Water productivity assessment by using MODIS images and agrometeorological data in the Petrolina municipality, Brazil

The municipality of Petrolina, located in the semi-arid region of Brazil, is highlighted as an important agricultural growing region, however the irrigated areas have cleared natural vegetation inducing a loss of biodiversity. To analyze the contrast between these two ecosystems the large scale values of biomass production (BIO), evapotranspiration (ET) and water productivity (WP) were quantified. Monteithś equation was applied for estimating the absorbed photosynthetically active radiation (APAR), while the new SAFER (Simple Algorithm For Evapotranspiration Retrieving) algorithm was used to retrieve ET. The water productivity (WP) was analysed by the ratio of BIO by ET at monthly time scale with four bands of MODIS satellite images together with agrometeorological data for the year of 2011. The period with the highest water productivity values were from March to April in the rainy period for both irrigated and not irrigated conditions. However the largest ET rates were in November for irrigated crops and April for natural vegetation. More uniformity of the vegetation and water variables occurs in natural vegetation, evidenced by the lower values of standard deviation when comparing to irrigated crops, due to the different crop stages, cultural and irrigation managements. The models applied with MODIS satellite images on a large scale are considered to be suitable for water productivity assessments and for quantifying the effects of increasing irrigated areas over natural vegetation on regional water consumption in situations of quick changing land use pattern. © 2012 SPIE.

Large-Scale Water Productivity Assessments with MODIS Images in a Changing Semi-Arid Environment: A Brazilian Case Study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study.

Multi-Decadal Record of Ice Dynamics on Daugaard Jensen Gletscher, East Greenland, from Satellite Imagery and Terrestrial Measurements

The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained in approximately the same position over the past similar to 50 years. There is no evidence of a change in ice motion between 1968 and 2001, based on a comparison of velocities derived from terrestrial surveying and feature tracking using sequential satellite images. Estimates of flux near the entrance to the fjord vs snow accumulation in the interior catchment show that Daugaard Jensen Gletscher has a small negative mass balance. This result is consistent with other mass-balance estimates for the inland region of the glacier.

Table 1: Glacier measurements from air photos 1969 and landsat images 1972 and 1973

The extent of snow cover at the end of the ablation season on glaciers in the Tyrolean Alps in 1972 and 1973 was determined from Landsat-1 Multispectral Scanner (MSS) images. For snovv mapping the MSS-images with a ground resolution of 80 meters were enlarged to a scale of 1: 100.000 by photographic methods. Different appearance of snow cover in the 4 MSS-channels is discussed in connection with ground truth control. The accuracy of snow and ice mapping from Landsat images was checked on 15 glaciers with an area from 1 to 10 km2 by aerial photography and/or ground truth control. These comparisons imply the usefulness of Landsat images for snow mapping on glaciers of a few square kilometers. The altitude of the equilibrium line was determined from Landsat images for 53 glaciers in the Tyrolean Alps. The regional differences in the equilibrium line altitude correspond to the regional precipitation patterns. The equilibrium line was identical with the snow line at the end of the budget year 1971/1972; therefore it was possible to determine the equilibrium line from satellite images. For 1968/69 the equilibrium line was mapped from aerial photographs for several glaciers. In 1972/73 mass balance was strongly negative and the equilibrimn line was within the firn area of the glaciers. Therefore it was not possible to distinguish between accumulation and ablation areas from the Landsat images of September 1973; however, snow and ice areas could be olearly differentiated. The ratios of accumulation area 01' snow area to the total area of the glaciers were determineel from satellite images and aerial photography separately for aelvancing anel for retreating glaciers and were relateel to the mass balance. In the budget years 1968/69 and 1972/73 with negative mass balance the accumulation area ratios of the advancing glacien; were olearly different from the ratios of the retreating glaciers, in 1971/72 with positive 01' balanced mass budget the differences between advancing and retreating glaciers were not significant.

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT method

A new approach to change detection in multispectral images by means of ERGAS index

In this letter, we propose a novel method for unsupervised change detection (CD) in multitemporal Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) satellite images by using the relative dimensionless global error in synthesis index locally. In order to obtain the change image, the index is calculated around a pixel neighborhood (3x3 window) processing simultaneously all the spectral bands available. With the objective of finding the binary change masks, six thresholding methods are selected. A comparison between the proposed method and the change vector analysis method is reported. The accuracy CD showed in the experimental results demonstrates the effectiveness of the proposed method.

The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations

Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.

Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape

Overrecentdecades,remotesensinghasemergedasaneffectivetoolforimprov- ing agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi- resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchi- cal segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra- variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a sin- gle image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.

Superpixel-based roughness measure for multispectral satellite image segmentation

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.