This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet?s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.

Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.

LAI is defined as one sided green leaf area per unit ground area in broadleaf canopies and is an important input parameter to monitor crop growth conditions and to improve the performance of crop yield models. Because direct measurements of LAI are usually time-consuming and require continuous updates, remote sensing is an alternative to estimate this attribute over large areas as watershed scale. The primary objective of this work was to derive a reliable LAI estimation model from VHR satellite data to be compared with moderate resolution satellite products in order to improve LAI estimation performance for next validation activities. Due to lack of contemporaneous satellite and on-site sensor data acquisitions and intrinsic complexity of physical models, in our study case the semi-empirical approach with the CLAIR model was applied. It is based on an inverse exponential relationship between LAI and the WDVI (Weighted Difference Vegetation Index) related to different land covers. LAI values were generated from multispectral GeoEye-1 sensor data covering a time space of 5 years (2009-2013) to study crop phenological stages on the study area of the Carapelle watershed located in the North of Puglia region (Southern Italy). Data were preliminarily pre-processed (geometric and radiometric correction), classified (ISODATA method) and texture based analyzed in order to extract the vegetated areas (mainly cereal crops). Finally, the resulted maps were compared with moderate resolution satellite data by reaching a possible correspondence.

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data. Depending on site-specific pruning practices, the morphologic characteristics of tree crowns may generate one or more brightness peaks (tree top) on the imagery. To optimize tree counting and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands (asymmetrical smoothing filter, local minimum filter, mask layer, and spatial aggregation operator). System performance was evaluated through objective criteria, showing consistent results in fast capturing tree position for precision agriculture tasks.

Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring.

The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects.

In recent years, the wide-spreading of vineyard cultivation in the Apulia Region (Italy) has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape which has been significantly altered by the heavy diffusion of artificial plastic coverings. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. With the increase of spatial resolution, pixel based approaches no longer capture the characteristics of classification targets. Consequently, classification accuracy is poor. Object-based image classification techniques overcome this issue by first segmenting the image into meaningful multipixel objects of various sizes and then assigning segments to classes using fuzzy methods and hierarchical decision keys. In this study an object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on a test area located between the Apulian municipalities of Ginosa and Palagiano in order to support the update of the existing land use database aimed at plastic covered vineyard monitoring.

This study was primarily aimed at verifying the potentiality of medium spatial resolution Thermal Infrared (TIR) data in monitoring environmental aspects strictly correlated to SST (Sea Surface Temperature), such as industrial cooling water discharges and the increase in seawater temperature over time, at two coastal sites in the Apulia region (Italy).TIR data acquired by LANDSAT ETM+ and ASTER satellite instruments were used to observe SST and provide insight into its spatial variability, in this case totally lacking information from conventional in situ measurements. The spatial information obtained, clearly mapped for its visual interpretation, can significantly contribute to improving general surface near-shore water quality monitoring.

This paper presents a semi-automatic approach for archaeological traces detection from aerial images. The method developed was based on the multiphase active contour model (ACM). The image was segmented into three competing regions to improve the visibility of buried remains showing in the image as crop marks (i.e. centuriations, agricultural allocations, ancient roads, etc.). An initial determination of relevant traces can be quickly carried out by the operator by sketching straight lines close to the traces. Subsequently, tuning parameters (i.e. eccentricity, orientation, minimum area and distance from input line) are used to remove non-target objects and parameterize the detected traces. The algorithm and graphical user interface for this method were developed in a MATLAB environment and tested on high resolution orthorectified aerial images. A qualitative analysis of the method was lastly performed by comparing the traces extracted with ancient traces verified by archaeologists.

In this study, a semi-automatic approach to support archaeological line tracing is proposed. The suggested procedure is based on colour and texture information derived from orthorectified RGB digital aerial data and consists of four steps: (1) line sketching; (2) steerable filtering; (3) objects selection; and (4) straight line fitting and vectorisation. Good results were observed by evaluating the algorithm according to trace visibility and integrity, global difficulty and level of feature extraction. Further reliability tests were performed to study poor data initialisation and different annual seasonal land use at the same site