Phenology-based Land Cover Classification Using Landsat 8 Time Series PDF Download
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Author:
Publisher:
ISBN: 9789279408441
Category :
Languages : en
Pages : 56
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Book Description
This article describes the methodology and results of a new JRC phenology-based classification algorithm able to generate accurate land cover map sin a fully automatic manner from Landsat 8 (L8)remote sensed data available since 12th April 2013 at no charge throughout the USGS website. A preliminary study aiming to bypass the single date classification inaccuracy (mainly due to seasonality) using long term MODIS time series as a "driver" to fill gaps between high resolution data, has been carried out. The high global acquisition frequency (~16 days) and distribution policy are making Landsat 8product extremely suitable for near real time land cover mapping and monitoring. Five national parks in east Africa have been selected asstudy areas (Mahale Mountains, Mana Pools, West Lunga, Gorongosa, Tsimanampetsotsa); they are covering diverse eco-regions and vegetation types, from evergreen to deciduous. A buffer of 20km around each park has been considered as well.^Selected single date images were first preprocessed in order to convert raw DN values to top of atmosphere (TOA) reflectance and minimizes spectral differences caused by different acquisition time, sun elevation, sun-earth distance, and after processed by the algorithm to generate a thematic raster map with land cover classes. Is worth noting that the single date classification accuracy is closely related to the acquisition date of the image, the status of the vegetation and weather conditions such as cloud and shadows often present in tropical regions; here the need of developing a phenology based algorithm that considers the vegetation evolution and generates a more accurate land cover map including evergreen and deciduous discrimination on the basis of "frequency" rules. Land cover map shave been created for all parks and an exhaustive accuracy assessment has been carried out on Mahale Mountains and Tsimanampetsotsa.^The combined overall accuracy of 82.8% demonstrates the high potentiality of this method and makes it usable at either local or regional scale.
Author:
Publisher:
ISBN: 9789279408441
Category :
Languages : en
Pages : 56
Get Book Here
Book Description
This article describes the methodology and results of a new JRC phenology-based classification algorithm able to generate accurate land cover map sin a fully automatic manner from Landsat 8 (L8)remote sensed data available since 12th April 2013 at no charge throughout the USGS website. A preliminary study aiming to bypass the single date classification inaccuracy (mainly due to seasonality) using long term MODIS time series as a "driver" to fill gaps between high resolution data, has been carried out. The high global acquisition frequency (~16 days) and distribution policy are making Landsat 8product extremely suitable for near real time land cover mapping and monitoring. Five national parks in east Africa have been selected asstudy areas (Mahale Mountains, Mana Pools, West Lunga, Gorongosa, Tsimanampetsotsa); they are covering diverse eco-regions and vegetation types, from evergreen to deciduous. A buffer of 20km around each park has been considered as well.^Selected single date images were first preprocessed in order to convert raw DN values to top of atmosphere (TOA) reflectance and minimizes spectral differences caused by different acquisition time, sun elevation, sun-earth distance, and after processed by the algorithm to generate a thematic raster map with land cover classes. Is worth noting that the single date classification accuracy is closely related to the acquisition date of the image, the status of the vegetation and weather conditions such as cloud and shadows often present in tropical regions; here the need of developing a phenology based algorithm that considers the vegetation evolution and generates a more accurate land cover map including evergreen and deciduous discrimination on the basis of "frequency" rules. Land cover map shave been created for all parks and an exhaustive accuracy assessment has been carried out on Mahale Mountains and Tsimanampetsotsa.^The combined overall accuracy of 82.8% demonstrates the high potentiality of this method and makes it usable at either local or regional scale.
Author: Roger L. Claassen
Publisher: DIANE Publishing
ISBN: 1437988784
Category : Business & Economics
Languages : en
Pages : 85
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Book Description
Author: Claudia Kuenzer
Publisher: Springer
ISBN: 3319159674
Category : Technology & Engineering
Languages : en
Pages : 458
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Book Description
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.
Author: Bethany A. Bradley
Publisher:
ISBN:
Category : Biotic communities
Languages : en
Pages : 13
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Book Description
Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long-term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10-year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology-based land cover classification and an identification of phenological variability and 10-year trends stratified by land cover class. The analysis used a 10-year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology-based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10-year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long-term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon-juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional-scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.
Author: George Xian
Publisher: Frontiers Media SA
ISBN: 2832542689
Category : Science
Languages : en
Pages : 206
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Book Description
Author: Lalit Kumar
Publisher: MDPI
ISBN: 3038978841
Category : Science
Languages : en
Pages : 420
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Book Description
In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Author: Irene L. Hudson
Publisher: Springer Science & Business Media
ISBN: 9048133351
Category : Science
Languages : en
Pages : 525
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Book Description
As climate change continues to dominate the international environmental agenda, phenology – the study of the timing of recurring biological events – has received increasing research attention, leading to an emerging consensus that phenology can be viewed as an ‘early warning system’ for climate change impact. A multidisciplinary science involving many branches of ecology, geography and remote sensing, phenology to date has lacked a coherent methodological text. This new synthesis, including contributions from many of the world’s leading phenologists, therefore fills a critical gap in the current biological literature. Providing critiques of current methods, as well as detailing novel and emerging methodologies, the book, with its extensive suite of references, provides readers with an understanding of both the theoretical basis and the potential applications required to adopt and adapt new analytical and design methods. An invaluable source book for researchers and students in ecology and climate change science, the book also provides a useful reference for practitioners in a range of sectors, including human health, fisheries, forestry, agriculture and natural resource management.
Author: Asko Noormets
Publisher: Springer Science & Business Media
ISBN: 1441900268
Category : Science
Languages : en
Pages : 281
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Book Description
Terrestrial carbon balance is uncertain at the regional and global scale. A significant source of variability in mid-latitude ecosystems is related to the timing and duration of phenological phases. Spring phenology, in particular, has disproportionate effects on the annual carbon balance. However, the traditional phenological indices that are based on leaf-out and flowering times of select indicator species are not universally amenable for predicting the temporal dynamics of ecosystem carbon and water exchange. Phenology of Ecosystem Processes evaluates current applications of traditional phenology in carbon and H2O cycle research, as well as the potential to identify phenological signals in ecosystem processes themselves. The book summarizes recent progress in the understanding of the seasonal dynamics of ecosystem carbon and H2O fluxes, the novel use of various methods (stable isotopes, time-series, forward and inverse modeling), and the implications for remote sensing and global carbon cycle modeling. Each chapter includes a literature review, in order to present the state-of-the-science in the field and enhance the book’s usability as an educational aid, as well as a case study to exemplify the use and applicability of various methods. Chapters that apply a specific methodology summarize the successes and challenges of particular methods for quantifying the seasonal changes in ecosystem carbon, water and energy fluxes. The book will benefit global change researchers, modelers, and advanced students.
Author: Qihao Weng
Publisher: CRC Press
ISBN: 1351680560
Category : Science
Languages : en
Pages : 244
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Book Description
Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world’s natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions. Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization. This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.
Author: Food and Agriculture Organization of the United Nations
Publisher: Food & Agriculture Org.
ISBN: 9251345600
Category : Law
Languages : en
Pages : 58
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Book Description
Land cover assessment and monitoring of its dynamics are essential requirements for the sustainable management of natural resources, environmental protection, food security, humanitarian programmes as well as core data for monitoring and modelling. Land Cover (LC) data are therefore fundamental in fulfilling the mandates of many United Nations (UN), international and national institutions and programmes. Despite the recognition of such importance, current users of LC data still lack access to sufficient reliable or comparable baseline LC data. These data are essential to tackle the increasing concerns in regard to food security, environmental degradation, and climate change. Critically, maintaining and restoring land resources plays a vital task in tackling climate change, securing biodiversity, and maintaining crucial ecosystem services, while ensuring resilient livelihoods and food security.
