Ignacio Melendez-Pastor

Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area

Abstract: Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean land-covers from the perspective of vegetation phenology and its relation with climate. A time series from 2001 to 2007 of the MODIS Enhanced Vegetation Index 16-day composite (MOD13Q1) was analyzed to extract anomalies (by calculating z-scores) and frequency domain components (by the Fourier Transform). Vegetation phenology analyses were developed for diverse land-covers for an area in south Alicante (Spain) providing a useful way to analyze and understand the phenology associated to those land-covers. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the

Multi-resolution and temporal characterization of land-use classes in a Mediterranean wetland with land-cover fractions

Four different methods for analysing land-use and land-cover fractions at multiple scales, namely composite operator, t-test, Dutilleul’s modified t-test and ternary diagrams of physical models for process pathways, were applied to sets of multi-resolution images in order to evaluate the usefulness of coarse-resolution satellite data (e.g. the Moderate Resolution Imaging Spectroradiometer; MODIS) in obtaining similar results to those obtainable with moderate-resolution satellite data (e.g. Landsat). A spectral-mixture model based on three endmembers (soil, vegetation and water) was used to determine the land-cover fractions of the main land-use classes of a wetland in southeast Spain. The land-use map was produced by applying the unsupervised k-means classification method to the moderate-resolution image. Spatial and temporal changes in the mixture fractions at multiple resolutions and their corresponding land-cover fraction maps were assessed. Three different t-tests (paired-samples, independent-samples and Dutilleul’s modified t-tests) were used to evaluate the effects of pixel aggregation on land-cover fractions and land-use maps in terms of surface-area estimations. Ternary plots of land-use classes characterized by land-cover fractions were used to visualize environmental processes pathways describing temporal changes in the landscape. The results obtained with moderate- and coarse-resolution data were not significantly different from each other. Land-use and land-cover surface-area estimations were not significantly different between Landsat moderate-resolution (30 m) and Landsat resampled coarse-resolution (300 m) data. Spatial autocorrelation had an important effect when comparing Landsat moderate-resolution (30 m) with MODIS coarse-resolution (250 m) data. In order to minimize these effects Dutilleul’s modified t-test was applied for the comparison of Landsat with MODIS image data. However, this test did not reveal significant differences between both datasets, whereas with the ordinary t-test, significant differences were found, which suggest the existence of a bias by spatial autocorrelation that must be taken into account for up-scaling or down-scaling of remote-sensing data. The results suggest the possibility of using coarse-resolution images (MODIS) to characterize environmental changes with a similar accuracy to those of moderate-resolution images (Landsat), as long as potential spatial autocorrelation effects are taken into account. This finding indicates that a substantial reduction in the costs of conducting wetland management and monitoring tasks can be achieved by using free or low-cost coarse-resolution satellite images.

Spectral indices for the detection of salinity effects in melon plants

Water scarcity and soil salinization affect large semiarid agricultural areas throughout the world. The maintenance of agricultural productivity implies better agricultural practices and a careful selection of resistant crops. A proper monitoring of the physiological status of plants can lead to better knowledge of plant nutritional requirements. Visible and near-infrared (VNIR) radiometry provides a non-destructive and quantitative method to monitor vegetation status by quantifying chemical properties using spectroscopic techniques. In this study, the capability of VNIR spectral measurements to detect salinity effects on melon (Cucumis melo L.) plants was tested. Melon plants were cultivated under multiple soil salinity conditions (electrical conductivity, (EC)1:5: 0.5, 1.0 and 2.5 dS m-1). Spectral data of leaves were transformed into vegetation indices indicative of the physiological status of the plants. The results showed differences for N (p < 0.05), K and Na content (p < 0.01) due to salinity suggesting different degrees of salt stress on the plants. Specific leaf area increased with salinity levels (p < 0.001). The capabilities of VNIR radiometry to assess the influence of soil salinity on melon physiology using a non-destructive method were demonstrated. A normalized difference vegetation index (NDVI750-705), and the ratio between water index (WI) and normalized difference vegetation index (WI/NDVI750-705) showed significant relationships (p < 0.01) with the salinity. Therefore, this method could be used for in-situ early detection of salinity stress effects.

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain

Soil salt content is a key factor that determines soil chemical quality together with soil reaction, charge properties and nutrient reserves (Lal et al., 1999). An adequate salt supply is essential for an optimum development of photosynthetic mechanism and other biochemical processes in plants (Sitte et al., 1994). Soil salt content constitutes an environmental problem when salt accumulation generates drastic changes in soil physical and chemical properties, adversely affecting soil productivity and plant growth (Richards, 1954; Qadir et al., 2000).

A Model for Evaluating Soil Vulnerability to Erosion Using Remote Sensing Data and A Fuzzy Logic System

Soil vulnerability is the capacity of one or more of the ecological functions of the soil system to be harmed. It is a complex concept which requires the identification of multiple environmental factors and land management at different temporal and space scales. The employment of geospatial information with good update capabilities could be a satisfac‐ tory tool to assess potential soil vulnerability changes in large areas. This chapter pres‐ ents the application of two land degradation case studies which is simple, synoptic, and suitable for continuous monitoring model based on the fuzzy logic. The model combines topography and vegetation status information to assess soil vulnerability to land deg‐ radation. Topographic parameters were obtained from digital elevation models (DEM), and vegetation status information was derived from the computation of the normalized difference vegetation index (NDVI) satellite images. This spectral index provides rele‐ vance and is updated for each scene, evidences about the biomass and soil productivity, and vegetation density cover or vegetation stress (e.g., forest fires, droughts). Modeled output maps are suitable for temporal change analysis, which allows the identification of the effect of land management practices, soil and vegetation regeneration, or climate effects.

Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring

Conventional wastewater treatment generates large amounts of organic matter–rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities. The composting process is chemically and microbiologically complex and requires an adequate aeration of the biosolid (e.g., with a turner machine) for proper maturation of the compost. Adequate (near) real-time monitoring of the compost maturity process is highly difficult and the operation of composting facilities is not as automatized as other industrial processes. Spectroscopic analysis of compost samples has been successfully employed for compost maturity assessment but the preparation of the solid compost samples is difficult and time-consuming. This manuscript presents a methodology based on a combination of a less time-consuming compost sample preparation and ultraviolet, visible and short-wave near-infrared spectroscopy. Spectroscopic measurements were performed with liquid compost extract instead of solid compost samples. Partial least square (PLS) models were developed to quantify chemical fractions commonly employed for compost maturity assessment. Effective regression models were obtained for total organic matter (residual predictive deviation—RPD = 2.68), humification ratio (RPD = 2.23), total exchangeable carbon (RPD = 2.07) and total organic carbon (RPD = 1.66) with a modular and cost-effective visible and near infrared (VNIR) spectroradiometer. This combination of a less time-consuming compost sample preparation with a versatile sensor system provides an easy-to-implement, efficient and cost-effective protocol for compost maturity assessment and near-real-time monitoring.

Multitemporal Analysis in Mediterranean Forestland with Remote Sensing

The study employs a Fourier transform analysis approach to assess the land-cover changes in a mountainous Mediterranean protected area using multi-temporal satellite images. Harmonic analysis was applied to a time series of Landsat satellite images acquired from 1984 to 2008 to extract information about land cover status with a vegetation spectral index, the Normalized Difference Vegetation Index (NDVI). Ancillary cartographic information depicting land cover classes and the enlargement of the protected area over time (i.e., maps showing the original delineation in 1995 and subsequent enlargement in 2007) were employed as additional factors to understand vegetation-cover changes. Significant differences in the NDVI and harmonic compo‐ nents values were observed with respect to both factors. The application of the Fourier transform was particularly successful to extract subtle information. The harmonic analysis of the NDVI time series revealed valuable information about the evolution of the landscape. The initially protected area (northern sector) seems more affected by human activities than the southern sector (enlarged area in 2007) as revealed by the analysis of the first harmonic component that was closely related with vegetation coverage. Rural abandonment is a major driver of land-cover changes in the study area.