GIS and Remote Sensing Unit

San Michele Mondovì, Italy

GIS and Remote Sensing Unit

San Michele Mondovì, Italy
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Strever A.E.,Stellenbosch University | Bezuidenhout D.,Software Engineering | Zorer R.,GIS and Remote Sensing Unit | Moffat T.,Stellenbosch University | Hunter J.J.,Stellenbosch University
SAIEE Africa Research Journal | Year: 2012

In this article, some optical and thermal applications in grapevine research are reviewed and methods to quantify the light and temperature regime around a grape bunch are discussed. This includes temperature measurement techniques (thermocouples and thermal imaging) as well as methods to quantify light quantity (hemispherical photography) as well as light quality (spectroradiometric applications) around a grape bunch. Available methods for real-time quantification of grapevine canopy size and density for application in variable rate technology sprayers are discussed, and a novel and simple approach of using opto-electronic sensors for quantification of grapevine canopy thickness and density is presented. Some scientific as well as practical applications of these individual techniques are discussed, along with their potential integration to improve knowledge of the grape bunch and canopy interaction with the environment.

Garzon-Lopez C.X.,University of Groningen | Garzon-Lopez C.X.,GIS and Remote Sensing Unit | Ballesteros-Mejia L.,University of Groningen | Ballesteros-Mejia L.,Federal University of Goais | And 7 more authors.
Ecology Letters | Year: 2015

The coexistence of numerous tree species in tropical forests is commonly explained by negative dependence of recruitment on the conspecific seed and tree density due to specialist natural enemies that attack seeds and seedlings ('Janzen-Connell' effects). Less known is whether guilds of shared seed predators can induce a negative dependence of recruitment on the density of different species of the same plant functional group. We studied 54 plots in tropical forest on Barro Colorado Island, Panama, with contrasting mature tree densities of three coexisting large seeded tree species with shared seed predators. Levels of seed predation were far better explained by incorporating seed densities of all three focal species than by conspecific seed density alone. Both positive and negative density dependencies were observed for different species combinations. Thus, indirect interactions via shared seed predators can either promote or reduce the coexistence of different plant functional groups in tropical forest. © 2015 John Wiley & Sons Ltd/CNRS.

Wegmann M.,University of Würzburg | Santini L.,University of Rome La Sapienza | Leutner B.,University of Würzburg | Safi K.,Max Planck Institute for Ornithology (Radolfzell) | And 7 more authors.
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2014

The African protected area (PA) network has the potential to act as a set of functionally interconnected patches that conserve meta-populations of mammal species, but individual PAs are vulnerable to habitat change which may disrupt connectivity and increase extinction risk. Individual PAs have different roles inmaintaining connectivity, depending on their size and location. We measured their contribution to network connectivity (irreplaceability) for carnivores and ungulates and combined it with a measure of vulnerability based on a 30-year trend in remotely sensed vegetation cover (Normalized Difference Vegetation Index). Highly irreplaceable PAs occurred mainly in southern and eastern Africa. Vegetation cover change was generally faster outside than inside PAs and particularly so in southernAfrica. The extent of change increased with the distance from PAs. About 5% of highly irreplaceable PAs experienced a faster vegetation cover loss than their surroundings, thus requiring particular conservation attention. Our analysis identified PAs at risk whose isolationwould disrupt the connectivity of the PA network for large mammals. This is an example of how ecological spatial modelling can be combined with large-scale remote sensing data to investigate how land cover change may affect ecological processes and species conservation. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Baluja J.,University of La Rioja | Diago M.P.,University of La Rioja | Balda P.,University of La Rioja | Zorer R.,GIS and Remote Sensing Unit | And 3 more authors.
Irrigation Science | Year: 2012

The goal of this study was to assess the water status variability of a commercial rain-fed Tempranillo vineyard (Vitis vinifera L.) by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). The relationships between aerial temperatures or indices derived from the imagery and leaf stomatal conductance (g s) and stem water potential (Ψ stem) were determined. Aerial temperature was significantly correlated with g s (R 2 = 0.68, p <0.01) and Ψ stem (R 2 = 0.50, p < 0.05). Furthermore, the thermal indices derived from aerial imagery were also strongly correlated with Ψ stem and g s. Moreover, different spectral indices were related to vineyard water status, although NDVI (normalized difference vegetation index) and TCARI/OSAVI (ratio between transformed chlorophyll absorption in reflectance and optimized soil-adjusted vegetation index) showed the highest coefficient of determination with Ψ stem (R 2 = 0.68, p < 0.05) and g s (R 2 = 0.84, p < 0.05), respectively. While the relationship with thermal imagery and water status parameters could be considered as a short-term response, NDVI and TCARI/OSAVI indices were probably reflecting the result of cumulative water deficits, hence a long-term response. In conclusion, thermal and multispectral imagery using an UAV allowed assessing and mapping spatial variability of water status within the vineyard. © 2012 Springer-Verlag.

Feilhauer H.,Friedrich - Alexander - University, Erlangen - Nuremberg | He K.S.,Murray State University | Rocchini D.,GIS and Remote Sensing Unit
Remote Sensing | Year: 2012

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species. © 2012 by the authors.

Amici V.,University of Siena | Landi S.,University of Bologna | Frascaroli F.,University of Bologna | Frascaroli F.,University of Zürich | And 3 more authors.
Biodiversity and Conservation | Year: 2015

Changes in land use are among the forces shaping Earth’s surface. In many industrialized areas, the loss of a traditional state of dynamic equilibrium between traditional management and natural dynamics is followed by abandonment to regeneration processes. This can reduce ecological complexity at the landscape scale and negatively affect biodiversity patterns. In this study, we investigate the relation between land use change and plant species diversity in the network of protected areas (PAs) of the province of Siena (Tuscany, Central Italy). This is an area characterized by long-lasting human activities and highly renowned cultural landscapes. We used remotely sensed, mapping and ground based plant compositional data, to investigate the present pattern of plant species diversity, the changes of landscape structure and changes in forest habitats. Most of the plant diversity present in this network of PAs is due to broad scale gradients due to ecological diversity but also to human management. Most of the area is currently covered by forests and analysis of a historical sequence of spatial data reveals that this is largely a consequence of the abandonment of traditional management during the last decades. Finally, focusing on forest succession as a consequence of land use change, we demonstrate that species richness significantly declines with increasing age of forest stands. Taken together, our results confirm that the recent trends of rural abandonment are leading to homogenization and biodiversity loss in traditional landscapes of Mediterranean Europe. We discuss implications for policy, and suggest that PA management in cultural and historical landscapes should pay increasing attention traditional anthropic practices. © 2015, Springer Science+Business Media Dordrecht.

Bacaro G.,University of Siena | Santi E.,CNR Research Institute for Geo-hydrological Protection | Rocchini D.,University of Siena | Rocchini D.,GIS and Remote Sensing Unit | And 4 more authors.
Biodiversity and Conservation | Year: 2011

Identifying spatial patterns in species diversity represents an essential task to be accounted for when establishing conservation strategies or monitoring programs. Predicting patterns of species richness by a model-based approach has recently been recognised as a significant component of conservation planning. Finding those environmental predictors which are related to these patterns is crucial since they may represent surrogates of biodiversity, indicating in a fast and cheap way the spatial location of biodiversity hotspots and, consequently, where conservation efforts should be addressed. Predictive models based on classical multiple linear regression or generalised linear models crowded the recent ecological literature. However, very often, problems related with spatial autocorrelation in observed data were not adequately considered. Here, a spatially-explicit data-set on birds presence and distribution across the whole Tuscany region was analysed. Species richness was calculated within 1 × 1 km grid cells and 10 environmental predictors (e.g. altitude, habitat diversity and satellite-derived landscape heterogeneity indices) were included in the analysis. Integrating spatial components of variation with predictive ecological factors, i.e. using geostatistical models, a general model of bird species richness was developed and used to obtain predictive regional maps of bird diversity hotspots. A meaningful subset of environmental predictors, namely habitat productivity, habitat heterogeneity, combined with topographic and geographic information, were included in the final geostatistical model. Conservation strategies based on the predicted hotspots as well as directions for increasing sampling effort efficiency could be extrapolated by the proposed model. © 2011 Springer Science+Business Media B.V.

Metz M.,GIS and Remote Sensing Unit | Rocchini D.,GIS and Remote Sensing Unit | Neteler M.,GIS and Remote Sensing Unit
Remote Sensing | Year: 2014

Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications. © 2014 by the authors.

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