Alejandra A. López-Caloca

Tropical Dry Forests in the Global Picture: The Challenge of Remote Sensing-Based Change Detection in Tropical Dry Environments

Global environmental change has recently pushed the scientific community in the quest for more comprehensive spatial information on the continental biosphere. In terms of climate change, ecosystem monitoring has become one of the priorities to better understand the evolution of terrestrial carbon stocks, as well as to foster conservation policies for these carbon stocks. According to IPCC (2002), deforestation and land clearing activities, mostly from sub-tropical regions, contributed with one fifth of the greenhouse gas emission during the 1990s. The tropical dry forests are one of the most extended tropical forested ecosystems, and yet have received only recent attention from the scientific community. This ecosystem is also scarcely represented in the international protection schemes, which perhaps causes increased vulnerability of this ecosystem to the tropical fingerprint of global human development. Additionally, the climatic conditions are relatively attractive for human settlement and the ecosystem has historically supported dense agriculture activity. In megadiverse Mexico for example, these forests extend up to 60% of tropical forests, and an estimated 30% of this extent is considered as highly modified under anthropic pressure. The annual deforestation rate of the deciduous tropical dry forest in Mexico has been evaluated at around 1.4 2 %. The contribution of the latter to climate change is manifolds, including carbon emissions, increased albedo and regional hydrographic cycle alteration. Moreover, the very loss of biodiversity derived from the conversion of forest to grassland for pasture is considered as a triggering factor for future forest fires and conversion to more grassland. The monitoring and analysis of the forest distribution pattern, including phenological and anthropogenic modifications, contributes to the uneasy task of slowing down the tendency of forest loss. Remote sensing has proved a fundamental tool for such monitoring, owing to its contribution to the study and understanding of the global environment through time, and the calibration of models which help building environmental scenarios in the future.

Lake Chapala change detection using time series

The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lakes limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

Image fusion with the Hermite transform

The Hermite transform is an image representation model that incorporates some important properties of visual perception such as the analysis through overlapping receptive fields and the Gaussian derivative model of early vision. It also allows the construction of pyramidal multiresolution analysis-synthesis schemes. We show how the Hermite transform can be used to build image fusion schemes that take advantage of the fact that Gaussian derivatives are good operators for the detection of relevant image patterns at different spatial scales. These patterns are later combined in the transform coefficient domain. Applications of this fusion algorithm are found in medical imagery and remote sensing, name.

Satellite Remote Sensing of Coral Reef Habitats Mapping in Shallow Waters at Banco Chinchorro Reefs, México: A Classification Approach

Interest in protecting nature has arisen in contemporary society as awareness has developed of the serious environmental crisis confronting us. One of the ecosystems most impacted is the coral reefs, which while offering a great wealth of habitats, diversity of species and limitless environmental services, have also been terribly damaged by anthropogenic causes. One example of this is the oil spill from petroleum platforms (in the recent case of the Gulf of Mexico). The effects of global warming—such as the increase in the incidence and intensity of hurricanes and drastic changes in ocean temperature—have caused dramatic damage, such as the bleaching and decrease of coral colonies. In light of this devastating situation, scientific studies are needed of coral reef communities and the negative effects they are undergoing.

Multiresolution fusion of remotely sensed images with the Hermite transform

The Hermite Transform is an image representation model that incorporates some important properties of visual perception such as the analysis through overlapping receptive fields and the Gaussian derivative model of early vision. It also allows the construction of pyramidal multiresolution analysis-synthesis schemes. We show how the Hermite Transform can be used to build image fusion schemes that take advantage of the fact that Gaussian derivatives are good operators for the detection of relevant image patterns at different spatial scales. These patterns are later combined in the transform coefficient domain. Applications of this fusion algorithm are shown with remote sensing images, namely LANDSAT, IKONOS, RADARSAT and SAR AeS-1 images.

Data Fusion Approach for Employing Multiple Classifiers to Improve Lake Shoreline Analysis

Remote sensing images have been widely employed to analyze bodies of water and have become essential to studying their dynamics. While the use of indices based on the threshold segmentation technique is preferred, the search for methods that define water edge contour continues. The segmentation algorithm introduced in this study is based on Mean-Shift and Watershed methods. We propose a fusion classifier strategy which allows us to obtain results that are consistent with the segmentation process. The use of two or more segmentation processes has been shown to improve pattern recognition. It is important to implement a good data integration scheme. Preliminary results suggest that the approach reported herein can improve the definition of lake shorelines.

Monitoring evapotranspiration at landscape scale in Mexico: applying the energy balance model using remotely sensed data

Thermal and spectral remotely sensed data make the monitoring from flux energy variables in the land atmosphere interface possible. Therefore, remotely sensed data can be used as an alternative to estimate actual evapotranspiration (ET) by applying the energy balance equation. In order to test the applicability of this approach in Mexico, MODIS (Moderate Resolution Imaging Spectroradiometer) estimations from land surface variables are used at 16-day intervals of composite data. Ancillary information is collected from 2000 ground stations. The methodology includes the Simplified Surface Energy Balance model (SSEB) and its intercomparison with a combined model from the Surface Energy Balance Algorithm (SEBAL) and the Two Source Energy Balance (TSEB) procedures. Preliminary results applied to one 16-day interval during winter, 2002, showed that ET is spatially structured at a landscape level. The most significant discrepancies between estimations are found due to the general assumptions applied to each model. Secondly, the use of interpolated ancilliary data from local observations, along with remote sensing data, provides a better representation of spatial variations of ET with SEBAL-TSEB model for the study period. There is not enough evidence to asses objectively the performance of both applied procedures. Further testing is required to evaluate at a local scale the reliability from estimations.

Mapping and characterization of urban forest in Mexico City

Mapping and characterization of forest and vegetation are particularly challenging in urban areas. High resolution imagery is needed for mapping and characterization purposes, due to the areal extent of urban forests, parks and recreational areas. Fusion techniques of panchromatic (1m resolution) and multiband (4m resolution) IKONOS data were used for mapping and characterization of land covering characteristics of urban green areas, allowing the identification of parks, tree areas and fields with a minimal mapping unit of 160 m2. Techniques, that integrate the fine details of the input data into the fused image, are used. Experimental results for different image fusion methods (Laplacian, Gradient pyramids, Principal Component Analysis and Wavelet transform) are also demonstrated in order to improve spatial resolution. Classification of urban areas, mapped with fused data, results in higher accuracies than when using a multiband approach with 4 m data alone. Furthermore, high spatial resolution data permitted to obtain new areal extents of green areas of the city, giving a better estimate of international indicators for a suitable green areas policy. Vegetation indexes derived from red and near infrared data IKONOS are used to evaluate vegetation conditions, which, along with their distribution, location and urban context, resulted in better indicators of green areas.

Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion

Several built-up indices have been proposed in the literature in order to extract the urban sprawl from satellite data. Given their relative simplicity and easy implementation, such methods have been widely adopted for urban growth monitoring. Previous research has shown that built-up indices are sensitive to different factors related to image resolution, seasonality, and study area location. Also, most of them confuse urban surfaces with bare soil and barren land covers. By gathering the existing built-up indices, the aim of this paper is to discuss some of their advantages, difficulties, and limitations. In order to illustrate our study, we provide some application examples using Sentinel 2A data.

Data fusion approach for Urban area identification using multisensor information

This article presents a procedure to identify and extract urban areas in medium resolution satellite images. At present, we have and continue to study various methodologies to process and extract information on urban surfaces, since urban growth is having environmental impacts on the involved ecological systems. The proposed method takes advantage of the fact that data fusion allows us to combine in an optimal manner, multiple sources of classifiers and to generate a single source of information. In this context, we propose the use of data fusion algorithms, by multiple classifiers, taking into account the spectral and spatial characteristics of the satellite data, which in our case are the Landsat ETM+ and the ENVISAT-ASAR. The developed system includes an ensemble fusion architecture and the use of algorithms such as Fuzzy K-mean and Markov Random Field (MRF). The study case is the Guadalajara metropolitan area, in Jalisco, Mexico, which has great growth and sprawl; in its surrounding areas there are regions which are interesting in terms of geothermal exploitation and with great ecological value. The experimental results, using the multiple classifier system (MCS) show the urban characteristics at the regional scale, offering results that are potentially significant at this scale and the direction of changes in urban growth.