Accurate fuel mapping is vital for wildfire risk assessment and management. This study combines Remote Sensing (RS) data and Machine Learning (ML) to differentiate fire behavior fuel models. Three ML approaches - Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) - are compared in terms of accuracy, recall, and F1 score. Employing Sentinel-2 imagery, the ML-based classification accurately categorizes fuel types into eight main classes: broadleaf, conifer, shrub, grass, bare soil, burned area, urban area, and water. Past results in a Sardinia test case were promising, with CNN achieving impressive metrics - accuracy, recall, and F1 score - each at 99%. Notably, the network exhibits high validation score in identifying classes in unseen pixels: broadleaf at 99%, conifer at 79%, shrub at 76%, and grass at 84%. Subclasses, aligned with the Standard Scott and Burgan (2005) system, were derived from the eight classes using Above Ground Biomass (AGB) and Bio-Climatic (BC) maps to refine fuel mapping. A significant enhancement of this work involves testing the proposed method in Portugal and Greece to validate its robustness across diverse geographical regions. Furthermore, the final fuel type maps are rigorously validated by comparing them with FirEUrisk’s pre-existing validated fuel maps, providing a benchmark to assess the accuracy and reliability of the new maps.

The planetary crisis regarding water resources means that new methods are needed to monitor large areas of water basins that are threatened by chemical and natural pollutants, together with climate change. With the aim to monitor the pollution status of some Sites of National Interest (SIN) which represent very large contaminated Italian areas classified as dangerous, we are applying new or already well established algorithms to optical satellite images. In particular, a recently introduced oil spill detection algorithm [1] was able to, consistently and reliably, confirm the presence of oil in five polluted lake waters analysed. The combination of this algorithm with metagenomic analysis of the spill areas detected by the satellite allowed to identify drivers of the microbial response to oil pollution. Based on ortholog abundances, metabolic pathway reconstruction carried out in Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) software highlighted the degradative capacity of these microorganisms. These microorganisms could be suitable candidates for treatment of crude oil, aromatic hydrocarbons and the desulfurization of persistent petroleum substances like dibenzothiophene. Building upon this recent algorithmic development, the SatellOmic project funded by the Italian Space Agency (ASI) focuses on the pre-operational application of such integrated approach combining satellite sensing and metagenomics analyses for real-time monitoring of water bodies threatened by oil spills, as well as for the design of recovery strategies based on the use of valuable hydrocarbonoclastic microorganisms.

The present paper aims at addressing the issue of how the use of multispectral and hyperspectral satellite imagery can help identify the presence of diseases and toxins within the agri-food sector in order to provide technical and scientific support to Food Security and Safety policies in Africa. This topic is one among those addressed within the AFRI4Cast project. A project, funded by European Space Agency, that in its entirety will provide national-, regional-, parcel-, pixel-specific in season production estimates of rust disease outbreak probability.

After carbon dioxide, methane is the second most relevant anthropogenic greenhouse gas in terms of its impact on climate change. Landfill-related emissions make up 15-18% of total methane emissions. Estimating methane emissions globally is crucial for effective global warming mitigation. To this end, this study, part of the CLEAR-UP project funded by the Italian Space Agency, investigates the application of the hyperspectral satellite platform PRISMA (PRecursore IperSpettrale della Missione Applicativa) for monitoring methane emissions from landfills. The retrieval methodology is based on an improved matched filter approach, and in this paper, we intend to assess PRISMA’s capacity to detect methane emissions by focusing on large-size sites as a case study. This research not only demonstrates PRISMA's potential in environmental monitoring but also contributes to strategies aimed at mitigating climate change impacts through improved waste management.

Reliable and efficient crop field segmentation is a fundamental pre-requisite for statistical analyses of agricultural practices. Traditional methodologies such as the Canny-Watershed (CW) algorithm require expert tuning of parameters for optimal results. This paper introduces an innovative approach for crop field segmentation in high-resolution satellite images, leveraging the use of multi-temporal Canny edge detection to train convolutional neural networks (CNNs) and fully automate the segmentation process. The Canny filter, applied to Sentinel-2 multi-temporal data, provides refined input for training ResUnet models, facilitating the generation of a generalized training dataset. ResUnet allows the model to learn complex features from diverse data, encapsulating seasonal changes. The dataset was specifically designed to enable the model to make accurate predictions from a single image, significantly outperforming traditional Canny filter predictions. In addition, the ResUnet may be applied to multiple images, generating output masks that, when overlayed, produce better results with respect to the multi-temporal Canny approach, demonstrating superior ability in recognizing real field boundaries while reducing false detections.To enhance generalizability, ResUnet is trained on a varied global dataset capturing a wide range of agricultural conditions and seasonal variations. This generalized model is tested across different regions and seasons, and preliminary results indicate that the proposed approach offers operational efficiency and accuracy in automating crop field segmentation.

Temporal changes in soil moisture (SM) and green vegetation affecting the spectral reflectance can heavily reduce the accuracy of topsoil properties estimation from satellite imaging. To minimize these effects on the soil organic carbon (SOC), sand, silt and clay estimations, an external parameter orthogonalization (EPO) model developed using laboratory based measured spectra was tested on PRISMA hyperspectral satellite data. The estimation of soil properties was performed using different machine learning algorithms. The results show that as compared to the uncorrected spectra, removing the effects of both green vegetation and SM (EPOSM+GV) from the reflectance spectra leads to 18%, 13%, 10%, and 24% improvement in the R2for clay, silt, sand and SOC retrieval, respectively. The Gaussian Process Regression (GPR) algorithm provides the best results for all of the soil properties with an RMSE of 9.5%, 14.2%, 6.9% and 0.68% for clay, silt, sand and SOC retrievals, respectively.

Live Fuel Moisture Content (LFMC) is a fundamental variable of fire meteorology, fire behavior models and fire danger indices. The possibility of creating remote sensing LFMC products by directly training machine learning algorithms onto field measurements is severely limited by the sparse geographic distribution of such datasets, which are mostly concentrated in USA, Mediterranean Europe and Australia. Therefore, the physical foundation provided by a Radiative Transfer Model (RTM) such as PROSAIL remains an irreplaceable component of any LFMC product designed for global applicability. However, radiative transfer model inversion usually requires a lot of time and computing power. A Look-Up Table (LUT) approach saves time by running the model forward only during LUT creation, but still requires each row of the LUT to be compared with each observation when searching for the optimal solution. In this paper, we trained a random forest regressor on LUTs generated using PROSAIL and the Jasinski geometric model, aiming to exploit the efficiency of ML regressors to speed up calculation time while still maintaining the foundation of a physically-based approach that enables global applicability. The regressor was trained specifically to invert the LFMC, and was tested using Globe-LFMC v2 field-collected LFMC timeseries as a ground truth. The inversion, while returning results comparable in accuracy with the ones obtained using conventional methods, is now performed virtually instantly.

Land cover, or the biophysical cover of the earth's surface, plays an essential role in climate and environmental dynamics. Processes involving land cover change, are among the factors that most threaten the ecosystems sustainability and services. The objective of the work is to explore the potential of the PRISMA multi-temporal hyperspectral imagery in generating new EO products to complement/improve the products provided by Copernicus' Land Monitoring Service for the analysis and monitoring of complex and fragile ecosystems such as the coastal Metaponto (Southern Italy) by estimating of the land biological and economic productivity loss and land degradation vulnerability. Preliminary results showed that an improvement in ecosystem mapping is supported by the use of Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) and a hybrid approach to define the vegetation trait, leads to significant improvement in the damage assessment and land degradation assessment.

The THERESA (THErmal infRarEd SBG Algorithms) project aims to enhance algorithms for processing Thermal InfraRed data from the SBG-TIR (Surface Biology and Geology – Thermal InfraRed) mission. Starting from state-of-the-art algorithms, THERESA takes in account the mission's technical features to develop algorithms exploiting diverse spectral channels. During the two years lifetime of the project, THERESA will contribute to enhance the investigation of terrestrial phenomena by using both visible and thermal images. The thematic areas that will benefit from SBG-TIR data range from the vegetation analysis to the volcanic eruptions and fires monitoring. Several parameters will be achieved such as the estimation of ash and SO2emissions from volcanoes, the surface temperature, the detection of hotspots as well as the FRP (Fire Radiative Power) in case of HTE’s (High Temperature Events). THERESA's innovation lies in algorithms advancements; the project offers a 360-degree support to the SBG-TIR mission.

Monitoring the United Nation Sustainable Development Goals (UN-SDGs) calls for adequate methodologies to extract specific indicators of status for each realm (e.g., air, water, land) and socio-economic-environmental issue. Earth observation has been considered, since the beginning, one of the pillars of this task, but the use of Synthetic Aperture Radar is still limited. This work report about the results achieved by a joint research project between Italy and Argentina, whose goal was to design and implement data processing techniques able to exploit the data from the COSMO-SkyMed and SAOCOM constellations, in conjunction with those by the Sentinel constellation by the European Space Agency and provide country-wide indicators for some of the UN-SDGs.

The detection of burnt areas from satellite imagery seems one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images [1]. This change detection analysis is usually carried out by comparing the values of specific spectral indices such as: NBR (Normalized Burn Ratio) [2], BAI (Burned Area Index) [3], MIRBI (Mid Infrared Burn Index) [4]. However, some potential sources of error arise, in particular when near-real-time automated approaches are adopted. An automated approach is needed when the burnt area monitoring should operate systematically on a given area of large sizes (country). Potential sources of error are: clouds on the pre- or post-event images, clouds or topographic shadows [5], agricultural practices, image pixel size, level of damage, etc. [6]Sources of difference between different existing global datasets of burned areas based on satellite images could be related to the spatial resolution of the images used, the land cover mask adopted to avoid false alarms, the quality of the cloud and shadow masks. This paper aims at comparing several burned area databases (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective of identifying and characterizing their approximations. In order to do so, ground survey data provided by CUFA (Comando unità forestali, ambientali e agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) have been used. In using ground data, the accessibility of the burned area has been also considered in order to account for the approximations due to the difficulties in reaching the affected area.

The incidence of wildfires and megafires with their disastrous consequences is increasing all over the planet, both in terms of burned area surface and in terms of power released by the fires. For this reason, the strategic importance of wildfire prevention is greater than ever, and providing the decision makers with powerful and state-of-the-art tools is an utmost priority. To this end, satellite observations offer a privileged platform to cover large spatial scales with a high time frequency. However, the most popular fire danger products tend to cover very large spatial scales at a coarse resolution, and inherently lack the capability to provide a level of detail which is very useful at the local scale. At the same time, using different products at different spatial scales would be impractical, and would increase the workload and training requirements of the personnel. To this end, this paper proposes a scalable-resolution fire danger index, named Daily Fire Danger Index, based on Sentinel-2 L2A and Sentinel-3 Synergy products. This novel index exploits both weather and satellite data to estimate all the main fire weather variables, and is calibrated using the historical records of wildfire occurrence in the area of interest.

Live Fuel Moisture Content is strongly related to the proneness of live vegetation to ignite and burn, and as such, it is one of the fundamental variables in fire ignition and fire behavior models. While in-situ measurements of this physical variable remain irreplaceable and invaluable, long-term historical records are mostly concentrated in select regions of Mediterranean Europe, United States and Australia. For this and other reasons, there is a strong interest in obtaining estimates based on remotely sensed data on a large spatial scale. To this end, this paper proposes a Live Fuel Moisture Content product based on optical satellite imagery acquired by the European satellites Sentinel-2 and Sentinel-3, and an inversion procedure based on the PROSAIL family of radiative transfer models. An early validation of the product is presented at the S2 and S3 spatial scales using field data provided by the Portuguese Association for the Development of Industrial Aerodynamics of the University of Coimbra and the Institute of BioEconomy of the Italian National Research Council.

The aim of this paper is to classify irrigated crop fields in Kenya during the year 2021 using Machine-Learning (ML) techniques and Sentinel-2 time-series data, identifying the best performing classifier and combinations of spectral indices in terms of accuracy scores.To observe changes due to irrigation and monitoring vegetation status, remote sensing data can provide valuable information. By combining different spectral bands, high-resolution multispectral Sentinel-2 imagery can provide various vegetation indices, sensitive to certain aspects of crop status and moisture content.To distinguish irrigated fields using these indices, ML methods for multivariate time-series classification can be applied. By training a ML model on time-series data consisting of six different vegetation indices, two different classifiers were tested: the Time Series Forest (TSF) and the Weasel-Muse algorithm (one of the most promising according to Ruiz et al., 2020).In conclusion, both algorithms demonstrated to be reliable for multi-index Sentinel-2 time-series classification, and the Normalized Multiband Drought Index and the Modified Normalized Water Index were revealed as good indicators to detect irrigation.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

The PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite, launched by the Italian Space Agency (ASI) is presently operational on a global scale. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 234 bands and a panchromatic (PAN) camera (400–750 nm). In the context of this work, we intend to determine the two noise components (photon and thermal noise) and assess SNR with an image based approach. Results show that the SNR evaluation assessed through the collected images is coherent with the mission requirements and that the PRISMA noise components, derived on the fragmented Pignola test site, in Southern Italy, are comparable to the ones derived on the Rail Road Valley calibration site.

This paper presents the results of a collaboration between Italian and Chinese research groups carried out under the context of the GEO work programme and AfricultuReS H2020 project. The paper encompasses three main aspects: (a) the description of the results achieved on the high resolution crop mapping carried out in some African countries in the framework of the AfricultuReS project; (b) the description of the crop early warning service delivered in the framework of the AfricultuReS project; and (c) the application of the desert locust disaster monitoring model to the case of Somali. Using a multi-source data approach, the factors that have an important influence on the desert locust occurrence and spread process were extracted. The connection between the three points mentioned above must be sought in the fact that the combination of an accurate mapping of agricultural areas accompanied by techniques for estimating any threats to them allows to accurately estimate the possible effect in terms of production loss and food security.

City based pollution monitoring is essential for overall health and sustainability of the concerned city. PM2.5is one of the most hazardous pollutants whose excessive presence in the urban air makes it unfit to breathe. A study is conducted to estimate PM2.5(particulate matter with diameter ≤$2.5\ \mu\mathrm{m}$) using PRISMA (hyperspectral imagery based satellite) hyperspectral bands for the Delhi region in India. By using ground station measurements and simulated PM2.5concentrations (using Sequential Gaussian Simulation) as a reference, estimates of PM2.5are created from the PRISMA imagery. Various regression models are developed for PM2.5estimation from hyperspectral data and deployed for spatial estimation. This study provides a comparative demonstration of ground level PM2.5prediction for urban areas using various machine learning models from hyperspectral imagery and indicates the importance of it.

The recent surge in interest towards hyperspectral imagery has the potential to unlock a new range of applications for the scientific community. However, compared to traditional multi-spectral images, the workload required to process such high-dimensional data is dramatically increased, to the point that new and more flexible strategies must be developed in order to properly monitor the quality of this type of products. In the particular case of sharpness assessment, traditional procedures based on the edge method tend to be extremely time-consuming due to their reliance on visual analysis performed by human operators, and would make proper processing of all bands a daunting task to perform on a large scale. In this paper we propose a flexible and fully automatic approach to edge method-based sharpness assessment that can be applied inde-pendently from the number of spectral bands. We then present the results of the application of the methodology on the visible and near-infrared and shortwave infrared spectral cubes of a selection of PRISMA L2D images, which confirm the relia-bility of the methodology and suggest further improvements.

The main objectives of the Sentinel-3 mission are to support ocean forecasting systems, environmental and climate monitoring. However, the coverage of the visible, near-infrared and short-wave infrared portion of the electromagnetic spectrum with a 300 meter resolution and a revisit period of less than 2 days make it very appealing also for vegetation monitoring. In this paper we explore the possibility of using the Sentinel-3 Synergy surface directional reflectances and the PROSAIL model to reliably estimate biophysical variables in general and live fuel moisture content in particular. The latter is a fundamental variable in fire behaviour models and in fire danger assessment, and consequently of high interest in fire management activities. We performed a Global Sensitivity Analysis to identify the most significant PROSAIL parameters in each Synergy channel, and tested the results by implementing a simple Look-Up Table based retrieval algorithm. The outcome shows the potential of biophysical parameter estimation based on this Sentinel-3 product.

Agricultural areas are naturally affected by significant variations within relatively short time intervals, in accordance with the growing season. These dynamics could, in principle, be exploited to classify different types of crops. Thus, this study aims to investigate methodologies and results of crop type classification making use of phenological information extracted from high spatial resolution satellite imagery. Vegetation indices (VI) retrieved from Sentinel-2 imagery are evaluated to track the year-round vegetation behavior. Starting from a multi-temporal image series of the same scene, the phenological profiles can be extracted and introduced into a supervised classification process to detect crop fields, discriminating among different species. Following this, we propose a cross-correlation based model that, using a priori information from ground training data, searches for the best matching phenology. When compared to machine learning models for crop classification, the one proposed in this study can provide useful information about phenology that can be stored and used for better monitoring spatio-temporal variations of crops species through the future years and guiding agricultural management accordingly. Our case studies are the regions of Bothaville and Harrismith, located in South Africa, and the region of Jendouba in Tunisia. The results for the Bothaville region show 89.19% of user accuracy on the main crop type classification (maize crops). For beans and sorghum, the confusion matrix attests 93% and 74% of accuracy respectively, even if their statistics are less significant due to the limited number of available ground data for secondary crops.

Italy has witnessed a significant increase in wildfires in recent decades. Forest fire fuel maps play a vital role in the prevention, management and risk assessment of wildfires, and this paper presents the procedure implemented to develop a dynamic wildfire fuel map using PRISMA hyperspectral data and Sentinel-2 multispectral data. Freely available multispectral datasets are widely used for land cover and land use mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. So, in this study, hyperspectral imagery (HSI) from PRISMA has been used for fuel types classification. The feed-forward neural network showed an overall accuracy of 79% by validation. To convert the classification map into a dynamic fuel map, the knowledge of the proportion of live/dead herbaceous loads available in that area is essential. The Relative Greenness approach, which places the Normalized Difference Vegetation Index (NDVI) in the time series of measurements, was implemented using Sentinel - 2 multispectral data. By fusing the fuel types classification, relative greenness map and iso-bioclimatic map, a dynamic fuel map for the west of Latium in Italy was developed with reference to Scott/Burgan fuel models.

Typical advantages and limitations of prior knowledge-based (deductive, top-down) expert systems are well known in literature: they typically score “high” in efficiency and interpretability, but they tend to score “low” in transferability/robustness to changes in input data. To benefit from these advantages while overcoming their typical shortcomings, an original expert system, based on a priori purely spectral-domain knowledge, is proposed for per-pixel (spatial context-insensitive) automatic near real-time detection of thermal anomalies in geostationary GOES-16 ABI multi-spectral (MS) imagery. Unable to learn-from-data, the proposed static decision-tree for MS signature recognition (classification) requires neither training data nor human-machine interaction to run. Its degrees of novelty pertain to the Marr levels of system understanding known as information/knowledge representation, system design (architecture) and implementation. Input with day and night ABI imagery acquired every 15 minutes, the proposed expert system detected 680 pixels with thermal anomalies in ABI images of the North and South Americas acquired from 30/01/2018 (15:00 UTC) to 31/01/2018 (01:30 UTC).

We investigated the use of phenological information extracted from satellite imagery in accurate crop classification. Vegetation indices (VI) extracted from Sentinel-2 imagery are capable to track the vegetation development through the year and from them the phenological profile can be retrieved and introduced into a multi-temporal automatic classification process to detect crop fields and to discriminate among different crop species. The matching and discrimination between phenology was evaluated by means of cross-correlation. Our case study is the Narok county located within the Great Rift Valley of Kenya.

The Italian Space Agency (ASI) is supporting the calibration/validation (CAL/VAL) activities of the PRISMA hyperspectral mission with the PRISMA Calibration Validation project (PRISCAV). PRISCAV provides, a network of reference test sites to support the PRISMA validation of the L1 and L2 processing chain performance. Among the PRISCAV test sites, representing the different Italian territory, the Pignola test site depicts an agricultural scenario pertaining to the Southern Apennines in the Basilicata Region (Italy). On this site, contemporary to PRISMA acquisitions, a set of ground measurements have been collected from October 2019 to December 2020 to characterize the atmosphere and the ground optical properties and validate the PRISMA radiometry and the L2 reflectance products. Measures are still ongoing on the base of the PRISMA acquisition plan. The comparison of the Modtran simulated radiance with the PRISMA L1 radiance data show the same magnitude and shape. RMSE for the full range of wavelengths vary from 0.000153 to 0.000995 [W/m−2sr−1nm−1]. Further analyses will include the new PRISMA acquisitions and the possible matchups with Sentinel-2, to assure the full exploitation of the PRISMA data for the agricultural monitoring in the Southern Apennines.

Hyperspectral images have its applications in various fields. Here, hyperspectral image from PRISMA which is a fundamental satellite of Italian Space Agency is being used for discriminating the wildfire fuel types on Sardinian Island of Italy. PRISMA is an on-demand mission and the available data in the archive are limited. There is no literature available on land use/vegetation classification using PRISMA data. In this paper, a new approach for generating samples to form a dataset for classifying the wildfire fuels and for classifying mixed pixels using iso-bioclimatic conditions are proposed. The classified map created using the dataset and using the iso-bioclimatic conditions is been validated. From the accuracy assessment, SVM classifier showed an overall accuracy of 86% and kappa coefficient of 0.79. Then, the classified map is converted into fuel map. This study suggests that the proposed approach can be used to generate samples for land use/vegetation classification and to assign vegetation types to mixed pixels depending upon the iso-bioclimatic conditions.

The metrics traditionally used for assessing the sharpness level of optical imagery acquired by spaceborne sensors (e.g., relative edge response, point spread function, full width at half maximum of the line spread function, modulation transfer function) are usually measured before launch using a set of standard simulated inputs. However, vibrations occurring during launch and satellite deployment, as well as sensor degradation through time, may alter the nominal characteristics significantly. Therefore, post-launch assessment analyses are necessary for monitoring data quality. To this end, on-orbit measurement techniques for sharpness assessment - e.g., the edge method (EM) - were developed. Selection of suitable targets to be used for such techniques is a crucial step for carrying out the assessment successfully. The objective of this paper is to describe an automatic method for identification of suitable edges to be used for on-orbit sharpness assessment via the EM by taking into account the widespread presence of natural targets - like agricultural fields - on Earth. The presented approach is expected to ease the continuous monitoring of the sharpness level of optical data acquired by spaceborne sensors.

Smallholder farmers produce about 70% of Africa's food supply. These farmers are vulnerable to a number of risks, mainly climate related, which have a tremendous impact on food security and thus poverty. Information about crop yields, vegetation conditions and weather, among others, are essential to policy makers to enhance food security. Earth observation data, analytics and modeling from various sources, at a variety of spatial and temporal scales could be used to support policy and decision making in the field of food security. This paper describes crops, water and drought services that are being developed in the AfriCultuReS project. Preliminary results are presented, which reflect the uneven distribution of precipitation, water bodies, and vegetation conditions throughout Africa.

In this work, we present the calibration and validation method we have applied in order to retrieve the split window (SW) coefficients for land surface temperature (LST) estimations from Thermal Airborne Spectrographic imager (TASI). For calibration and validation two different datasets has been used, both extracted from SeeBor V5.0 training dataset. The coefficients have been retrieved by a multiple regression analysis and MODTRAN simulations. For the radiative transfer experiment, we considered seven different viewing angles in a range between 0° and 60° with a step of 10°. Simulations have been performed considering all TASI channel combinations and the sensor spectral response functions. Preliminary results are presented for best band combinations suitable for SW algorithm application; these are channel 19 (10.034 gm) with 28 (11.024 gm), and channel 29 (11.134 gm) with 31 (11.354 gm). Finally, validation of the LST retrievals presents a RMSE lower than 0.6 K for both band combinations.

Agricultural activities conducted in the Great Rift Valley of Kenya, show a significant decline of productivity levels. In this study, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations. We investigated the use of phenological variables and metrics extracted from satellite in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year and from them the phenological profile can be retrieved and implemented into a multi-temporal automatic classification process to detect agricultural vegetated areas and to discriminate among different crop species. The phenological profiles extracted by satellite images were compared with crop calendar data, compiled by FAO for the area of interest.

Tree pests can cause rapid and widespread damage, reducing the economic value of plants, production, in the case of fruit trees, and their role in mitigating climate change. There are several diseases that affect trees, including, for example, pine tree nematode (PWN), trunk fungal diseases, or Xylella fastidiosa (Xf).Mapping of diseased plants based on visual or automatic analysis of remote sensing data could be a useful support for in situ investigation planning. However, there is a clear need for better modeling methods to elaborate potential critical scenarios in order to early detect diseases (e.g. Xf) in host plants.Maxent (Maximum Entropy) has proved powerful when modeling species with available scarce presence-only occurrence data. The purpose is to predict potential distributions or explore expanding distributions. In this work we applied the Maxent model comparing local modeling results with worldwide cases towards a more comprehensive analysis of potential pest risk zones.

In mitigating climate change and ecosystems preservation that are unique and indispensable to the life of the planet, efforts are being made to reduce current levels of deforestation and degradation of tropical forests. In the arch of deforestation of the Amazon forest, studies on the occurrence of fires and the dynamics of deforestation are frequent in the literature on several aspects such as soil management, climate and vegetation change and forest resilience. This work presents an analysis of the relationship between deforested areas and the occurrence of fire outbreaks observed from January 2007 to November 2018, in a study area of approximately 36,000 km2, corresponding to the area covered by a Landsat-8 scene. Further, it was performed a correlation of fire outbreaks occurred in and near the forest area from the boundaries of the deforested areas from 2015 to 2018. Annual fire outbreaks data were obtained through the Queimadas Program of the National Institute for Space Research (INPE) from 2007 to 2018, the deforestation polygons from the Project for Deforestation Monitoring in the Amazon Forest by Satellite (PRODES) and the INPE's Real-Time Detection System (DETER-B) from 2007 to 2018. The temporal analysis revealed an increasing of the incidence of fires with the increasing of deforestation in that period, evidencing the systematic use of fire as a means of suppressing biomass. In the last 4 years, the high occurrence of forest fires between 500 to 1000 m from the edge boundaries of the deforested areas, showed a considerable degradation of the forest and a probable decrease of forest resilience. Deforestation, Forest Degradation, Amazon Rainforest, Temporal Analysis, Remote Sensing.

Geostationary satellites like MSG allows to detect and monitor thermal anomalies (wild fires, volcanic eruption) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in contrasting the fire events and could provide information on the temporal behavior of the fire (through Fire Radiative Power, FRP) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation to the operational applicability of this tool is represented by the present low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. Whereas the limitations related to the sensitivity of the geostationary sensor to fire sizes has been, at least in part, overcome by introducing specific algorithms, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km2(at Mediterranean latitude) makes this information not very much interesting for the institutions involved in fire fighting. This paper is focused on the analysis of the feasibility of improving the localization of the thermal anomalies (hot spots) based on geostationary sensors by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 (RSS) located at long. 9.0° and MSG-8 (IODC) located at long. 40.5°. The results seem to confirm the possibility to improve the accuracy of the detection by exploiting the observation of the events from different position in the space.

Agricultural activities conducted in the Great Rift Valley of Kenya, show a significant decline of productivity levels. This phenomenon is mainly related to the limited water resources availability, the lack of supporting irrigation and the harvesting techniques ineffectiveness. The production risks reduction is closely related with a better use of water resources and a better understanding of the effects resulting from the multiple interactions between climate, agricultural vegetation, soil type and crops management techniques. In this study, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations.

The Leaf Area Index (LAI) is an important indicator of vegetation development which can be used as an input parameter in hydrological and biochemical models (e.g. crop models for yield prediction and forecast) and is, thus, relevant information to monitor food production and to feed an early warning system for famine crisis. Satellite LAI data is available on a regular basis (high temporal resolution) with maps at regional or global scales (low spatial resolution). This study aimed at enhancing the spatial resolution of the MODIS LAI product to bring it to the Landsat resolution. The proposed method was applied in four sites with different climate and vegetation conditions. Regression analysis between MODIS EVI (Enhanced Vegetation Index) and LAI data was applied across time and the estimated regression equations were input in a downscaling model using Landsat EVI images and land cover maps. Comparison between the downscaled LAI values and LAI field measurements showed high correlation, with correlation coefficient values ranging from moderate (0.5-0.7 in two cases) to high (0.7-0.96 in five cases). The results show that it is possible to use this methodology to reliably estimate LAI at a 30m spatial resolution across various climates and ecosystems, thus supporting a food security early warning system.

SBAM (Satellite Based Agricultural Monitoring) is a project funded by Italian Space Agency in the framework of Italian-Kenya cooperation. The project has four main objectives: a) to produce an updated map of the agricultural areas for Kenya based on Landsat 8 and Sentinel 2 imagery; b) to develop an automatic monitoring system able to classify agricultural areas and detect land use changes; c) to develop and deliver to the Kenyan partner of the project a system capable to download and process automatically Landsat8, Sentinel2, MODIS and MSG/SEVIRI images by providing standard products (vegetation indices, statistics, temporal analysis, etc.); d) to provide a tool for assessing changes in the agricultural area stability and crop yield. and study the feasibility of a tool capable to forecast crop yields. The paper is devoted to describe the activity carried out in the field of forecasting crop yield by using biomass estimate based on SAR images. The results obtained by using images acquired by X-band (Cosmo-Skymed), C-Band (Sentinel-1) and L-band (PALSAR) systems on a study area devoted to sugarcane will be described.

We investigated the use of phenological information extracted from satellite imagery and supported by agro-ecological zoning (AEZ) in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year and from them the phenological profile can be extrapolated and implemented into a multi-temporal automatic classification process to detect agricultural vegetated areas and to discriminate among different crop species. Our case study is the Nakuru district located within the Great Rift Valley of Kenya.

The three years FP7 project PREFER (Space-based information support for the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area) devoted to develop a satellite based service infrastructure capable to provide up-to-date information to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region, has been successfully completed at the end of 2015. However, the project consortium will make available its products for the 2016 summer season, too. The present paper aims at presenting the project achievements emphasizing the most innovative information products developed in the framework of the project. For such products the methodology, validation and demonstration results will be presented and discussed.

The territory knowledge plays a key role in the proper management and planning of many human activities. The relevance of land monitoring and mapping, that finally leads to quantify changes in land cover, is widely recognized as a key element in the study of global changes. Vegetation indices derived from satellite imagery are well correlated with those parameters that defines the crop yield's status; as a consequence remote sensed earth observation data are really notable for monitoring cultivated areas and crop yields and to provide information concerning food security and famine early warning.

During last years, several studies related to remote sensing technologies analyzed the processes to extract and classify slicks from SAR imagery. These images are used, among other purposes, for monitoring coastal and marine waters pollution where oil floating on the surface becomes visible because it damps the short gravity-capillary waves that are responsible for the radar backscattering [14]. Nowadays an important number of SAR images are available and this number will increase in coming years thanks the launch of Cosmo-Skymed 2ndgeneration, recent availability of Sentinel-1, ALOS Palsar-2 products and future SAOCOM launch. That will provide information suitable to support decision makers in managing emergencies or potential disasters. The present study show the results obtained from 190 regions of interest extracted from a set of X, C and L Band images, where a database related to spatial, textural, spectral and contextual characteristics of the features detected was ingested into a neural network algorithm. The classification process reached percentages of up to 95% of cases of oil spills and look-alikes correctly classified depending on the wavelength, the polarization and incidence angle.

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

PREFER 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 pre-operational portfolio of products, based on Earth Observation data for helping fires management at Mediterranean scale. Samples of the PREFER (Space-based Information Support for Prevention and REcovery of Forest Fires Emergency in the MediteRranean Area) 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 low (MODIS), medium (Landsat, Spot) and high (Kompsat, RapidEye, Pleiades, Cosmo-SkyMed, TanDEM-X, etc.) 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 introduce the methodology developed for computing the maps of the level of damage caused in vegetated areas by fires and to present the results of the validation process just started.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

The monitoring of cataloged landfills and the detection of uncontrolled dump sites is becoming a crucial environmental issue in all European countries. Remote Sensing application has been demonstrated to be helpful in mitigating the problem of landfills monitoring and in the continuous surveillance of known waste disposal sites. In previous works [1][2][3], the suitability of EO data exploitation, coming from SAR and Optical sensors, has been tested to be an important key factor in providing valid information to the local stakeholders for the identification, classification and monitoring of contaminated sites using non-invasive methods. The present paper aims to describe the consolidated and validated results obtained in the framework of the SIMDEO project, co-funded by the Italian Space Agency (ASI) and Euro Soft srl (contract n. I/055/11/0), detailing the algorithms adopted and describing the output products validated using ground-truth data.

The main objectives of the PRISMA (Hyperspectral Precursor of the Application Mission) mission are: the implementation of an Earth Observation pre-operative payload, the in-orbit demonstration and qualification of an Italian state-of-the-art hyperspectral/panchromatic technology and the validation of end-to-end data processing system able to support the development of new applications based on high spectral resolution images. The aim of the paper is to provide an overview of the PRISMA mission by describing the current status of the program and giving a brief outline of the work done till now in the framework of the SAP4PRISMA project scientific studies in supporting the exploitation of the future PRISMA hyperspectral images for environmental applications.

In this paper we deal with the integrated use of time-series of SAR and MODIS images to derive the temporal behavior, the abundance and the distribution of the floating macrophytes in the Winam Gulf (Kenyan portion of the Lake Victoria). The proliferation of invasive plants and aquatic weeds is of growing concern. Starting from 1989, Lake Victoria has been interested by the highest infestation of water hyacinth with significant socio-economic impact on riparian populations. The information provided by satellite can play an important role in supporting a decision system for the management of the water resources allowing also an easy and inexpensive way of monitoring the environment response to any action that might be undertaken to contrast its degradation. This paper aims at assessing the capability of medium/high resolution (Wideregion and Stripmap) COSMO-SkyMed ScanSAR time series imagery to support/supplement optical data, frequently affected by clouds, in the knowledge of temporal macrophytes growing cycles and sustain the monitor and management of the Lake Victoria waters.

The SIGRI (Sistema Integrato per la Gestione del Rischio Incendi) pilot project, funded by ASI (the Italian Space Agency), aims at developing an integrated system for the management of the wild fire events. The system provides satellite based products capable of assisting all the phases of the fire contrasting activities: prevision, detection, and damage assessment/recovering. The SIGRI project aims at implementing consolidated methodologies and/or developing innovative tools and methods for the analysis of remote sensing data and the extraction of information useful to the application. This paper concerns the final phase of the SIGRI project: test and validation of algorithms developed. The validation of the generated products is a very important phase through which the products potentiality is assessed and the algorithms can be calibrated. In this paper we will discuss the results of a preliminary validation process.

The SAP4PRISMA is a four year research project which aims at developing algorithms and products for the future PRISMA mission. The project started on May 2010 and is now entering his full activities as the ”PRISMA like” data set has been defined and the test areas were selected. The paper describes the main project objectives and the activities realized in the first 9 months of the project.

The SIGRI (Sistema Integrato per la Gestione del Rischio Incendi) pilot project, funded by ASI (the Italian Space Agency), aims at developing an Integrated System for the Management of the Wild Fire Events. The system should provide satellite based products capable to help fire contrasting activities during all the phases: prevision, detection, and damage assessment/recovering. In particular, the paper concerns the development of a Fire Risk Index to be produced daily with the objective of showing the total level of risk for the area of interest and the zones of major concern within such area. In the European Community the member countries interested by forest fires are at least six: Portugal, Spain, France, Germany, Italy, and Greece. The higher number of wild fires occurs in the western part of Spain and Portugal, in southern Italy and in the Mediterranean islands The idea to develop maps able to show the fire risk is based on the observation that there is a tight relationship between the fire and the characteristics of the fuel (vegetation type, density, humidity content), of the topography (slope, altitude, solar aspect angle) and the meteorological conditions (rainfall, wind direction and speed, air humidity, surface and air temperature). These parameters directly impact the proneness of a given area to the fire ignition and propagation. Since these quantities can be measured, notwithstanding the cause of the fire ignition, mainly due, in Italy, to human actions (more than 90% of the ignitions is intentional or accidental), could be unpredictable the behaviour of the fire can be considered strictly dependent from those and then it can be foreseen when such parameters are known.

Two multitemporal techniques based on the consecutive analysis of multispectral data ranging from high-temporal to high-spatial resolution, were implemented into a remote-sensing system dedicated to the quantitative monitoring of wildfires in the Mediterranean Region, operating at low-to-nil supervision levels. The system covers the whole fire remote sensing chain, from accurate hot-spot detection and location to high resolution mapping of burn scars. It operates on strong multi-payload basis, as the first part of the processing chain acts on data acquired by the multispectral geostationary payload SEVIRI, whereas the second was designed to operate switching between data acquired by LEO high resolution payloads TM, ETM+, ASTER, HRVIR and LISS 3, as a function of availability and content.

Aquatic weeds in lakes can cause different problems both to lake ecology and food webs and interfere with human activities. This drove our interest to exploit recursive satellite imagery to retrieve optical parameters suitable to develop an early warning strategy by mapping the aquatic weeds. This paper aims at assessing the capability of satellite-based remotely sensed imagery to provide information suitable for monitoring and managing the Lake Victoria resources. The spectral data collected during a field campaign, carried out for that purpose, were used to map the floating aquatic vegetation. By analyzing the ¿in situ¿ measurements and time series of satellite data we retrieved the distribution of aquatic weeds from 2004 to 2007 and the seasonal aquatic vegetation growth. These maps, when provided with an appropriate time frequency, can be useful to identify the preconditions for the occurrence of hazard events such as aquatic weeds and to develop an up-to-date decision support system.

The estimate of the burned biomass starts from the computation of the FRP (Fire Radiative Power) that is the radiative power released by the fire. By integrating this quantity in the time it is possible to estimate the FRE (Fire Radiative Energy) and the burned biomass, if coefficients providing the burning efficiency of the vegetation interested by the fire are available. The FRP has been estimated by following three different approaches: the method proposed for the MODIS sensor, based on the eighth power of the brightness temperature of the fired pixel times a suitable coefficient; or by using the hypothesis that the fire size and its burning temperature can be computed by means of the Dozier approach and estimating the FRP by using the Stefan-Bolzmann relationship; or avoiding the computation of the brightness temperature of the fired pixel, by using the approach proposed by Wooster, in which the spatial resolution of the satellite image and the fired pixel emitted radiance are considered. Due to the high temporal frequency of the SEVIRI observations, the integration with the time of the FRP (computed every 15 min) can be carried out allowing to estimate the total energy released by the fire (FRE) and possibly the amount of burned biomass (BB). The paper aims at analyzing the suitability of this approach by focusing on the Sardinia region (Italy). The availability of the sizes of burned areas, provided by the Corpo Forestale e di Vigilanza Ambientale of the Sardinia region, allows to check the significance of the retrieved BB value.

The red mud dust risk involves the accumulative contamination of land and dwellings in the community with highly alkaline fine particulate containing heavy metals and other pollutants. This paper demonstrates that hyperspectral airborne remote sensing data can provide an effective, rapid and repeatable tool for mapping and monitoring the spread of red dust providing the location of the polluted areas to be checked. We perform field and laboratory analyses of red mud and soil samples collected in the study area and identify the optical characteristics of the samples to characterize the red mud spectral features. Next, we use hyperspectral airborne data covering an aluminium processing plant in Montenegro (EU). The joint use of MIVIS reflectance and emissivities data allowed us to individuate and map those sites on which the red dust is spread by the dominant winds, where a check for reclamation or a neutralization intervention is required.

One of the most important factors responsible of the fire-detection algorithms fail is represented by the inaccurate cloud detection methods. In fact, the cloud-contaminated pixels are often associated with false fire pixel because of the brightness temperature increase in the mid-infrared channel. On the other hand an incorrect cloud masking could hide a real fire pixel, especially at the borders of clouds. Together with the SEVIRI images EUMETSAT provides its own cloud mask (CLM product). This mask is computed by making full use of the MSG-SEVIRI spectral channels. Among the 12 channels, only channels 8 (IR 9.7) and 12 (HRV) are not included in the cloud detection and analysis. Due to the particular application for which CRPSM is using SEVIRI images, detection of fire at its early stage (sizes lower than 0.1 ha), a high sensitivity to changes in the radiance measured by the sensor in channel 4 (3.9 iquestm) is required. Since the presence of a cloud covering only a fraction of the pixel (~4 times 4 km at mid latitude) can produce an increase in the estimated brightness temperature, in such channel, capable to provoke a false alarm we decided to use also channel 12 in the cloud detection algorithm. Thus, in order to improve the cloud masks provided by EUMETSAT a new methodology has been introduced. The approach is firstly based on the application of the HRV channel during daytime.

The spectral and radiometric performance of payload SEVIRI onboard the geostationary platform MSG-2, make its data particularly well suited not only to the detection of the onset of volcanic activity, but also to the measurement of thermal radiant fluxes and eruption rates. Thorough testing was carried out on two volcanoes - Stromboli (Aeolian Islands, Southern Italy) and Piton de la Fournaise (Réunion Island, northwestern Indian Ocean) - that mostly give rise to short-lived lava flows. Aimed to comply with the outstandingly high acquisition rate, we developed an ad-hoc code to automatically detect volcanic hot-spots, measure radiant fluxes, and derive lava volume effusion rates within the 15-minute interval between two SEVIRI data streams.

This paper is devoted to describe the activity carried out by CRPSM(Centro di Ricerca Progetto San Marco) in the framework of the SIGRI(Italian acronym for Integrated System for Fire Risks Management) project. This project aims to develop a system, based on satellite data, able to support operationally the activities of users like Italian Civil Protection Agencies or Fire Dept. involved in fighting wild fires. In particular, the system should be able to support all the phases in which a fire fighting activity can be distinguished, namely: Territory management and resources dislocation (fire risk indices), fires detection and monitoring, damage assessment (burned areas and emissions in atmosphere). This paper presents the results obtained in the process of assessing the quality of a fire hazard forecast based on a fire potential index especially designed for the Mediterranean areas. This quality assessment is carried out comparing the daily computed indices with the fire distribution obtained by using a fire detection algorithm based on SEVIRI/MSG images.In fact, using a fire detection algorithm (SFIDE, System for Fires Detection), recently proposed by the authors, a despite of its low spatial resolution,the SEVIRI system is able to reveal, at latitudes corresponding to Italy,fires covering an area of the order of 0.1 ha. The fire potential index (FPI) is one of the most suitable to be computed by using satellite data even if ancillary information are still needed. The computation of this index requires the estimate of the relative greenness, the evaluation of the leaves humidity, the preparation of vegetation fuel maps. Among the parameters needed to compile this index the fuel type map is particularly crucial. In fact, accurate maps of this kind are not available for the Italian territory. Then, first of all, using Corine Land Cover and other available vegetation maps, medium resolution satellite images and "in situ" observations CRPSM carried out the development of these maps for a couple of Italian regions where the summer wild fires problem has higher incidence.

In the framework of the European project GMOSS (Global Monitoring for Security and Stability) the CRPSM (Centre di Ricerca Progetto San Marco) is developing automatic procedures for detecting and counting dwelling units in refugee camps. The possibility of monitoring refugee camps, using very high spatial resolution satellite (VHSR) images, has been already demonstrated, in the past, by several authors. This paper aims at reporting our results on the way of developing new algorithms to improve the performances of previous techniques in terms of detection accuracy and to extend their applicability to a broader set of background conditions. The algorithms developed by CRPSM to estimate, in an as automatic as possible way, the refugee camps population (tents) are based on the mathematical morphology (MM). The method has been applied to Ikonos and Quickbird VHSR images of several refugee camps (Goz Amer, Mille, Lukole, etc) located in Africa, to solve the tents counting problem. The error associated with these techniques, computed comparing the number of tents detected by the different algorithms with the one obtained by a visual counting, results satisfactorily low. The developed techniques have been compared with other already available methods (eCognition, etc.) and the results are also described.

Fires represent one of the main factors of degradation and destruction of the Mediterranean forest heritage. According to fire-fighting agencies, a satellite-based fire-detection system can be considered operationally useful for Mediterranean countries when fires with a minimum extent of 1500 m2can be detected with a temporal resolution of 30 min. In fact, such a system should be able to detect fires at their first stage when it is possible to extinguish them more easily. The Centro di Ricerca Progetto San Marco has been analyzing for several years the possibility of using images acquired by the Spinning Enhanced Visible and Infrared Imager sensor onboard the geostationary satellite Meteosat Second Generation for this purpose. A new processing approach exploiting the increase in both spatial and temporal resolution (15 min) with respect to previous meteosat systems is described in this paper. The idea is based on the use of a change-detection technique to maximize the detection capabilities of the system in spite of its limited spatial resolution. This technique consists of comparing two or more images acquired at 15-min intervals, for which any temperature change can be attributed to fast dynamic phenomena, such as fires, when natural changes are modeled and removed. An assessment of the performances of this algorithm is carried out comparing its results with the report made available by Italian fire-fighting agencies and with fire products based on higher resolution sensors such as the Moderate Resolution Imaging Spectroradiometer

Fires represent one of the main factors of degrada- tion and destruction of the Mediterranean forest heritage. Ac- cording to fire-fighting agencies, a satellite-based fire-detection system can be considered operationally useful for Mediterranean countries when fires with a minimum extent of 1500 m 2 can be detected with a temporal resolution of 30 min. In fact, such a system should be able to detect fires at their first stage when it is possible to extinguish them more easily. The Centro di Ricerca Progetto San Marco has been analyzing for several years the possi- bility of using images acquired by the Spinning Enhanced Visible and Infrared Imager sensor onboard the geostationary satellite Meteosat Second Generation for this purpose. A new processing approach exploiting the increase in both spatial and temporal resolution (15 min) with respect to previous meteosat systems is described in this paper. The idea is based on the use of a change- detection technique to maximize the detection capabilities of the system in spite of its limited spatial resolution. This technique consists of comparing two or more images acquired at 15-min intervals, for which any temperature change can be attributed to fast dynamic phenomena, such as fires, when natural changes are modeled and removed. An assessment of the performances of this algorithm is carried out comparing its results with the reportmadeavailablebyItalianfire-fightingagenciesandwithfire products based on higher resolution sensors such as the Moderate Resolution Imaging Spectroradiometer.

Obtaining quantitative information about vegetation with remote sensing continues to prove difficult, with most healthy plants showing absorption bands that are similar. Quantifying such subtle differences in plants in a predictable way still poses a challenge, with abundances in species, the limited nature of spectrometric measurements on plants, and in our case the relatively small quantities of macrophytes under investigation, coupled with the location of the study site - in an aquatic environment. We used multispectral Landsat Enhanced Thematic Mapper (ETM+) imagery in investigating the possibility for mapping and quantification of macrophytes in a water hyacinth infested area. An ETM+ image is being examined using hyperspectral-processing techniques. With accurate information on macrophytic weeds, natural resource managers are empowered in making informed decisions about weed management.

Operational assessment and monitoring of plant cover in large areas commonly relies on vegetation indices (VIs) determined using the functions of the reflectance in the red and near infrared spectral bands. There is a large degree of variability in the spectral characteristics of different types of vegetation and soil/rock for arid environments. This requires the use of a vegetation index in conjunction with the knowledge of the vegetation and land characteristics in the area being assessed or monitored. A previous paper has been devoted to assess desertification trend in the north part of Kenya. The present work concerns the calibration/validation of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) for a tropical arid environment using ground-based spectral data and Landsat Enhanced Thematic Mapper (ETM) images. Data from Landsat (ETM) covering a dry seasons over one of the dry ecosystems in Kenya were processed and the relative NDVI and EVI computed. Spectral data measurements were performed by means of a spectroradiometer (ASD FieldSpec PRO) and other land cover characteristics were observed in the field for the same season and area