Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory

2017 ◽  
Vol 26 (11) ◽  
pp. 1292-1302 ◽  
Author(s):  
Edna Rödig ◽  
Matthias Cuntz ◽  
Jens Heinke ◽  
Anja Rammig ◽  
Andreas Huth
Keyword(s):  
Remote Sensing ◽  
Spatial Heterogeneity ◽  
Forest Structure ◽  
Rain Forest ◽  
Forest Modelling ◽  
Amazon Rain Forest

Satellite Based Study on Amazon Rain Forest Fires and Effect on Climate

2019 ◽  
Author(s):  
Madhura Chakraborty ◽  
Goutam Dey ◽  
Rohit Kumar Prasad Gupta
Keyword(s):  
Forest Fires ◽  
Rain Forest ◽  
Amazon Rain Forest

A New Species of Proceratophrys (Anura: Leptodactylidae) from the Amazon Rain Forest

2000 ◽  
Vol 34 (2) ◽  
pp. 173 ◽  
Author(s):  
Ariovaldo A. Giaretta ◽  
Paulo S. Bernarde ◽  
Marcelo N. de C. Kokubum
Keyword(s):  
New Species ◽  
Rain Forest ◽  
Amazon Rain Forest ◽  
A New Species

Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro

2017 ◽  
Vol 32 (9) ◽  
pp. 1881-1894 ◽  
Author(s):  
Stephan Getzin ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Spatial Heterogeneity ◽  
Forest Structure ◽  
Airborne Lidar ◽  
Mount Kilimanjaro

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

scholarly journals Coupled carbon-water exchange of the Amazon rain forest, I. Model description, parameterization and sensitivity analysis

2005 ◽  
Vol 2 (2) ◽  
pp. 333-397 ◽  
Author(s):  
E. Simon ◽  
F. X. Meixner ◽  
L. Ganzeveld ◽  
J. Kesselmeier
Keyword(s):  
Stomatal Conductance ◽  
Leaf Area ◽  
Rain Forest ◽  
Canopy Structure ◽  
Leaf Nitrogen ◽  
Optical Parameters ◽  
Amazon Rain Forest ◽  
Leaf Level ◽  
Net Assimilation ◽  
Parameter Values

Abstract. Detailed one-dimensional multilayer biosphere-atmosphere models, also referred to as CANVEG models, are used for more than a decade to describe coupled water-carbon exchange between the terrestrial vegetation and the lower atmosphere. Within the present study, a modified CANVEG scheme is described. A generic parameterization and characterization of biophysical properties of Amazon rain forest canopies is inferred using available field measurements of canopy structure, in-canopy profiles of horizontal wind speed and radiation, canopy albedo, soil heat flux and soil respiration, photosynthetic capacity and leaf nitrogen as well as leaf level enclosure measurements made on sunlit and shaded branches of several Amazonian tree species during the wet and dry season. The sensitivity of calculated canopy energy and CO2 fluxes to the uncertainty of individual parameter values is assessed. In the companion paper, the predicted seasonal exchange of energy, CO2, ozone and isoprene is compared to observations. A bi-modal distribution of leaf area density with a total leaf area index of 6 is inferred from several observations in Amazonia. Predicted light attenuation within the canopy agrees reasonably well with observations made at different field sites. A comparison of predicted and observed canopy albedo shows a high model sensitivity to the leaf optical parameters for near-infrared short-wave radiation (NIR). The predictions agree much better with observations when the leaf reflectance and transmission coefficients for NIR are reduced by 25–40%. Available vertical distributions of photosynthetic capacity and leaf nitrogen concentration suggest a low but significant light acclimation of the rain forest canopy that scales nearly linearly with accumulated leaf area. Evaluation of the biochemical leaf model, using the enclosure measurements, showed that recommended parameter values describing the photosynthetic light response, have to be optimized. Otherwise, predicted net assimilation is overestimated by 30–50%. Two stomatal models have been tested, which apply a well established semi-empirical relationship between stomatal conductance and net assimilation. Both models differ in the way they describe the influence of humidity on stomatal response. However, they show a very similar performance within the range of observed environmental conditions. The agreement between predicted and observed stomatal conductance rates is reasonable. In general, the leaf level data suggests seasonal physiological changes, which can be reproduced reasonably well by assuming increased stomatal conductance rates during the wet season, and decreased assimilation rates during the dry season. The sensitivity of the predicted canopy fluxes of energy and CO2 to the parameterization of canopy structure, the leaf optical parameters, and the scaling of photosynthetic parameters is relatively low (1–12%), with respect to parameter uncertainty. In contrast, modifying leaf model parameters within their uncertainty range results in much larger changes of the predicted canopy net fluxes (5–35%).

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