The use of AVHRR data for large area vegetation studies

This thesis compares the attributes and limitations of the various Advanced Very High Resolution Radiometer (AVHRR) data types for large area vegetation studies in the context of global change research. In chapter 1, some of the major scientific issues customarily associated with the Global Change concept are outlined, and the specific role of vegetation dynamics in the earth's energy, hydrological and biogeochemical cycles is discussed. Deficiencies in currently available global vegetation data sets and the role of remote sensing as a new source of information are reviewed. This review is supplemented by detailed discussion of the spectral properties of vegetation, soil and water in chapter 2. In chapter 3 the Normalised Difference Vegetation Index (NDVI) and the Perpendicular Vegetation Index (PVI) from 1 km resolution AVHRR High Resolution Picture Transmission (HRPT) data are compared as a means of providing biomass estimates for large geographical areas. The PVI provides better measures of biomass than NDVI, particularly where cover is sparse. Caution in the use of NDVI is recommended. Problems of accurate ground location because of the AVHRR's coarse resolution are highlighted. High resolution (80 m) satellite imagery from the Landsat Multi Spectral Scanner (MSS) is shown to be an effective intermediate step. Comparison with MSS also shows the effect of landscape structure on areal estimates of vegetation cover from the AVHRR HRPT. In chapter 4, MSS and HRPT comparisons show that sensor-induced spatial autocorrelation in AVHRR HRPT data can limit the use of these data for the identification of spatial structure and pattern at full resolution. The same limitation on the use of full resolution data is also found to apply to the spatially sampled AVHRR Global Area Coverage (GAC) archives. In this case local variance induced by sampling in GAC data generation is thought to be the cause. In chapter 5 the structural analysis is extended across a range of resolutions. Sampling artefacts are found for all West African ecosystems at full GAC resolution (4 km), though at coarse resolutions spatial patterns observed with the GAC and the unsampled AVHRR HRPT are very similar. Differences between the two are most pronounced where landscape features are either points or lines. A resolution of 12 km is found to suppress the negative effects of the sampling for all West African ecosystems. At resolutions coarser than 12 km, the GAC data are just as good a measure of landscape structure as the unsampled data. The relevance of these coarse measurement scales to global change research is discussed. Chapter 6 shows that transitions across major ecological zones can be detected at resolutions coarser than 12 km, though the agreement between the sampled and unsampled AVHRR changes both with geographical location and time. This is particularly so for ecosystems affected by fire. Fire is increasingly recognised as a key factor in both the study of global vegetation dynamics directly, and for global change research. This is the focus of chapter 7. Problems relating to the poor sensitivity of the AVHRR's middle-infrared and thermal channels are identified and resolved. The GAC sampling method is shown to affect the sensitivity of these data for fire detection in relation to ecosystem and season. Regional stratification is highlighted as a means of improving fire analysis over large areas, improving land cover classification, and helping in the definition of new cover classes (particularly at continental and global scales) Finally, the results of the previous chapters are discussed with reference to the existing and planned processed AVHRR data archives. The importance of the 12 km limit with reference to the GAC archives is stressed, the need for circumspection in the use of NDVI reiterated, particularly with reference to the derivation of physical measurements from AVHRR, and the requirement for new synergistic approaches to the use of AVHRR with other data emphasised.Ph

On the use of marker data to determine the kinetics of the digestive behaviour of feeds

A model of the transport process that follows the progress of digesta successively through the small intestine of a monogastric is investigated. The process is multi-phase and multi-constituent, as described in detail by Bastianelli et al. [J. Anim. Sci., 74:1873–1887, 1996]. The model describes the movement of marker substances that are used to obtain data on the interactions between the intestinal sections and digesta with differing levels of soluble fibre. A multi-stage process is modelled by a set of coupled first order linear differential equations. Solutions of steady and initial value problems provide information on the transfer rates of the processes. Properties of the solutions as functions of system parameters are examined. References M. Renton, J. Hanan and K. Burrage, Using the canonical modelling approach to simplify the simulation of function in functional-structural plant models. New Phytologist, 166:845–857, 2005. doi:10.1111/j.1469-8137.2005.01330.x D. Bastianelli, D. Sauvant and A. Rerat, Mathematical modeling of digestion and nutrient absorption in pigs. J. Animal Science, 74:1873–1887, 1996. http://www.journalofanimalscience.org/content/74/8/1873.abstract R. G. Lentle and P. W. M. Janssen, Manipulating Digestion with Foods designed to Change the Physical Characteristics of digesta. Critical Reviews in Food Science and Nutrition, 50:130–145, 2010. doi:10.1080/10408390802248726 J. France, J. H. M. Thornley, M. S. Dhanoa and R. C. Siddons, On the mathematics of digesta flow kinetics. Journal of Theoretical Biology, 113:743–758, 1985. doi:10.1016/S0022-5193(85)80191-0 A. Mazanov and J. V. Nolan, Simulation of the dynamics of nitrogen metabolism in sheep. British Journal of Nutrition, 35:149–174, 1976. doi:10.1079/BJN19760017 A. Mazanov, Stability of Multi-pool Models with Lags. Journal of Theoretical Biology, 59:429–442, 1976. doi:10.1016/0022-5193(76)90181-

The Global Landsat Archive: Status, Consolidation, and Direction

New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had twoadditional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

A Land Cover Map of Africa. Carte de l'Occupation du Sol de l'Afrique.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Remote Sensing of Environment: Current status of Landsat program, science, and applications

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat- 1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat

A comparision of supervised maximum likelihood and decision tree classification for crop cover estimation from multiespectral Landsat MSS data

Pages:

Global and Regional Land Cover Characterization from Satellite Data. An Introduction to the Special Issue.

Abstract not availableJRC.(SAI)-Space Application Institut

Mapping soil and rock variation from satellite images in the Sahel

Pages: 13