Jesus Soria-Ruiz

Land-cover classification using radar and optical images: a case study in Central Mexico

Land-cover studies based on optical remote sensing in regions which exhibit disorderly urban growth and quick-use conversion of farmland to non-farm usage face problems due to inaccurate discrimination of cover types and hence inaccurate extent estimations. The use of data in the visible and infrared areas of the electromagnetic spectrum for classifying crop types has been extensively explored, concluding that data acquisitions must be made during critical crop development periods. This raises a concern in Central Mexico where such periods coincide with important cloud coverage and where good estimates of the extent of agricultural areas and of particular crops are keenly sought by government agencies for planning purposes. Due to the interest in accurate and updated maps for this area, repeated studies have been carried out over a number of years by the National Institute of Research for Forestry, Agriculture and Livestock for the Ministry of Agriculture of Mexico. Taking into consideration the difficulties of acquiring and analysing data derived from optical sensors, the objective of this study was to assess the advantages of combining synthetic aperture radar (SAR) and optical remote sensing in producing more accurate maps. The study area covers 15 634 ha and is located in Central Mexico in a region where agricultural plots of varied sizes and forms are interspersed with rapid urbanization spaces. We investigated alternative supervised classification schemes combining the Radarsat-1 C-band with Landsat Enhanced Thematic Mapper Plus (ETM+) bands to estimate land cover distributions and assess the quality of results with field data. Then, we set forth and evaluated a methodology which applies data fusion of selected Landsat ETM+ bands and the C-radar band. The separation and similarities for vegetated and non-vegetated cover types depends on whether the selected agricultural crops are annual or perennial, and on whether there are bare soils present. This knowledge for the particular study area influenced the selection of dates for image take and analysis. Partial fused and non-fused land-cover maps were assessed for accuracy and were combined to obtain a final map. The results demonstrate that the combined utilization of optical and radar imagery yields useful land cover information and improved classification accuracy over those obtained using either type of image on its own.

Prediction of Corn Yield in Mexico Using Vegetation Indices from NOAA-AVHRR Satellite Images and Degree-days

Abstract The heterogeneity of corn‐growing conditions in Mexico makes accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of corn required to be imported to meet the expected domestic shortfall. In this paper, therefore, a methodology for the estimation of corn yield ahead of harvest time is developed specifically for the corn‐growing conditions particular to Mexico. The method is based on the multi‐temporal analysis of NOAA‐AVHRR satellite images, and uses normalized difference vegetation indices (NDVIs), leaf area indices (LAIs), and degree‐days (DDs) to predict corn occurrence and yield. Results of the application of the methodology to successfully identify sites with corn, and to predict corn yield, for a 15,840 ha area of land in the State of Guanajuato, are presented and discussed.

Potential regionalization for amaranth, sorghum and sunflower in Guanajuato, Mexico

Abstract Please click here to download the map associated with this article. Non-irrigation agriculture involves risks and uncertainty. Farmers of non-irrigated fields consistently get lower yields per surface unit along with the highest loss indices. Losses are chiefly caused by the environment, largely as a result of the steady climate variations that have taken place over the latest years. It is overly important to determine the current behavior of the elements of climate, as well as their spatial and temporal distribution, in order to adapt farming techniques tailored to these changes. This work consisted in integrating a set of thermal and pluvial indicators which directly influence agricultural activities. The methodology was applied to Guanajuatos northern region (13,794 km2) at a scale of 1:250,000. This represents a database from which it is possible to suggest the introduction of alternative crops: amaranth, sorghum and forager sunflower.

Geographic metadata and ontology based satellite image management

Geospatial database items originate from the analysis of images and from the manipulation of geographic data. The corresponding datasets are described via diverse structures of metadata. GeoBase L9 is a project whose aim is to build a geospatial database to support geomatics research in agricultural and natural resource management. The first objective, to support basic browsing access to datasets, has been attained in a pilot version. This paper reports on the stage-based outlook that we have adopted towards building a semantic query facility as a medium-term objective. In particular, we examine the representation of processes applied to satellite images with respect to information items that are contained in the metadata lineage section. This query facility supports a research unit that is collectively developing and using geospatial datasets. The development of such datasets will enhance both the sharing and reuse of data by users. Further into the future, the geodatabases will able to be opened to semantic web browsers by incorporating meaning in the metadata.

Methodology to generate yield maps of maize crops

In central Mexico, specifically in the State of Mexico maize is cultivated under different technological regimes ranging from traditional rain water dependency and native seeds producing yields of under 1.0 ton/ha, up to irrigation and improved seeds regimes with yields above 12.0 ton/ha. The average state yield harvested area for this crop in the past 10 years has been 549, 000 ha with an average yield of 3.22 tons/ha and an overall average of 1.8 million tons of grain. To obtain the cultivated area, SPOT panchromatic and multispectral satellite images were processed over the growth and development stages of the plants. In the cultivated areas sample yield data were collected, geo-referencing the collection sites with geographic information management products. These data were spatially represented via interpolation. The final products were yield maps at different cartographic scales.

El cambio climático afecta el número de horas dentro de los rangos térmicos del chile (Capsicum annuum L.) en el Norte-Centro de México

In Mexico, chili cultivation has a long cultural tradition and is one of the main centers of origin and domestication. An average of 97,306 ha is planted annually in the north of the country. However, the productivity of the chili crop is very erratic due to biotic and abiotic factors that occur during its development. Extreme temperatures and water availability are two of the dominant abiotic stressors. The objective of this work was to know the effect of global warming, on the thermal range of the chili crop. A historical analysis was performed on the variation of the number of hours with temperature inside and outside the thermal range of chili, and a similar analysis in the climatic scenarios 2050 and 2070 in the RCPs 4.5 and 8.5. Both in the historical analysis and in the climatologies we found positive and negative effects of global warming. Global warming will favor chilli cultivation with a surface increase (22.6%) with hourly temperature within development thresholds (15 to 32 °C) and a surface increase (15.8%) within the optimum temperature range (18 to 28 °C). Chili cultivation will be limited by the increase (20.8%) in the surface area with hourly temperature above the upper threshold (32 °C) and an increase (18.5%) in the area with a night time temperature above the upper threshold of night temperature (18 °C) in RCP 4.5 and in climatology 2050. High temperature stress will have an eff ect on the yield decrease due to the negative eff ect on pollination and fruit binding processes.

A Software Process Framework for Guiding the Construction Specification of Geospatial Databases

Geospatial databases are in increasing demand for serving a variety of applications and user expectations. The general requirements for building such databases are understood, but technological tools and approaches continue to be developed because of the specific requirements of operating environments. However, in many situations, the implementation of domain-specific databases still presents major challenges. In interdisciplinary research environments where groups work on a common geographical region, several issues arise from the use of diverse sources of geospatial data. Such issues need the input of software specialists, and researchers usually lack the time and/or expertise required to become substantially involved in informatics projects. Groups investigating natural and social processes related to agriculture, ecology or natural resources management need to solve data incompatibilities in order to share and reuse information. Accordingly, this paper presents MP-Geo, a framework for a software process specification for guiding the construction of a specialized geospatial database and its management system. The salient characteristic of the framework is its emphasis on and inclusion of software engineering good practices and standards. The framework emerges from the real needs of academics and students working in the field of agricultural and natural resource management. The framework can be used to build useful geospatial databases in diverse domains.

Potential inland aquaculture sites using high resolution satellite images in a region of high marginalization

Aquaculture has an important potential in the alleviation of food shortfalls and poverty in many countries. The effective practice of aquaculture is based on several requirements, among which the location of appropriate sites and information which allows merging with activities of local populations are important. This paper reports on the location of appropriate water bodies for inland aquaculture in Mexico based on high resolution satellite images processing. The approach is illustrated for the state of Guerrero. The information is presented in thematic maps of water bodies suitable for aquaculture and the methodology employed is amenable to application in other regions.

Land use/cover in the compact agricultural areas of Mexico

Compact agricultural areas in Mexico have been identified, which are monitored as to their behavior concerning production and rural productivity in a network of Agrotech Observatories (AOTs). An AOT is a compact agricultural area representative of agro-ecological, technological and social conditions in the country. A multidisciplinary team of scientists and researchers analyze and define the best production options for the different types of producers in these areas. To optimize production and agricultural productivity in compact areas, a multidisciplinary and holistic approach with four lines of activity (agro-ecological, technological, economic, and social), and ten actions are used. One of them is oriented towards determination of the land use/cover over sixteen compact agricultural areas in Mexico. Currently, it is important to have updated and accurate information to support actions and programs of federal, state and local government for farmers, particularly in compact areas with high agricultural production potential.

Forest inventory applications using optical and RADARSAT-2 images in mexico

There is a need in Mexico for accurate and up to date forest inventories due to concerns related with sustainable development programs. Forest inventories in the past have been incomplete and are not useful at regional levels. The national forest inventory is scheduled to be updated soon -optical remote sensing techniques and traditional field surveying are envisaged. There are no operational applications of radar remote sensing for forest management within government agencies or academic institutions in the country. Forest land cover is dynamical due to urban area growth, illegal logging and forest clearing for agricultural purposes in many regions. A previous land cover project combined Landsat-ETM and RADARSAT-1 imagery in Central Mexico, where forest areas are frequently foggy. A current project involves testing the potential benefits of combining polarimetric radar and optical data for forest applications. RADARSAT-2 imagery will be used as part of the Science and Operational Applications Research Program. The project aims to evaluate combined optical/radar approaches to improve forest inventory at regional scales. As a first step the total forested area is determined from optical SPOT 5 images. We show preliminary results from the optical data which are being validated on the field. As RADARSAT-2 imagery become available, polarization signatures for forest parameters will be obtained. Further work will evaluate the complementarities with optical signatures in determining forest species.