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.
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.
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.
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.