The extensive presence of macroplastic pollutants along coastlines poses a significant environmental challenge, threatening both human health and coastal ecosystems. Accurate identification of these pollutants is essential for planning effective cleanup activities and improving overall quality of life. Traditionally, in-situ surveys have been the go-to method for locating macroplastics, but they are costly and time-consuming. To overcome these limitations, this study explores the use of Remotely Piloted Aircraft Systems (RPAS) equipped with high-resolution cameras as a cost-effective alternative for mapping macroplastic litter on beaches and distinguishing between different types of contaminants. The Brindisi shoreline was selected as a pilot site, where it was surveyed using a DJI MAVIC MINI drone with an RGB camera. The collected images were processed with Metashape software and ground control points from a GARMIN Forerunner 245 were used for georeferencing. The resulting RGB orthophoto was analyzed using Transformed Divergence and Bhattacharyya Distance criteria to assess the inter-class spectral separability and classification accuracy. The analysis identified 1,154 waste elements, demonstrating that RPAS imagery is effective for detecting macroplastic items. However, the study found that while the RPAS RGB orthophoto was suitable for manual classification, its performance in automatic classification was limited. The separability algorithms used affected the accuracy of the final classification maps, indicating that while RPAS technology is promising, improvements in spectral analysis and classification algorithms are needed for better-automated results.
The Energy Union Framework Strategy is pushing the entire world to move from fossil fuels to renewable energy to tackle climate changes and mitigate their effects. Among the clean energy alternatives, the sun is recognized as the most abundant and inexhaustible source and the energy production can be carried out through photovoltaic panels. Nevertheless, such a solar park requires the use of large land areas, stolen, in such a way, from food production, which demand has strongly increased in the last few years due to the growing world population. Thus, agrophotovoltaic systems, also known as agrivoltaic structures, are under way to meet the above-mentioned needs synergistically. This has led to the necessity of monitoring solar panels amount and allocation. Their detection is challenging since, albeit their spectral signature is totally different from that one emitted from other land covers, their occurrence received little attention in the field of remote sensing. Thus, in this study, a proper rule-based model for distinguishing photovoltaic panels developed on eCognition environment was proposed. Such a model is based on the combination of Object-Based Image Analysis and machine learning algorithm. Indeed, after optimizing segmentation parameters and analyzing morphological features of the panels, the Random Forest classification algorithm was implemented. Lastly, classification accuracy was evaluated. The experimentation was conducted on the study area of Viterbo (Lazio Region, Italy) by adopting open medium-resolution satellite data (Sentinel 2). This research showed promising results in classifying targets for almost all months of the time series, except for the months of October and November where there is a lowering of the accuracy value due to the variability of spectral signatures.
Albedo has long been recognized as a relevant bio-geophysical variable to model Earth surface and it was involved in all the climate simulation models. Therefore, the correct modelling of albedo is essential to reduce the error propagation in the prediction algorithms. To meet such a purpose, different methods have been developed over the past years. Among them, the simplified approach proposed by Liang in 2000 and the corrected algorithm introduced by Silva et al. (2016) are commonly used. To the best of our knowledge, the outcomes produced by applying such techniques have not been investigated yet. The present paper is intended to explore the potentialities of Google Earth Engine (GEE) platform in estimating land surface albedo from three medium-resolution geospatial data gathered by different Landsat sensors in diverse acquisition periods. Java-script code was developed to numerically implement the above-mentioned algorithms in GEE environment. Their performances were compared and the error committed adopting the simplified method was quantified. As a result, the corrected algorithm reported more accurate values. Nevertheless, its complexity implies a high implementation difficulty and, consequently, a higher processing time is required to handle the data. Conversely, the simplified approach allowed to estimate land surface albedo in a short time. Quantifying the error committed using the simplified approach allows us to correct its results, improving their accuracy. Although obtained results are preliminary, this research enhanced the possibility to model the albedo by adopting the simplified algorithm after correcting it. This implies to reduce error propagation and, simultaneously, to speed up the data handling.
In the field of Cultural Heritage preservation and enhancement, detecting objects quickly and inexpensively, with the possibility of repeating measurements several times for monitoring any deterioration, has become an increasingly significant requirement. The existence of a conspicuous historical heritage across the Italian territory often forces local authorities to orient their survey strategies towards the research of the most economic, but still efficient, solutions. Due to these reasons, also in consideration of possible emergency situations, it is necessary to find the optimal solution to allow a timely and comprehensive detection of exhaustive 3D digital object reconstructions. An important task is therefore to test the potential accuracies of recent measurement technologies and procedures in order to produce high quality results. This study analyzes the generation of three-dimensional reconstructions of Torre Zozzoli, an historic fortified tower located 25 km from Taranto (Apulia region, Italy), through two close-range detection techniques, by comparing Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicles (UAVs) photogrammetric imagery. To arrange and improve the methodologies of ground control point measurements, two survey techniques were implemented by means of a Total Station (TS) and a GNSS receiver in nRTK mode. Lastly, using the cloud-to-cloud (C2C) comparison tools and implementing three distributions of GCPs, UAVs and TLS points clouds were compared. Considering their accessibility in terms of costs and use, photogrammetric products from UAVs, represent a valid alternative to TLS-based 3D data in multi-temporal analysis.
Timely and accurate maps of land cover changes are crucial for understanding the evolution of Earth's features and, consequently, the relationships between individual and collective needs. Therefore, this information is extremely important to develop future planning strategies and tackle environmental issues. This paper aims to exploit the use of Google Earth Engine (GEE) platform to examine land cover changes over a period of about fiftheen years in the pilot site of Siponto, an historical municipality in Puglia, Southern Italy. Six atmospherically corrected Landsat data, two for each selected mission (5, 7 and 8), were collected: the former was acquired in fall and the latter in spring. Land cover information was automatically extracted from each image through the implementation of an innovative Landsat Images Classifications algorithm (LICA) based on spectral indices analysis. Six classes (water, built-up, mining areas, bare soil, dense and sparse vegetation) were detected from each image, with an average overall accuracy higher than 85%. Land cover changes were assessed comparing classification maps of the same season, showing bare soil areas as the most altered ones, having been converted into arable lands in consideration of the adavantageous geomorphological features of the investigated site. This is also confirmed by the historical events experienced by the area.
Remote sensing provides reliable information for the quantification of evapotranspiration (ET) over large areas, essential for water management and irrigation scheduling. The ET represents the Crop Water Requirements (CWR) that must be provided by rainfall and/or irrigation to ensure the crop yield. During last decades different ET estimation methods were developed according to problem-specific requirements, characteristics of data input (e.g. data accuracy, availability and resolution) and temporal and spatial scale of interest. The selection of the best methodology has a great influence on the results of ET estimation. Generally, the comparison of ET estimated trough different methods is affected by many parameters: data input (different sources, typology, temporal and spatial resolutions), different scales of analysis (from field to global scale), contests, crop type and climate condition. For this reason, defining whether algorithm can capture spatial and temporal pattern of ET at the required accuracy is a significant challenge. In this study two different methods, both based on the logic of the Penman-Monteith equation, were tested for ET trends estimation at irrigation district scale: the improved algorithm of the MODIS ET product (MYD16A2 V006) and the “Analytical Approach”. While the MODIS product follows the energy balance method, the Analytical Approach exploits the single crop coefficient (Kc) approach proposed by the FAO in Irrigation and Drainage Paper No. 56. It combines agrometeorological data measured in situ and surface reflectance satellite derived data: the albedo (α) of the crop-soil surface and the Leaf Area Index (LAI). In order to compare the two ET trends, the satellite data input used in the present work were chosen from the MODIS products: MODIS LAI (MCD15A2H V006) and MODIS Albedo (MCD43A3 V006). The comparison was assessed in the study area of “Sinistra Ofanto” Irrigation district located in the Apulia Region (Italy) and characterized by an extremely heterogeneous and fragmented landscape.
KEYWORDS: Radiometric corrections, Landsat, Sensors, Earth observing sensors, Calibration, Data acquisition, Data processing, Data modeling, Reflectivity, Detection and tracking algorithms
The quality of information derived from processed remotely sensed data may depend upon many factors, mostly related to the extent data acquisition is influenced by atmospheric conditions, topographic effects, sun angle and so on. The goal of radiometric corrections is to reduce such effects in order enhance the performance of change detection analysis. There are two approaches to radiometric correction: absolute and relative calibrations. Due to the large amount of free data products available, absolute radiometric calibration techniques may be time consuming and financially expensive because of the necessary inputs for absolute calibration models (often these data are not available and can be difficult to obtain). The relative approach to radiometric correction, known as relative radiometric normalization, is preferred with some research topics because no in situ ancillary data, at the time of satellite overpasses, are required. In this study we evaluated three well known relative radiometric correction techniques using two Landsat 8 - OLI scenes over a subset area of the Apulia Region (southern Italy): the IR-MAD (Iteratively Reweighted Multivariate Alteration Detection), the HM (Histogram Matching) and the DOS (Dark Object Subtraction). IR-MAD results were statistically assessed within a territory with an extremely heterogeneous landscape and all computations performed in a Matlab environment. The panchromatic and thermal bands were excluded from the comparisons.
This work analyzes the potentiality of WorldView-2 satellite data for retrieving the Leaf Area Index (LAI) area located in Apulia, the most Eastern region of Italy, overlooking the Adriatic and Ionian seas. Lacking contemporary in-situ measurements, the semi-empiric method of Clevers (1989) (CLAIR model) was chosen as a feasible image-based LAI retrieval method, which is based on an inverse exponential relationship between the LAI and the WDVI (Weighted Difference Vegetation Index) with relation to different land covers. Results were examined in homogeneous land cover classes and compared with values obtained in recent literature.
Plastic covering is a common practice in agricultural fields. From an agronomic point of view, plastic coverings offer
many advantages against unfavourable growing conditions. This explains their widespread utilization with consequent
positive impact on local economy. On the other hand, plasticulture raises both environmental and landscape issues. In the
Apulia Region (Italy) the wide implementation of such practice generally relates to vineyard cultivation. Continuous
vineyard protection has resulted in negative effects on the hydrogeological balance of soils, causing a deep modification
of the traditional rural landscape and therefore affecting its quality. To guarantee both the protection of local economy as
well as the preservation of local environment and landscape features, a detailed site mapping of the areas involved is
necessary. Indeed, the quantification of this phenomenon is essential in the periodic updating of the existing land use
database and in the development of local policies. In this study we evaluate the potential of the novel Thermal Infrared
Sensor bands (TIRS) provided by the LANDSAT 8 mission in plasticulture discrimination. Using the evident anomaly
retrieved in the study area on the Quality Assessment (QA) band, a fast procedure involving TIRS data was developed,
proposing a new index (Plastic Surface Index- PSI) able to emphasize plasticulture. For the aim of this study, two
different acquisition dates on a test area in the Apulia region (Italy) were analyzed, one in the growing season with high
plastic covering density and one in the post-harvest period with low plastic cover density.
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.
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.
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 work proposes a features extraction strategy for each land cover class using a hybrid classification method on multidate
ASTER data. To enable an effective comparison among multi-date images, Multivariate Alteration Detection
(MAD) transformation was applied for data homogenization to reduce noises due to local atmospheric conditions and
sensor characteristics. Consequently, different features identification procedures, both spectral and object-based, were
implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a postclassification
comparison was performed on multi-date ASTER-derived land cover (LC) maps to evaluate the effects of
change in the study area. All the above methods, when used in multi-date analysis, do not consider the issue of data
homogenization in change detection to reduce noises due to local atmospheric conditions and sensor characteristics.
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