Agustín Lobo

Analysis of fine-scale spatial pattern of a grassland from remotely-sensed imagery and field collected data

An important practical problem in the analysis of spatial pattern in ecological systems is that requires spatially-intensive data, with both fine resolution and large extent. Such information is often difficult to obtain from field-measured variables. Digital imagery can offer a valuable, alternative source of information in the analysis of ecological pattern. In the present paper, we use remotely-sensed imagery to provide a link between field-based information and spatially-explicit modeling of ecological processes. We analyzed one digitized color infrared aerial photograph of a serpentine grassland to develop a detailed digital map of land cover categories (31.24 m × 50.04 m of extent and 135 mm of resolution), and an image of vegetation index (proportional to the amount of green biomass cover in the field). We conducted a variogram analysis of the spatial pattern of both field-measured (microtopography, soil depth) and image-derived (land cover map, vegetation index, gopher disturbance) landscape variables, and used a statistical simulation method to produce random realizations of the image of vegetation index based upon our characterization of its spatial structure. The analysis revealed strong relationships in the spatial distribution of the ecological variables (e.g., gopher mounds and perennial grasses are found primarily on deeper soils) and a non-fractal nested spatial pattern in the distribution of green biomass as measured by the vegetation index. The spatial pattern of the vegetation index was composed of three basic components: an exponential trend from 0 m to 4 m, which is related to local ecological processes, a linear trend at broader scales, which is related to a general change in topography across the study site, and a superimposed periodic structure, which is related to the regular spacing of deeper soils within the study site. Simulations of the image of vegetation index confirmed our interpretation of the variograms. The simulations also illustrated the limits of statistical analysis and interpolations based solely on the semivariogram, because they cannot adequately characterize spatial discontinuities.

Mapping invasive woody plants in Azores Protected Areas by using very high-resolution multispectral imagery

Abstract We assessed the effectiveness of very high spatial resolution IKONOS imagery for mapping a top invasive woody plant, Pittosporum undulatum, in a Protected Area in S.Miguel Island. We developed a segmentation-based classification scheme. A strong separability between most important land cover classes and a high accuracy in supervised classification maps was achieved. Overall separability improved significantly after the training data depuration process. Support Vector Machine and Maximum Likelihoods supervised classifiers showed a strong agreement and a good accuracy at land-cover class level, especially with P. undulatum. This approach was confirmed as a cost-effective method to map woody plant invaders in Azores Protected Areas.

Fine-scale mapping of a grassland from digitized aerial photography: An approach using image segmentation and discriminant analysis

Conventional methods of classification from remotely-sensed images seldom discriminate accurately among the land cover categories that are relevant in ecological applications. In the present study, we apply an image segmentation technique to a high-spatial-resolution (13.5cm), digitized, aerial, colour-infrared photograph of an annual grassland and subsequently identify land cover categories through field inspection and linear canonical discriminant analysis of the image. We show that per-segment statistics can be used to discriminate among four land cover categories bunch grasses, dense cover of annuals, sparse cover of annuals and bare ground while conventional per-pixel statistics produce low separabilities for the same categories. We also show that soil disturbances by pocket gophers (Thomomys bottae) can be identified and they are significantly concentrated in areas covered by bunch grasses at the time of image acquisition. We conclude that image segmentation and linear canonical discriminant analysi...

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard

Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38-0.70) and mean cluster weight (0.40). These results proved the utility of the

Linking GMES Space Component to the development of land policies in Outermost Regions - the Azores (Portugal) case-study

Abstract The aim of this study is to assess the potential effectiveness of GMES Space Component Sentinel Missions for land-based environmental policy support in the Azores Autonomous Region (Portugal). Sixteen different types of legal and spatial instruments are currently being applied in this region. Most of them require detailed and accurate Land-use/Land-cover cartography in order to deliver reliable outputs at municipal, island and archipelagic scales. Sentinel-2 Mission products can fulfill these requirements in a cost-effective way. A Spatial Data Infrastructure-based Regional GMES framework is proposed in order to process, assess, validate and integrate this GMES data into the decision support system of Azorean regional land policies.

Mapping crop planting quality in sugarcane from UAV imagery: A pilot study in Nicaragua

Sugarcane is an important economic resource for many tropical countries and optimizing plantations is a serious concern with economic and environmental benefits. One of the best ways to optimize the use of resources in those plantations is to minimize the occurrence of gaps. Typically, gaps open in the crop canopy because of damaged rhizomes, unsuccessful sprouting or death young stalks. In order to avoid severe yield decrease, farmers need to fill the gaps with new plants. Mapping gap density is therefore critical to evaluate crop planting quality and guide replanting. Current field practices of linear gap evaluation are very labor intensive and cannot be performed with sufficient intensity as to provide detailed spatial information for mapping, which makes replanting difficult to perform. Others have used sensors carried by land vehicles to detect gaps, but these are complex and require circulating over the entire area. We present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle (UAV) that produced a map of gaps at 23.5 cm resolution in a study area of 8.7 ha with 92.9% overall accuracy. Linear Gap percentage estimated from this map for a grid with cells of 10 m × 10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R2)= 0.9; a root mean square error (RMSE) = 5.04; and probability (p) << 0.01. Crop Planting Quality levels calculated from image-derived gaps agree with those calculated from a photo-interpreted version of currently used field methods (Spearman coefficient = 0.92). These results clearly demonstrate the effectiveness of processing mosaics of Unmanned Aerial System (UAS) images for mapping gap density and, together with previous studies using satellite and hand-held spectroradiometry, suggests the extension towards multi-spectral imagery to add insight on plant condition.

Land cover classification at a regional scale in Iberia: separability in a multi-temporal and multi-spectral data set of satellite images

Earth observation at regional scales, such as of the Iberian Peninsula or Mediterranean Basin, is an important tool to understand the relationships between climate and surface properties. Among the different layers of information that can be derived from satellite imagery, land cover maps are important by themselves and as an aid to infer other variables. Land cover legends at regional scales require finer categories than those used at a global scale, which implies processing multi-spectral imagery acquired by Earth observing systems with daily acquisition rates. In this article we discuss several alternatives to analyse satellite image datasets that are both multi-temporal and multi-spectral, with spatial resolution of 1 km2. In order to facilitate the interpretation of our results, we restrict our analysis to pixels that correspond to cells with a uniform and known cover on the ground, as described by a detailed vegetation map, in Catalonia (NE Spain). Our results indicate that canonical redundancy analysis is efficient at reducing the multi-spectral and multi-temporal space while keeping high statistical separability among habitat types. The small fraction of uniform pixels (∼2%) suggests that, at least for the Mediterranean Region, data fusion techniques would be convenient to increase spatial resolution in the dataset, and that instruments keeping daily acquisition rates but with higher spatial resolution (∼1 ha) should be considered.

Mapping forest fire impact from Landsat-TM imagery

We address the problem of estimating fire impact in Mediterranean forests based on Landsat-TM imagery, for which we have used a forest fire in Andalucia (S. Spain) as a case of study. We processed two LANDSAT-TM scenes, acquired before and after the date of the fire. The post-fire scene was segmented and a table of segment statistics was submitted to a hierarchical model-based agglomerative clustering. The boundary defined by the classification closely matches the boundary defined by helicopter. We also produced an image classification of the pre-fire image based on image segmentation and canonical analysis and studied the trajectories of burnt and not-burnt centroids in the Kauth- Thomas plane. The trajectories indicated the existence of a differential response to fire and to phenologic change. We modeled the post-fire conditions as if the region had not been burnt and defined as Index of Fire Impact as the difference between the actual and the modeled second Kauth-Thomas component. The Index of Fire Impact is significantly related to field estimates, but with a scatter that introduces uncertainty in the inversion.

Multivariate factorial analysis to design a robust batch leaching test to assess the volcanic ash geochemical hazard

A method to obtain robust information on short term leaching behaviour of volcanic ashes has been developed independently on the sample age. A mixed factorial design (MFD) was employed as a multivariate strategy for the evaluation of the effects of selected control factors and their interactions (amount of sample (A), contact time (B), and liquid to solid ratio or L/S (C)) on the leaching process of selected metals (Na, K, Mg, Ca, Si, Al, V, Mn, Fe, and Co) and anions (Cl(-) and SO(4)(2-)). Box plots of the data acquired were used to evaluate the reproducibility achieved at different experimental conditions. Both the amount of sample (A) and leaching time (B) had a significant effect on the element stripping whereas the L/S ratio influenced only few elements. The lowest dispersion values have been observed when 1.0 g was leached with an L/S ratio equal to 10, shaking during 4 h. The entire method is completed within few hours, and it is simple, feasible and reliable in laboratory conditions.

Assessing the impacts of the 2003 hot and dry spell with SPOT HRVIR images time series over south‐western France

A severe heatwave affected southern Europe and, in particular, south‐western France during the summer of 2003. The area was subjected to a severe dry spell with high temperatures and very little precipitation during a nearly 4‐month‐long period. A series of monthly 20‐m spatial resolution images, acquired between 2002 and 2003 by the SPOT HRVIR (High Resolution Visible–Infrared) was used to examine the impact of the dry and hot spell over a 50 km×50 km area of south‐western France. The use of the high spatial resolution data permitted the distinction of the various land surface types, and facilitated the diagnosis of the hot and dry spell effects for each class. The Normalized Difference Vegetation Index (NDVI) time series of the four principal vegetated surfaces (meadow, deciduous forest, wheat and maize) reveal different responses to the dryness and hot spell. A significant shortening of the 2003 phenological cycle was observed for the meadows, but the response was not as clear for the deciduous forests. For the crop surfaces, a shortening of the cycle is observed, although the impact of the drought was translated differently as a function of the crop type.