometric optical–radiative transfer (GORT) model generally improves estimates of the VGF by taking into account within-crown gaps. Small footprint airborne lidar data are useful for mapping forest cover and height, which makes the parameterization of the GORT model possible over a landscape. Better knowledge of the angular distribution of gaps in forest canopies holds promise for improving remote sensing of snow cover fraction.

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