Alicia Palacios-Orueta

Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods

Predicting fire hazard in fire-prone ecosystems in urbanized landscapes, such as the chaparral systems of California, is critical to risk assessment and mitigation. Understanding the dynamics of fire spread, topography and vegetation condition are necessary to increase the accuracy of fire risk assessment. One vital input to fire models is spatial and temporal estimates of canopy water content. However, timely estimates of such a dynamic ecosystem property cannot be provided for more than periodic point samples using ground based methods. This study examined the potential of three quasiphysical methods for estimating water content using remotely sensed Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data of chaparral systems in the Santa Monica Mountains, California. We examined estimates of water content at the leaf, canopy, and image level and compared them to each other and to ground-based estimates of plant water content. These methods predicted water content (with R2 between 0.62 and 0.95) but differ in their ease of use and the need for ancillary data inputs. The prospect for developing regional estimates for canopy water content at high spatial resolution (20 m) from high resolution optical sensors appears promising.

Remote Sensing of Soil Properties in the Santa Monica Mountains I. Spectral Analysis

Abstract AVIRIS (Advanced Visible/Infrared Imaging Spectrometer) bands were simulated from laboratory spectra to test their performance in analyzing soil properties from a semiarid region. Multivariate analysis, specifically principal component analysis and canonical discriminant analysis, as well as band depth analysis were applied to study the effect of organic matter, iron content, and texture in a set of samples from two valleys in the Santa Monica Mountains Recreation Area, California. Results showed that total iron and organic matter content were the main factors affecting spectral shape, although sand content significantly affected the spectral contrast of the absorption features. It was shown as well that the elimination of the atmospheric water bands from the analysis did not strongly affect the retrieval of spectral information related to these properties.

Remote sensing of soils in the Santa Monica Mountains: II. Hierarchical foreground and background analysis.

Hierarchical foreground and background analysis (HFBA) was used to discriminate soil properties from two valleys in the Santa Monica Mountains Recreation Area, California. The analysis was organized in two levels. First, spectral data from laboratory measured soil samples were used to train a vector in AVIRIS data for classifying the soils between valleys. The prediction of organic matter and iron contents is performed at a second level of resolution. Results showed that, in the laboratory, soils could be classified at a high level of accuracy. When applied to the image, the spatial predictions of organic matter and iron content were consistent for the first level of classification. The ranges of predicted organic matter and iron contents developed at the second level of classification were also consistent with the magnitude and distribution of field samples. The presence of vegetation and the steep terrain affect adversely the ability to resolve these soil properties.

Multivariate statistical classification of soil spectra

Abstract The purpose of this work was to evaluate whether AVIRIS (Advanced Visible Infrared Imaging Spectrometer) bands can be used to discriminate between soils having similar properties, as well as to compare AVIRIS spectra with those from laboratory measurements. Multivariate analysis techniques show that two soils belonging to the same series and a third soil belonging to a different, but related series can be discriminated at a high level of accuracy using reflectance data from AVIRIS or from laboratory measurements. It is also shown that wavelengths important in discriminating soils were highly correlated between AVIRIS and laboratory data. The distribution of variance and weighting functions also show consistent patterns between these data sets.

Characterization of Soil Erosion Indicators Using Hyperspectral Data From a Mediterranean Rainfed Cultivated Region

The determination of surface soil properties is an important application of remotely sensed hyperspectral imagery. Moreover, different soil properties can be associated with erosion processes, with significant implications for land management and agricultural uses. This study integrates hyperspectral data supported by morphological and physico-chemical ground data to identify and map soil properties that can be used to assess soil erosion and accumulation. These properties characterize different soil horizons that emerge at the surface as a consequence of the intensity of the erosion processes, or the result of accumulation conditions. This study includes: 1) field and laboratory characterization of the main soil types in the study area; 2) identification and definition of indicators of soil erosion and accumulation stages (SEAS); 3) compilation of the site-specific MEDiterranean Soil Erosion Stages (MEDSES) spectral library of soil surface characteristics using field spectroscopy; 4) using hyperspectral airborne data to determine a set of endmembers for different SEAS and introducing these into the support vector machine (SVM) classifier to obtain their spatial distribution; and 5) evaluation of the accuracy of the classification applying a field validation protocol. The study region is located within an agricultural region in Central Spain, representative of Mediterranean agricultural uses dominated by a gently sloping relief, and characterized by soils with contrasting horizons. Results show that the proposed method is successful in mapping different SEAS that indicate preservation, partial loss, or complete loss of fertile soils, as well as down-slope accumulation of different soil materials.

Development of land degradation spectral indices in a semi-arid Mediterranean ecosystem

The goal of this study is to develop remote sensing desertification indicators for drylands, in particular using the capabilities of imaging spectroscopy (hyperspectral imagery) to derive soil and vegetation specific properties linked to land degradation status. The Cabo de Gata-Nijar Natural Park in SE Spain presents a still-preserved semiarid Mediterranean ecosystem that has undergone several changes in landscape patterns and vegetation cover due to human activity. Previous studies have revealed that traditional land uses, particularly grazing, favoured in the Park the transition from tall arid brush to tall grass steppe. In the past ~40 years, tall grass steppes and arid garrigues increased while crop field decreased, and tall arid brushes decreased but then recovered after the area was declared a Natural Park in 1987. Presently, major risk is observed from a potential effect of exponential tourism and agricultural growth. A monitoring program has been recently established in the Park. Several land degradation parcels presenting variable levels of soil development and biological activity were defined in summer 2003 in agricultural lands, calcareous and volcanic areas, covering the park spatial dynamics. Intensive field spectral campaigns took place in Summer 2003 and May 2004 to monitor inter-annual changes, and assess the landscape spectral variability in spatial and temporal dimension, from the dry to the green season. Up to total 1200 field spectra were acquired over ~120 targets each year in the land degradation parcels. The targets were chosen to encompass the whole range of rocks, soils, lichens, and vegetation that can be observed in the park. Simultaneously, acquisition of hyperspectral images was performed with the HyMap sensor. This paper presents preliminary results from mainly the field spectral campaigns. Identifying sources of variability in the spectra, in relation with the ecosystem dynamics, will allow the definition of spectral indicators of change that can be used directly to derive the desertification status of a land.

Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models

Abstract The aim of this research was to model and forecast MODIS-based Fire Potential Index (FPI), implemented with Normalized Difference Water Index (NDWI), as a proxy of forest fire risk, in Navarre (Spain) on a pixel basis using time series models with a forecasting horizon of one year. We forecast FPI NDWI for 2009 based on time series from 2001 to 2008. In the modeling process, the Box and Jenkins methodology was applied in two consecutive stages. First, several generic models based on average FPI NDWI time series from different “fuel type-ecoregion” combinations were developed. In a second stage, the generic models were implemented at the pixel level for the entire study region. The usefulness of the proposed autoregressive (AR) model, using the original data and introducing significant seasonal AR parameters, was demonstrated. Results show that 93.18% of the estimated models (EMs) are highly accurate and present good forecasting ability, precisely reproducing the original FPI NDWI dynamics. Best results were found in the Mediterranean areas dominated by grasslands; slightly lower accuracies were found in the temperate and alpine regions, and especially in the transition areas between them and the Mediterranean region.

Spectral characterisation of land surface composition to determine soil erosion within semiarid rainfed cultivated areas

In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.

Estimating Latent and Sensible Heat Fluxes using the Temperature Vegetation Dryness Index and MODIS Data

In this work, daily latent heat (lambdaE_d) or evapotranspiration and sensible heat (H_d) fluxes were estimated from the TVDI (Temperature Vegetation Dryness Index) modified to account for climatic gradients (TVDI_t) using MODIS data in a Spanish region (Andalusia) with strong bioclimatic gradients. The TVDI_t was correlated (R=0.90) with field measured AWC (available water content). When reinterpreting TVDI_t as the ratio between actual and potential evapotranspiration to estimate surface energy fluxes, model vs. eddy covariance data from 2 semiarid sites were reasonable for H_d (R=0.94: RMSE=25.89 Wm^-2). For lambdaE_d RMSE was low (19.46 Wm^-2) but correlations were only significant after excluding summer dates (R=0.71) when transpiration is very low. Model accuracy is currently limited by the accuracy in Rn_d estimates (<27 Wm^-2) for which results at humid sites should be better than at semiarid sites.

Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

Agroforestry ecosystems have a significant social, economic and environmental impact on the development of many regions in the world. On the Iberian Peninsula the Mediterranean agroforestry oak forest known as the dehesa or montado (usually formed by species of the genus Quercus) is considered to be the extreme case of transformation of a Mediterranean forest by human management to provide a wide range of natural resources. The great variability of the Mediterranean climate and the different extensive management practices carried out by humans on the dehesa produces a high spatial and temporal variability in the dynamics of the ecosystem. This leads to a complex pattern of CO2 exchange between the atmosphere and the ecosystem that can act as a sink or as a source of CO2 over the years, depending on the various factors interacting with them. It is thus essential to assess the carbon cycle on the dehesa in order to obtain the maximum economic benefits and ensure environmental sustainability. The availability of high-frequency remote sensing time series allows the evolution of an ecosystem to be assessed at different temporal and spatial scales. In this study our overall objective is to assess the gross primary production (GPP) dynamics of a dehesa ecosystem in Central Spain by analysing the time series (2004–2008) of two models: (1) GPP provided by remote sensing images from the MODIS sensor (MOD17A2 product); and (2) GPP estimated by the implementation of a site-specific light-use efficiency model taking into account local ecological and meteorological parameters. Both models were compared to the production provided by an eddy covariance flux tower located in our study area. Dynamic relationships between models of GPP and precipitation and soil water content were investigated by means of cross-correlations and Granger causality tests. Our results indicate that both models of GPP show a typical dehesa dynamic where there are primarily two layers, the arboreal and the herbaceous strata. However, MODIS underestimates the production of the dehesa in a Mediterranean climate, while our site-specific model produced more similar values and dynamics to those of the eddy covariance tower. The analysis of the dynamic relationships corroborated the strong dynamic link between GPP and available water for plant growth. In conclusion, we succeeded in avoiding the main source of underestimation of the MODIS model by the implementation of a site-specific model. It therefore appears that the different ecological and meteorological parameters used in the MODIS model are primarily responsible for this underestimation. Finally, the Granger causality tests indicate that GPP prediction can be improved by including precipitation or soil water in the models.