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Book Optical Time Series Analysis for Monitoring Landcover Changes in Fiji

Download or read book Optical Time Series Analysis for Monitoring Landcover Changes in Fiji written by A. Paschalidou and published by . This book was released on 2013 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring forest cover dynamics is crucial for any initiative to combat tropical deforestation. Remote sensing approaches have the potential to determine forest cover dynamics, providing valuable information for monitoring mechanisms, such as REDD+. This MSc research assessed the potential of optical time series analysis to capture forest cover changes related to harvest operations. Landsat 7 derived NDVI and NDFI time series (2000 - 2012) were analysed by BFASTmonitor algorithm to retrieve temporal changes in forest cover of a study area. We validated the change detection ability and estimation accuracy of BFASTmonitor based on cross-comparison with a generated reference dataset and further, we assessed the performance of the examined spectral indices as change indicators. In addition, we examined factors potentially influencing the accuracy of the proposed method (e.g. magnitude of change, cloud contamination).

Book Advances in characterizing and monitoring land cover use and associated ecosystem changes using remote sensing data

Download or read book Advances in characterizing and monitoring land cover use and associated ecosystem changes using remote sensing data written by George Xian and published by Frontiers Media SA. This book was released on 2024-01-09 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Towards an Innovative Remote Sensing Tool for Tropical Forest Change Monitoring by Integrating Spatial Segments and Time Series Analysis

Download or read book Towards an Innovative Remote Sensing Tool for Tropical Forest Change Monitoring by Integrating Spatial Segments and Time Series Analysis written by J. González de Tánago Meñaca and published by . This book was released on 2012 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: The net tropical forest carbon emission still remains uncertain, challenging the REDD+ monitoring system. This uncertainty is extensively related to the uncertainties in the estimations of the forest cover loss, the extent and intensity of forest degradation events, and the reforestation and regrowth rate. Complex spatiotemporal analysis techniques are needed to improve both spatially and temporally the estimation precision of the forest cover changes on tropical areas. Many remote sensing techniques have been developed to detect Land Cover Changes (LCC), including recent object based approaches exploiting the advantage of extracting changes in their spatial context. However, these previous works have been applied in a limited temporal scale, mostly bi-temporal or reduced multi-temporal image comparison (only few observation dates). In contrast, emerging LCC analysis approaches exploit the full temporal information of land cover dynamics provided by remote sensing dense time series, though they have been mostly applied in a pixel-based approach. Only little research has been done combining both approaches. This MSc. thesis explored the potential of spatiotemporal segmentation and time series analysis. First, LCC was estimated from Landsat time series by the Iterative Reweighted Multivariate Alteration Detection algorithm (iMAD) and spatiotemporal segments were delineated extracting LCC objects. Second, a time series analysis by the mean of the “Break For Additive Seasonal and Trend” (BFAST) algorithm over the LCC segments was performed for characterizing the temporal pattern of the segments. Finally, the information provided by both analysis was integrated for classifying the segments. This spatial and temporal information integration enhanced the capabilities of characterization of the spatial and temporal dynamics of tropical forest disturbances, contributing to increase the understanding of the immediate and long term implications of human activities over the tropical forest. Data abundance was found to be of major importance. Forest and non-forest related land cover changes within the study area have been detected producing satisfactory results. Especially the relations of segments and temporal profile’s need to be further analyzed by making use of multi-temporal change detection products and well framed multivariate analysis.

Book Global Forest Monitoring from Earth Observation

Download or read book Global Forest Monitoring from Earth Observation written by Frederic Achard and published by CRC Press. This book was released on 2012-11-19 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forests provide a large range of beneficial services, including tangible ones such as timber and recreation, and intangible services such as climate regulation, biodiversity, and watershed protection. On the other hand, forests can also be considered roadblocks to progress that occupy space more productively used for agriculture, making consideration of their regulating services crucial for balancing land use and forest loss. Monitoring forest cover and loss is critical for obtaining the data necessary to help define what is needed to maintain the varying forest service requirements in different parts of the world. There is an increasing need for timely and accurate forest change information, and consequently a greater interest in monitoring those changes. Global Forest Monitoring from Earth Observation covers the very recent developments undertaken for monitoring forest areas from global to national levels using Earth observation satellite data. It describes operational tools and systems for monitoring forest ecosystems, discussing why and how researchers currently use remotely sensed data to study forest cover and loss over large areas. The book introduces the role of forests in providing ecosystem services and the need for monitoring their change over time, followed by an overview of the use of earth observation data to support forest monitoring. It discusses general methodological differences, including wall-to-wall mapping and sampling approaches, as well as data availability. This book provides excellent coverage of the research and applications of forest monitoring, indicator mapping at coarse spatial resolution, sample-based assessments, and wall-to-wall mapping at medium spatial resolution using optical remote sensing datasets, such as MODIS and Landsat. It examines the use of radar imagery in forest monitoring and presents a number of operational systems, from Brazil’s PRODES and DETER products to Australia’s NCAS system. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of global forest monitoring methods and shows how state-of-the-art technologies may soon provide key data for creating more balanced policies.

Book Merging Data Streams and Remote Sensing Change Detection Routines for Stand replacing Disturbances in Canada

Download or read book Merging Data Streams and Remote Sensing Change Detection Routines for Stand replacing Disturbances in Canada written by Elijah Perez and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Studying how the Earth's surface changes with time allows conservation managers a better understanding of land cover and composition for research and environmental management. Knowledge of where and when change occurs is important to land management and conservation in Canada. Satellites, like the U.S. Geological Survey's (USGS) Landsat 8, provide a regularly updated sequence of images that are used in various terrestrial monitoring approaches. In the Canadian context, Landsat 8 typically yields one or two clear images each growing season for any given location. The European Space Agency's (ESA) pair of Sentinel 2 satellites, perform marginally better in the Canadian context due to their higher resolution and more frequent revisit times. Separately, either system has limited potential to construct a time-series of clear observations for change detection. However, because of their technical compatibility combining Landsat-8 and Sentinel 2 data can enrich the total pool of clear Earth observations and make it possible to improve terrestrial monitoring approaches. This is particularly helpful for creating annual composite images to represent a growing season across an expansive region that can not otherwise be captured in one image. Using composited data, monitoring programs construct time series for trend-based detection of land-use / land-cover (LULC) change (i.e., disturbance). There are limits to constructing time series of change detection in this way, however. For example, if a method collects images from August 1st plus/minus thirty days to create a single clear image to represent peak growing season vegetation, then changes after this compositing period are not captured. Moreover, because terminal years of the time series lack a subsequent year of observations to confirm trends and values, change detection in terminal years is less certain than intermediate years. To try and resolve these issues, this thesis develops a novel classification method, dubbed Shrinking Latency in Multiple Streams (SLIMS), to combine two open-access satellite image streams (Landsat 8 and Sentinel 2). SLIMS captures the within-year spectral signature of forest disturbance and creates a sequence of near real-time classifications of forest disturbance. Subsequently, this thesis combines SLIMS with the Bayesian Updating of Land Cover (BULC) algorithm to synthesize these unpolished, individually noisy land cover classifications into a series of more accurate land cover classifications. Using a free and accessible cloud-based computing platform (i.e., Google Earth Engine), this work computes a forest change time series at a fine spatial resolution (10 metre) and very fine time slices (~5 days) to provide more detailed information concerning forest disturbances to conservation managers. This thesis discusses the opportunities and challenges of improving annual scale trend-based change detection approaches like the Canadian Forestry Service's Composite to Change (C2C) algorithm, namely in the a) time of compositing and b) the terminal years of a time series"--

Book Detecting Land cover Change Using MODIS Time series Data

Download or read book Detecting Land cover Change Using MODIS Time series Data written by Waldo Kleynhans and published by . This book was released on 2011 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Fusion of MODIS and Landsat Data to Allow Near Real time Monitoring of Land Surface Change

Download or read book Fusion of MODIS and Landsat Data to Allow Near Real time Monitoring of Land Surface Change written by Qinchuan Xin and published by . This book was released on 2012 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: A new methodology for fusion of MODIS and Landsat data improves monitoring of land surface change and snow mapping. This fusion method is based on prediction of MODIS data using a time-series of Landsat data. An underlying hypothesis is that the predicted MODIS images will form a more stable basis for comparison with new MODIS images than previous MODIS images. Correlations between predicted and observed MODIS images are higher than for successive days of MODIS data, confirming our hypothesis. Differences in the spectral signatures between predicted and real MODIS images become the "signal" used detect land surface change. Tests of the fusion method to detect forest clearing show producer's and user's accuracies of 86% and 85%, respectively. Cleared patches of forest as small as 5-6 ha in size can be detected, a considerable improvement over current published results. Additionally, the fusion method can be used to map snow cover on a daily basis and is more accurate than current operational MODIS snow products. The encouraging results indicate that the fusion method holds promise for improving monitoring of land surface change in near real-time.

Book Time Series Analysis of High Resolution Remote Sensing Data to Assess Degradation of Vegetation Cover of the Island of Socotra Yemen

Download or read book Time Series Analysis of High Resolution Remote Sensing Data to Assess Degradation of Vegetation Cover of the Island of Socotra Yemen written by Abdulmaged Alhemiary and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The island of Socotra has long been in geographical isolation, hence nearly 30% of the plant species are believed to be endemic to the island. Until the end of 20th century there was only very little and incomplete information and literature about the vegetation on the island. This isolation broke down in 1990 with the country unification in which then the island received much attention. Subsequently the scientific knowledge of the local flora slowly increased, but many of plant species are now reported to be confined into small populations, hence being particularly vulnerable to habitat loss, overgrazing, as well as urban expansion. 1. The overall objective of this research attempted to assess and examine the trends of vegetation changes since 1972 to 2010 with the use of Landsat MSS, TM and ETM+ images and to investigate the related driving factors, such as rainfall, grazing pressure changes and underlying spatial variability of the landscape. This is to answer the overall question: Is there a trend in biomass, cover and species composition on Socotra Island over the last 40 years? If so, is that trend associated with the rainfall patterns? What are the drivers behind the vegetation change? And then how can we define changes in patterns or changes in this study area? 2. From a methodological point of view, our approach of systematically using remote sensing technology data proved scientifically an applicable tool to improve our understanding of the spatial complexity and heterogeneity of the vegetation cover as well as to provide a conceptual method with specific data for monitoring the changes over this time period. Our data obtained from these different Landsat sensors during the study period were - after many sophisticated processing steps - essentially able to provide time series information for Normalized Difference Vegetation Index (NDVI) data and to assess the long term trend in vegetation cover in the island. 3. Moreover, our approach combining supervised maximum-likelihood and unsupervised classification with the pre- and the post-classification approaches besides the knowledge based classification was table to provide sufficient results to distinguish and to map nine (9) terrestrial vegetation cover classes. The overall accuracy (compared with ground truth data) was about 91%, 77%, 70% and 72% for the images 2005, 1994, 1984 and 1972 respectively. Consecutively, the GIS analysis allowed estimates of highly valuable information as absolute areas and relative coverage of particular vegetation classes over the island with their spatial distribution and also their ecological requirements. Analysis of climatic conditions and NDVI 4. As a results of the complex topography of the study area and the wide climate range, with the guidance of prior knowledge of functional relationships between site parameters, ecosystem and the specific form of biological production, our work resulted in a division of the entire area into six variously sized ecosystem units, which were enough to properly depict the spatial heterogeneity of the rainfall and vegetation and to assist reflecting the influence and reaction between environmental parameters as well as it might have significance both for development of resources and for conservation of environment.

Book Time Series Change Detection

Download or read book Time Series Change Detection written by Shyam Boriah and published by . This book was released on 2010 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Time Series Segmentation Techniques for Land Cover Change Detection

Download or read book Time Series Segmentation Techniques for Land Cover Change Detection written by Ashish Garg and published by . This book was released on 2013 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Remote Sensing Time Series for Vegetation Monitoring

Download or read book Remote Sensing Time Series for Vegetation Monitoring written by and published by . This book was released on 2019 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Time Series Generation and Classification of MODIS Data for Land Cover Mapping

Download or read book Time Series Generation and Classification of MODIS Data for Land Cover Mapping written by René Roland Colditz and published by . This book was released on 2007 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: