Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems

1999 ◽  
Vol 23 (3) ◽  
pp. 359-390 ◽  
Author(s):  
Paul M. Treitz ◽  
Philip J. Howarth
Keyword(s):  
Remote Sensing ◽  
Ecosystem Health ◽  
Forest Ecosystems ◽  
Structural Features ◽  
Biophysical Parameters ◽  
Physiological Character ◽  
Forest Condition ◽  
Hyperspectral Sensors ◽  
Physiological Information ◽  
The Relationship

Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/physiological information; and the potential of hyperspectral sensors in the measurement of these parameters.

Study on Wood Carbon Sequestration Based on Micro-Characteristics of Wood

2010 ◽  
Vol 113-116 ◽  
pp. 1693-1696
Author(s):  
Ming Hui Guo ◽  
Xin Guan ◽  
Li Zhu ◽  
Jian Li
Keyword(s):  
Carbon Sequestration ◽  
Forest Ecosystems ◽  
Structural Characteristics ◽  
Structural Features ◽  
Carbon Stocks ◽  
Xylem Parenchyma ◽  
Micro Level ◽  
Relationship Of ◽  
The Relationship

Trees are complicated and important organisms in forest ecosystem. They are both carbon stocks and carbon source. In order to give full play to the role of wood carbon sequestration, this paper discussed the relationship of wood structural features and wood carbon sequestration at micro-level. It shows that wood carbon sequestration can be synthetically reflected by vessel, tracheid/xylon, wood rays, intercellular canal, xylem parenchyma and so on. The rate of cell wall and wood carbon sequestration is the relationship of direct proportional function. Micro-structural characteristics of wood can reflect wood carbon sequestration of forest ecosystems, as well as have a practical guide to enhance carbon storage of wood.

A APLICAÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) PARA ANÁLISE DA DEGRADAÇÃO AMBIENTAL DA ÁREA DE INFLUÊNCIA DIRETA DO AÇUDE CASTANHÃO

2019 ◽  
Vol 21 (2) ◽  
pp. 674-685
Author(s):  
Amanda Menezes De Albuquerque ◽  
José Robério Cabral Ribeiro ◽  
Marta Celina Linhares Sales
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Spatial Location ◽  
Biophysical Parameters ◽  
Spectral Image ◽  
Vegetation Indexes ◽  
The Relationship

O aumento da degradação ambiental de terras secas vem conduzindo à erosão dos solos e desertificação, o uso intenso e predatório dos recursos naturais nessas áreas acaba impossibilitando a sobrevivência das comunidades que vivem nessas regiões. O estado do Ceará tem cerca de 92% de seu território inserido no semiárido, a pesquisa foi desenvolvida na Área de Influência Direta do Açude Castanhão – AIC. A através do registro de imagens, tornou-se possível às análises de relacionamento entre localização espacial de alvos do meio ambiente, variação espectral da imagem e variação da cobertura vegetal dos solos. A utilização do sensoriamento remoto e de índices de vegetação como o Índice de Vegetação da Diferença Normalizada (NDVI), facilita a obtenção e modelagem de parâmetros biofísicos das plantas, como a área foliar, biomassa e porcentagem de cobertura do solo, fornecendo importantes informações sobre a Degradação Ambiental da área.Palavras-chave: Degradação; Sensoriamento Remoto; Cobertura Vegetal. ABSTRACTThe increased environmental degradation of dry lands has led to soil erosion and desertification, the intense and predatory use of natural resources in these areas makes it impossible to survive the communities living in these regions. The state of Ceará has about 92% of its territory inserted in the semi-arid, the research was developed in the Area of Direct Influence of Castanhão - AIC. A through image registration, it became possible to analyze the relationship between spatial location of environmental targets, spectral image variation and variation of soil cover. The use of remote sensing and vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) facilitates the obtaining and modeling of plant biophysical parameters such as leaf area, biomass and percentage of soil cover, providing important information on the Environmental Degradation of the area.Keywords:Degradation; Remote Sensing; Vegetal Cover.

Skeletal elements of crinoid echinoderms: High-resolution structural and microanalytical results

1983 ◽  
Vol 41 ◽  
pp. 194-195
Author(s):  
D. F. Blake ◽  
L. F. Allard ◽  
D. R. Peacor
Keyword(s):  
High Resolution ◽  
Marine Invertebrates ◽  
Sea Urchins ◽  
Structural Features ◽  
Sea Stars ◽  
High Resolution Tem ◽  
Relationship Of ◽  
The Relationship ◽  
Insight Into

Echinodermata is a phylum of marine invertebrates which has been extant since Cambrian time (c.a. 500 m.y. before the present). Modern examples of echinoderms include sea urchins, sea stars, and sea lilies (crinoids). The endoskeletons of echinoderms are composed of plates or ossicles (Fig. 1) which are with few exceptions, porous, single crystals of high-magnesian calcite. Despite their single crystal nature, fracture surfaces do not exhibit the near-perfect {10.4} cleavage characteristic of inorganic calcite. This paradoxical mix of biogenic and inorganic features has prompted much recent work on echinoderm skeletal crystallography. Furthermore, fossil echinoderm hard parts comprise a volumetrically significant portion of some marine limestones sequences. The ultrastructural and microchemical characterization of modern skeletal material should lend insight into: 1). The nature of the biogenic processes involved, for example, the relationship of Mg heterogeneity to morphological and structural features in modern echinoderm material, and 2). The nature of the diagenetic changes undergone by their ancient, fossilized counterparts. In this study, high resolution TEM (HRTEM), high voltage TEM (HVTEM), and STEM microanalysis are used to characterize tha ultrastructural and microchemical composition of skeletal elements of the modern crinoid Neocrinus blakei.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

scholarly journals Mental imagery: pulling the plug on perceptualism

2021 ◽  
Author(s):  
Dan Cavedon-Taylor
Keyword(s):  
Mental Imagery ◽  
Structural Features ◽  
Predictive Processing ◽  
Perceptual State ◽  
Perceptual Disorders ◽  
Whole Class ◽  
The Relationship

AbstractWhat is the relationship between perception and mental imagery? I aim to eliminate an answer that I call perceptualism about mental imagery. Strong perceptualism, defended by Bence Nanay, predictive processing theorists, and several others, claims that imagery is a kind of perceptual state. Weak perceptualism, defended by M. G. F. Martin and Matthew Soteriou, claims that mental imagery is a representation of a perceptual state, a view sometimes called The Dependency Thesis. Strong perceptualism is to be rejected since it misclassifies imagery disorders and abnormalities as perceptual disorders and abnormalities. Weak Perceptualism is to be rejected since it gets wrong the aim and accuracy conditions of a whole class of mental imagery–projected mental imagery–and relies on an impoverished concept of perceptual states, ignoring certain of their structural features. Whatever the relationship between perception and imagery, the perceptualist has it wrong.

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