Establishing the Downscaling and Spatiotemporal Scale Conversion Models of NDVI Based on Fractal Methodology

Scale effect is a crucial scientific problem in quantitative remote sensing (RS), and scholars attempt to solve it with scale conversion models, which can characterize the numerical relationship of RS land surface parameters at different resolutions (scales). As a significant land surface parameter, scale conversion of normalized difference vegetation index (NDVI) has been studied for a long time. Therefore, taking NDVI as an example, the development of scaling research is described and analyzed in the paper, and based on fractal theory, the development trends are discussed for land surface parameters in quantitative remote sensing. These are our conclusions: it will be the new trend to establish downscaling models based on fractal theory for land surface parameters in quantitative remote sensing; additionally, it still is the hotspot to establish spatiotemporal scale conversion models for land surface parameters in quantitative remote sensing in the future, and addressed on that, the multi-fractal scaling methodology is proposed, and its availability is analyzed in the paper, which presents significant potential

Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling

With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail

The climatic significance of tropical forest edges and their representation in global climate models

An emerging theme in global climate modelling is whether land covers created in the clearance of tropical humid forests influence water exchange between remnant forest patches and the atmosphere, and, if so, how this affects regional and global water exchange. Fieldwork presented in this thesis ascertains whether the amount of water transferred to the atmosphere from a humid tropical forest situated in Sabah, Northern Borneo, Malaysia, differs between its edge and interior due to the influence of surrounding clearings through horizontal heat transfer. Using satellite imagery to measure the shape and size of tropical forests, field measurements of water transfer were extrapolated to continental and global levels to infer how differences in water exchange with the atmosphere between forest edges and interiors may influence regional and global forest-atmosphere water exchange. Mean sap flow in trees within 50 meters of a forest-clearing boundary was found to be 73% greater than that in trees further into the forest; an observation supported by the decreased canopy temperature also recorded there. Evaporation from the forest canopy constituted a high fraction of annual rainfall (33%), but showed no edge effect similar to that of sap flow. Edge plots, however, expressed evapotranspiration rates 22% lower than forest interiors (657-890 mm yr-1), owing to the lower number and size of trees there. One edge plot, however, exhibited evapotranspiration 49.5% greater than that of forest interiors. Gradients of air temperature, vapour pressure deficit and wind speed from the adjacent clearing to the forest interior indicated that warm, dry air moving from the clearing to the forest was the most credible cause of increased sap flow of trees near the forest edge. This hypothesis was supported by a strong correlation between the amount of vapour in the air moving from the clearing and tree water use. It was estimated that the influence of differences in water transfer to the atmosphere between the edges and interiors of tropical forest would not alter global water transfer to the atmosphere by more than 0.25-4%, or by 4-7% in the most fragmented tropical continent, Africa. However, it remains unclear whether the inclusion of tropical forest edge effects within climate models is necessary, as the pioneering nature of this thesis, and of existing studies reviewed within it, means that solid conclusions will be dependent upon future work. This thesis concludes with suggestions for future research that will most effectively consolidate the provisional conclusions and recommendations herein

Fractal Analysis

Fractal analysis is becoming more and more common in all walks of life. This includes biomedical engineering, steganography and art. Writing one book on all these topics is a very difficult task. For this reason, this book covers only selected topics. Interested readers will find in this book the topics of image compression, groundwater quality, establishing the downscaling and spatio-temporal scale conversion models of NDVI, modelling and optimization of 3T fractional nonlinear generalized magneto-thermoelastic multi-material, algebraic fractals in steganography, strain induced microstructures in metals and much more. The book will definitely be of interest to scientists dealing with fractal analysis, as well as biomedical engineers or IT engineers. I encourage you to view individual chapters

Fractal analyses of some natural systems

Fractal dimensions are estimated by the box-counting method for real world data sets and for mathematical models of three natural systems. 1 he natural systems are nearshore sea wave profiles, the topography of Shei-pa National Park in Taiwan, and the normalised difference vegetation index (NDV1) image of a fresh fern. I he mathematical models which represent the natural systems utilise multi-frequency sinusoids for the sea waves, a synthetic digital elevation model constructed by the mid-point displacement method for the topography and the Iterated Function System (IFS) codes for the fern leaf. The results show that similar fractal dimensions are obtained for discrete sub-sections of the real and synthetic one-dimensional wave data, whilst different fractal dimensions are obtained for discrete sections of the real and synthetic topographical and fern data. The similarities and differences are interpreted in the context of system evolution which was introduced by Mandelbrot (1977). Finally, the results for the fern images show that use of fractal dimensions can successfully separate void and filled elements of the two-dimensional series

Analytical framework for modeling scale-related variabilities in remote sensing

A general analytical framework was established to investigate the scale-related variabilities in remote sensing. The variabilities were studied first by investigating canopy structure, canopy interaction with light, relation between spectral reflectance and plant phenological parameters. The variabilities simulated by the plant model were compared with the actual spectral data acquired by ground spectroradiometer and satellite sensors. The theoretical relation between orthogonal-basedtransform and Kahunen-Lo6ve transform was investigated in the vector space. The role of spectral indices in identifying the status of phenological parameters was briefly studied. The radiometric corrections of the remotely sensed data were carefully controlled to avoid the unwanted noise introduced by typical resampling/correction procedures from commercial operation. The non-linearity and sensor response corrections were applied to the spectral data as necessary. Variability analysis was conducted to illustrate the complexities of spectral variability embedded in the remotely sensed data. The information extraction in spatial frequency domain was investigated with emphasis in Fourier domain feature extraction. The Radon transform was introduced as the potential tool to enhance the spatial information of the Fourier transformed image. The adequacy of entropy and fractal dimension as image information measures was proved. A functional link between entropy and fractal dimension was established. The image information content was extracted using various first and second order statistics, entropy, and fractal dimension. Results were presented for different remote sensors based on the full image information content and specific agricultural ground features. The quality of spatial resampling algorithms was tested by investigating the capability to maintain image information in the resampled image. Finally, two applications utilizing this analytical framework were presented to show its potential in land-use classification and multiscale data fusion

Tracking shifts in forest structural complexity through space and time in human‐modified tropical landscapes

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high‐resolution, repeat‐acquisition airborne laser scanning data collected over 450 km2 of human‐modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly degraded, selectively logged and old‐growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g. higher fractal dimension in oil palm plantations than old‐growth forests), while rugosity and height range were tightly correlated (r = 0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space. Keywords: Forest disturbance, LiDAR, logging, recovery, remote sensing, structural complexit