Enrico Borgogno Mondino

Evidences of drought stress as a predisposing factor to Scots pine decline in Valle d’Aosta (Italy)

Scots pine (Pinus sylvestris L.) forests of many inner Alpine valleys have recently displayed a quick loss of vitality. A decline disease has been suggested as the cause, with drought as the main predisposing factor and the additional contribution of biotic agents inciting tree dieback. This study is focused on Valle d’Aosta, a dry, inner-Alpine region in NW Italy. We inferred vitality changes between years 2000 and 2007 by computing reductions in enhanced vegetation index (EVI). Image differencing was carried out on pre-processed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery taken in late springtime and validated against ancillary ground truth. We: (1) tested whether EVI reductions in Scots pine forests were significantly higher than those of a control species and of a wetter region for the same species, (2) analyzed decline incidence as a function of site and topographic variables, and (3) assessed the relative influence of site and stand structure on decline probability by means of path analysis. Mean EVI in the study area increased due to an early onset of the 2007 growing season. Nevertheless, the incidence of decline was 6.3% and significantly greater for Scots pine than the control species and site. Low-elevation, northerly exposed sites exhibited the highest incidence of decline. Path analysis suggested that the most important determinants of decline probability were slope, solar radiation, and stand sparseness.

High resolution satellite images position accuracy tests

This paper concerns high resolution satellite images position accuracy tests carried out through orthoprojection procedures both using commercial softwares and home-made ones. Planimetric positioning errors and influence of GCPs number on the results are shown. This kind of test drives us to consider the possibility of using such images for cartographic updating purposes. Moreover they demonstrate how the reference DEM could influence the final result. In detail the improving action of the utilization of an urban dense DEM (altimetric information of terrain plus buildings) is shown. Finally future planned developments in the home-made orthoprojection algorithms are presented.

Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates

Abstract Remote sensing phenological works often use vegetation index (VI) time-series (TS). Since ground-observed phenological metrics occurrences vary by a few days from year to year, TS temporal accuracy became mandatory, but it is less strict in composite data. A technique to recover the temporal accuracy of 250 m 16-day composite VI from the MODIS MOD13Q1 product is proposed, relying on acquisition dates contained in the Composite day of the year layer. We demonstrated that the correction process significantly affected the VI TS during most of the year, especially in spring and autumn when the starting of season (SOS) and the end of the season (EOS) are expected. As a consequence of the TS correction process, SOS estimation showed to be affected too.

Satellite images geometric correction based on non-parametric algorithms and self-extracted GCPs

The geometric correction of high resolution satellite images can be carried out through generic non-parametric models that relates image to terrain coordinates. Traditional approaches to image geocoding rely on the measurement of a sufficient number of GCPs in both the ground and the image reference systems. Non-parametric models require a large number of GCPs well distributed on the whole scene, but the GCP identification and collection is a widely time-consuming operation and not always a simple task. Authors have developed two procedures for geometric correction based respectively on the rational function model (RFM) and on a new neural network approach (MLP, multilayer perception), and a procedure for automatic ground control points (GCPs) extraction (AGE, automatic GCPs extraction) by means of a multi-resolution least squares matching technique. This paper concerns about a new orthorectification procedure based on the sequential application of AGE, MLP and RFM algorithms for georeferencing high resolution satellite images. Tests have been carried out on Eros-A1 satellite images, using as reference maps available aerial orthoimages at a map scale of 1:10000. A Case study is presented

Site Selection of Large Ground-Mounted Photovoltaic Plants: A GIS Decision Support System and an Application to Italy

Latterly, central governments and local authorities have been establishing various constraints on the construction of new large ground-mounted photovoltaic (PV) plants, because of the soil consumption, landscape impact, and also competitiveness with the crop production. This is particularly important in contexts where the agricultural sector is closely linked to the territory. With the aim of providing a decision support tool based on quantitative indicators for the site selection of large ground-mounted PV plants, in this article the criteria for the identification of areas suitable for the installation of ground-mounted photovoltaic systems, recently emerged by regional government or in the technical and scientific literature, are applied to the entire territory of the Piedmont region (25,000 km2). Both qualitative criteria for inclusion/exclusion (e.g., exclusion from areas of great value) and criteria for quantification (e.g., solar resource availability) were considered. The aggregation of the quantitative criteria into the final indicator is done by means of an Artificial Neural Network (ANN) trained with values corresponding to sites of existing PV plants in the Region. It emerges that the available areas are very limited, concentrated, and strongly influenced by the criteria of exclusion/inclusion. Some considerations on the significance of the results for the region of analysis are finally made.

Spatial patterns of Scaphoideus titanus (Hemiptera: Cicadellidae): a geostatistical and neural network approach

The spatial distribution of the leafhopper Scaphoideus titanus Ball, the vector of the pathogen Flavescence dorée of grapevine, was studied in the Asti Province (Piedmont) Italy. Field sampling of adults was carried out using yellow sticky traps both in vineyards subjected to different pest management regimes, and in woods containing American grapevines. The spatial correlation of S. titanus captures was studied using geostatistical analyses. An artificial neural network (ANN) was designed to operate as a spatial predictor driven by external factors (elevation, slope, height above channel, agricultural communities, perimeter-to-area ratio, potential solar radiation, and pest management) for estimating the population density of the leafhopper. The captures were spatially related up to 210 m: the variogram fitting was significant, but resulted in low R 2 values. The ANN achieved a significant generalization of the infestation levels of S. titanus, permitting prediction maps based upon simulated pest management scenarios to be obtained. The most important factors affecting S. titanus population density were pest management, and secondarily agricultural communities.

Assessing the Effect of Disturbances on the Functionality of Direct Protection Forests

Forests provide direct protection to human settlements from hydrogeomorphic hazards. This paper proposes a method for assessing the effect of natural disturbances on the functionality of direct protection forests (DPFs) in order to prioritize management interventions. We georeferenced disturbance data for wildfires, wind and snow damage, avalanches, and insects and overlaid them to a region-wide DPF map. Within each disturbance polygon, we used a Landsat-5 TM image to identify DPFs with insufficient vegetation cover, by using a maximum likelihood classifier of 6 spectral bands plus 5 vegetation indices. For each disturbance agent, we fitted a generalized linear model of the probability of finding a forested pixel, as a function of topography, time since disturbance, distance from disturbance edge, summer precipitation, and drought in the disturbance year. DPFs covered almost half of total forest area in the study region. Disturbance by insects occurred in more than one sixth of all forests. Avalanche and wildfire occurred each in about one tenth of total forest area, and wind and snow disturbance in only 1%. In the last 50 years, disturbances had a recurrence rate of 3% every 10 years. Almost one sixth of DPFs are currently lacking sufficient forest cover. Wildfires resulted in the highest rate of nonforested pixels (42% of all DPFs), followed by avalanches (21%). Forest recovery was explained by time elapsed, distance from edge (for conifers), and aspect. Summer precipitation and drought had a mixed influence. Our approach to assessing the effect of disturbances on the functionality of DPFs is reproducible in all mountain regions using institutional or open-access geographic data and provides a tool to prioritize DPF management by indicating where restoration of protection is most urgent.

DTMs generation from satellite stereo images: accuracy tests in mountain region

Digital Terrain Models (DTM) represent an effective tool for many applications and in particular for terrain morphology investigation and orthoimages generation. The availability of satellite stereo images allows to generate updated DTMs through digital photogrammetric algorithms especially in those areas where old and poor maps exist. In this work we show some quality tests results about DEMs obtained from ASTER data (15m geometric resolution), elaborated through commercial software. We consider this information very important to understand which kind of applications can reasonably use these data. Shown results refer to a mountain area located in the NW Italian Alps, characterized by different height type regions (flat, hilly, mountain) whose evaluation can drive to consistent results for a better understanding of limits and forces of these data. Such tests, making use of the commercial software AsterDTM, take into consideration both qualitative and quantitative aspects. Height profiles comparisons, statistical analysis on differences and data mining have been carried out in order to evaluate accuracies and to define the nature of possible systematic errors. Reference data is the available Regional DTM (50m x 50m grid, accuracy of 2.5m) and the single height points extracted from technical maps at 1:10000 scale for more precise local investigation.

Urban areas classification tests using high resolution pan-sharpened satellite images

The possibility of transferring high spectral contents of medium geometric resolution images obtained from traditional satellite images (TM; ETM+) and newer ones (ASTER, ENVISAT) to high resolution images has been considered to resolve the problems connected to large scale classification. This work suggests an operational approach to this problem. It points out that the aspects related to obtaining good results in an easy, economic and rapid way are as important as the scientific and technological aspects. The suggested method is based on the well known pan-sharpening technique; only a limited amount of experience can however be found in literature concerning its verification for real applications. The authors do not intend proposing new pan-sharpening algorithms in this paper, but rather to demonstrate how its correct use and the customisation of already known techniques (mainly used for aesthetic purposes) can produce interesting scientific results and can also solve some practical problems such as the management of large size data. In what follows that following is illustrated: the techniques that were adopted to generate pan-sharpened synthetic bands; some radiometric verifications that were performed on Landsat 5 TM are shown as are the results of elaborations on QuickBird images; some results of LVQ neural classifications that were carried out on 4 bands of a QuickBird image in an urban area generated with the previously described technique. A preliminary qualitative analysis has shown how a classical pixel-based classification approach, such as the one that is here proposed, is not sufficient to generate suitable thematic images of the correct discrimination of urban environments.

Metric quality evaluation of satellite high resolution images in urban areas

The recent acquisition of high resolution satellite images leads one to consider and evaluate their potential metric accuracy in order to point out if they can successfully be used for photogrammetric applications. This study shows some results referred to the planimetric accuracy obtained by orthocorrection of IKONOS 2 panchromatic images acquired on urban areas. The procedures have been carried on using the OrthoEngine7 satellite module available within the software PCI.