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Coastal zones are the epicenter of economic activities in most developing countries in the world hence are bound to change. With advancement in technology, monitoring degradation of coastal zones using remote sensing (RS) and geographic information system (GIS) has become a critical subject for scientific research. RS refers to the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft) while GIS is a system that creates, manages, analyzes, and maps all types of data. This paper reports an evolving Kenyan coastline over the past three decades using RS and GIS. Literature on chronological evolution and its impacts along this coastline is scanty and it is therefore envisaged that this work will instigate more enquiry into factors that were involved in landscape formation along the Kenyan coast. Prior reports indicate that research work done along this coastline were by field survey. We report a prograding beach on Mto Tamamba delta as displayed by multispectral Landsat imagery. The marine ecosystems, riparian communities and tourism industry are facing a major threat. The finding establishes hydrodynamic effects and human influence as key contributors of this menace. This study is aimed at detecting the coastline change using multispectral Landsat images from the years 1990 to 2021. For this purpose, coastlines belonging to these years were drawn numerically with the aid of GIS and RS on ENVI 5.3 software. We utilized maximum likelihood algorithm to categorize the images, and employed a time series methodology on classified endmembers to infer the presence of erosion and accretion. Using the image subset technique, areas prone to erosion and accretion within the coastal datum were identified and cut. The patterns of spatiotemporal trend of these endmembers were effectively employed to corroborate these findings. Change in distribution of endmembers over the 31 years' period was assessed using thematic change technique; an approach not previously applied in this study area. From the classified Mto Tamamba image, the pixel coverage area for each endmember was extracted. Validation using field campaign data gave an overall accuracy of 84% and kappa coefficient of 0.799. Taking into account that, the traditional methods of monitoring land degradation over a large area are time consuming and expensive; remote sensing data used in this study has offered alternatively cheap, consistent, reliable and retrievable data. [ABSTRACT FROM AUTHOR]

Earth observation (EO) data are increasingly being used to monitor vegetation and detect plant growth anomalies due to water stress, drought, or pests, as well as to monitor water availability, weather conditions, disaster risks, land use/land cover changes and to evaluate soil degradation. Satellite data are provided regularly by worldwide organizations, covering a wide variety of spatial, temporal and spectral characteristics. In addition, weather, climate and crop growth models provide early estimates of the expected weather and climatic patterns and yield, which can be improved by fusion with EO data. The AfriCultuReS project is capitalizing on the above to contribute towards an integrated agricultural monitoring and early warning system for Africa, supporting decision‐making in the field of food security. The aim of this article is to present the design of EO services within the project, and how they will support food security in Africa. The services designed cover the users' requirements related to climate, drought, land, livestock, crops, water, and weather. For each category of services, results from one case study are presented. The services will be distributed to the stakeholders and are expected to provide a continuous monitoring framework for early and accurate assessment of factors affecting food security in Africa. [ABSTRACT FROM AUTHOR]

PREFER (space-based information support for prevention and recovery of forest fires emergency in the Mediterranean area) is one of the Copernicus FP7 Emergency projects funded in 2012. It is uniquely devoted to forest pre- and post-fire management. The overall goal of the project is to develop and demonstrate a preoperational portfolio of products, based on Earth observation data for helping fire management on a Mediterranean scale. Samples of the PREFER information products are available to stakeholders through the project Geoserver (prefer.cgspace.it). The project foresees the utilization of satellite images’ optical and SAR at medium (MODIS—moderate resolution imaging spectroradiometer), high (Landsat, Spot-Satellite Pour l’Observation de la Terre), and very high (KOMPSAT—Korea Multi-Purpose Satellite, RapidEye, Pleiades, COSMO-SkyMed—constellation of small satellites for Mediterranean basin observation, and TanDEM-X—TerraSAR-X add on for digital elevation measurement) spatial resolution, and a refresh rate of the products varying from high (days) to low (twice a month) to very low (once a year). The present paper is devoted to introducing the methodology developed for computing one of the project product, i.e., the damage-severity map. These maps provide the level of damage caused in vegetated areas by fires. Further, the paper aims at presenting the results of the validation of such product carried out during the first semester of 2015. The methodology is based on the utilization of Landsat8/OLI images. [ABSTRACT FROM PUBLISHER]

The pilot project SIGRI (Sistema Integrato per la Gestione del Rischio Incendi) of the Italian Space Agency aims at developing an integrated system for the management of wildfires. The system provides satellite-based products capable of assisting in all phases of fire combat activities: forecast, detection, damage assessment, and recovery. The SIGRI goals were achieved by implementing consolidated methodologies, and/or developing innovative tools and methods for the automated analysis of multispectral, Synthetic Aperture Radar, and non-EO data. This paper focuses on the testing and validation of algorithms developed during the final phase of SIGRI. The validation of the products generated is an important phase in which their potential is assessed, and algorithms are calibrated. We discuss the results of the validation process of the main chain of unsupervised products: Modified Fire Probability Index by multispectral moderate resolution data (prevention), early detection of fire Hot Spots by multispectral very-high temporal resolution data (response), and identification and mapping of Burn Scars (damage assessment) by high-spatial resolution electro-optical and Synthetic Aperture Radar data. [ABSTRACT FROM PUBLISHER]

Sensible heat flux (H) has a large impact on energy exchange between the surface and the atmosphere and, thus, affects climate change and climatic and hydrological modelling. In the past, remote sensing of H has been a major area of interest and, as a result, various methods have been established for its retrieval. However, large discrepancies between measured and simulated values of H have been observed over land surfaces because of various assumptions and simplifications. This article presents a generalized algorithm for the estimation of sensible heat flux that is suitable for a wide range of atmospheric and terrestrial conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Standard built-in atmospheric profiles in Fast Atmospheric Signature Code (FASCODE) together with atmospheric conditions obtained by periodic radio sounding, once a week, performed at the Broglio Space Centre in Malindi, Kenya, were used in simulating MODIS data at 11.03 and 12.02 μm wavelengths using PcLnWin software. This new approach improves the form of the Mito algorithm, developed to determine surface temperature, by removing some of the assumptions underlying the algorithm – for example, the assumption that air temperature T a is approximately equal to surface temperature T s. The resulting bulk aerodynamic resistance equation allows the formulation of a general algorithm for the determination of H, which takes into account the surface emittance effect, water vapour column (WVC), canopy properties, air temperature and different atmospheric stabilities. Unlike other conventional methods developed earlier for the determination of H, a prior knowledge of surface temperature as an auxiliary input is not necessary in this new algorithm. The estimates of sensible heat flux derived from MODIS using the proposed algorithm compared well with in situ measurements, giving a good correlation coefficient of r = 0.9. [ABSTRACT FROM PUBLISHER]

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases. [ABSTRACT FROM AUTHOR]

An assessment of the suitability of the Advanced Very High Resolution Radiometer (AVHRR) vegetation index to estimate land degradation in semi-arid areas has been carried out. comparing its behaviour with that of vegetation indices based on Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images. Notwithstanding the importance of the classic Normalized Difference Vegetation Index (NDVI) indicator, based on red NIR channels, several studies have identified some limitations related to its use, such as its dependence on the atmospheric profile, saturation problems, non-linearity in biophysical coupling with Leaf Area Index (LAI) and canopy background contamination. The relatively recent Enhanced Vegetation Index (EVI) overcomes these limits, using the information related to the blue channel arid a soil adjustment factor, SeaWiFS data allow the computation of both vegetation indices. On the other hand, the NDVI based on AVURR can be computed back in time to the 1980s, allowing a sufficient time span to obtain information on the desertification trend of the considered region (northern Kenya). In conclusion, taking advantage of both datasets, the accuracy of a change detection technique based on the classic NDVI has been confirmed as suitable for revealing any desertification trend. [ABSTRACT FROM AUTHOR]