Assessing Multi Temporal Remote Sensing Imagery For Discriminating Savannah Tree Species
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Assessing Multi Temporal Remote Sensing Imagery For Discriminating Savannah Tree Species
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Author : Sabelo Madonsela
language : en
Publisher:
Release Date : 2014
Assessing Multi Temporal Remote Sensing Imagery For Discriminating Savannah Tree Species written by Sabelo Madonsela and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Savannas categories.
Potential Of Multi Temporal Remote Sensing Data For Modeling Tree Species Distributions And Species Richness In Mexico
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Author : Anna Cord
language : en
Publisher:
Release Date : 2012
Potential Of Multi Temporal Remote Sensing Data For Modeling Tree Species Distributions And Species Richness In Mexico written by Anna Cord and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Multi Sensor And Multi Temporal Remote Sensing
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Author : Anil Kumar
language : en
Publisher: CRC Press
Release Date : 2023-04-17
Multi Sensor And Multi Temporal Remote Sensing written by Anil Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-17 with Computers categories.
This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields. Key features: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.
Mapping Forest Changes Using Multi Temporal Remote Sensing Images
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Author : Yanlei Chen
language : en
Publisher:
Release Date : 2014
Mapping Forest Changes Using Multi Temporal Remote Sensing Images written by Yanlei Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
We developed a semi-automatic algorithm named Berkeley Indices Trajectory Extractor (BITE) to detect forest disturbances, especially slow-onset disturbances such as insect mortality, from time series of Landsat 5 Thematic Mapper (TM) images. BITE is a streamlined process that features trajectory extraction and interpretation of multiple spectral indices followed by an integration of all indices. The algorithm was tested over Grand County in Colorado, located in the Southern Rocky Mountains Ecoregion, where forests dominated by lodgepole pine have been under mountain pine beetle attack since 2000. We produced a disturbance map using BITE with an identification accuracy of 94.7% assessed from 602 validation sample pixels. The algorithm shows its robustness in deriving forest disturbance type and timing with the presence of different levels of atmospheric conditions, noises, pixel misregistration and residual cloud/snow cover in the imagery. Outputs of the BITE algorithm could be used in studies designed to increase understanding of the mechanisms of mountain pine beetle dispersal and tree mortality, as well as other types of forest disturbances. Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden for information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen Space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We then applied CBEST in mapping the forest change from 1986-2011 for the entire state of California, USA with over 400 Landsat TM images. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies. We present an efficient approach for a practice of large-area mapping of forest changes based on the Clustering Based on Eigen Space Transformation (CBEST) algorithm using remote sensing. By analyzing 450 Landsat Thematic Mapper (TM) satellite images from 1986 to 2011 with a five-year interval covering the entire state of California, USA, we derived a forest change type map, a forest loss map and a forest gain map. Although California has 99.6 million acres land area in total and the spatial resolution of Landsat TM is 30m, the computing time of the task took only 10 hours in a computer with an Intel 2.8 Ghz i5 CPU and 8 Gigabytes RAM. The overall accuracy of the forest cover in year 2011 was reported as 92.9% " 1.6%. We found that the estimated forest area changed from 28.20 " 1.98 million acres to 28.05 " 1.98 million acres from 1986-2011. In particular, our rough estimate indicates that each year California's forest experienced loss of 92 thousand acres and recovery of 85 thousand acres, resulting in seven thousand acres forest loss per year. In addition, during 1986-2011, around 12% of the forestland experienced changes, in which the change was 4% each for deforestation, afforestation and deforestation then recovered respectively. We concluded that the forestland in California had been managed in a sustainable manner over the 25 years, since no significantly directional changes were observed. Our approach made a tighter estimate of the true canopy coverage such that 29% of land in California is forestland, comparing with the statistics of 33% and 40% made by previous studies that had lower spatial resolution and shorter temporal coverage.
Remote Sensing Time Series
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Author : Claudia Kuenzer
language : en
Publisher: Springer
Release Date : 2015-04-28
Remote Sensing Time Series written by Claudia Kuenzer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-28 with Technology & Engineering categories.
This volume comprises an outstanding variety of chapters on Earth Observation based time series analyses, undertaken to reveal past and current land surface dynamics for large areas. What exactly are time series of Earth Observation data? Which sensors are available to generate real time series? How can they be processed to reveal their valuable hidden information? Which challenges are encountered on the way and which pre-processing is needed? And last but not least: which processes can be observed? How are large regions of our planet changing over time and which dynamics and trends are visible? These and many other questions are answered within this book “Remote Sensing Time Series Analyses – Revealing Land Surface Dynamics”. Internationally renowned experts from Europe, the USA and China present their exciting findings based on the exploitation of satellite data archives from well-known sensors such as AVHRR, MODIS, Landsat, ENVISAT, ERS and METOP amongst others. Selected review and methods chapters provide a good overview over time series processing and the recent advances in the optical and radar domain. A fine selection of application chapters addresses multi-class land cover and land use change at national to continental scale, the derivation of patterns of vegetation phenology, biomass assessments, investigations on snow cover duration and recent dynamics, as well as urban sprawl observed over time.
Ers 1 Study 94 Final Workshop Proceedings
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Author :
language : en
Publisher:
Release Date : 1997
Ers 1 Study 94 Final Workshop Proceedings written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Artificial satellites in ecology categories.
Climate Change And Adaptive Land Management In Southern Africa
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Author : Rasmus Revermann
language : en
Publisher:
Release Date : 2018
Climate Change And Adaptive Land Management In Southern Africa written by Rasmus Revermann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Earth Resources
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Author :
language : en
Publisher:
Release Date : 1978
Earth Resources written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Astronautics in earth sciences categories.
Object Based Image Analysis
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Author : Thomas Blaschke
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-08-09
Object Based Image Analysis written by Thomas Blaschke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-09 with Science categories.
This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).
Gis And Remote Sensing Applications In Biogeography And Ecology
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Author : Andrew C. Millington
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-11
Gis And Remote Sensing Applications In Biogeography And Ecology written by Andrew C. Millington and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-11 with Science categories.
In recent years, the conservation of tropical forests has received worldwide publicity whereas effective forest management, particularly for timber extraction, has attracted little attention and gained some notoriety. The overall aim of the present paper was to examine how environmental micro-variation in the Chiquibul Forest Reserve of Belize can influence species distribution and thereby inform management strategy. The paper deals first with the background to forest management in Belize, then considers the methodology used in the present study and fin~~ly assesses the preliminary results. The specific objectives are: (1) to assess the effects of changing scale on the variability of selected individual soil properties in forest plots within the same vegetation class; and (2) to examine the variation in soil properties and tree species distribution, and to integrate environmental and ecological data over a range of scales. BACKGROUND Whereas the global and regional distribution of tropical forests is broadly governed by climatic and altitudinal variation, individual forest tracts need to consider a range of other, locally important factors to explain species distribution and change. With very high species diversity, tropical forests present a major challenge in the attempt to unravel controlling factors in distribution and growth (Swaine et aI. 1987). Research that attempts to explain diversity has looked at species distribution according to a range of factors, with a general recognition that soil fertility plays a significant if ill defined role (Swaine 1996).