Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition

tWater use control methods and water resources planning are of high priority. In irrigated agriculture, theright way to save water is to increase water use efficiency through better management. The present workvalidates procedures and methodologies using remote sensing to determine the water availability in thesoil at each moment, giving the opportunity for the application of the water depth strictly necessaryto optimise crop growth (optimum irrigation timing and irrigation amount). The analysis is applied tothe Irrigation District of Divor, Évora, using 7 experimental plots, which are areas irrigated by centre-pivot systems, cultivated to maize. Data were determined from images of the cultivated surface obtainedby satellite and integrated with atmosphere and crop parameters to calculate biophysical indicatorsand indices of water stress in the vegetation—Normalized Difference Vegetation Index (NDVI), Kc, andKcb. Therefore, evapotranspiration (ETc) was estimated and used to calculate crop water requirement,together with the opportunity and the amount of irrigation water to allocate. Although remote sensingdata available from satellite imagery presented some practical constraints, the study could contribute tothe validation of a new methodology that can be used for irrigation management of a large irrigated area,easier and at lower costs than the traditional FAO recommended crop coefficients method. The remotesensing based methodology can also contribute to significant saves of irrigation water.

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.

Social construction of Geographic Information Systems. (Volumes I and II)

Geographic Information Systems (GIS) is an emerging information technology (IT) which promises to have large scale influences in how spatially distributed resources are managed. It has had applications in the management of issues as diverse as recovering from the disaster of Hurricane Andrew to aiding military operations in Desert Storm. Implementation of GIS systems is an important issue because there are high cost and time involvement in setting them up. An important component of the implementation problem is the "meaning" different groups of people who are influencing the implementation give to the technology. The research was based on the theory of (theoretical stance to the problem was based on the) "Social Construction of Knowledge" systems which assumes knowledge systems are subject to sociological analysis both in usage and in content. An interpretive research approach was adopted to inductively derive a model which explains how the "meanings" of a GIS are socially constructed. The research design entailed a comparative case analysis over two county sites which were using the same GIS for a variety of purposes. A total of 75 in-depth interviews were conducted to elicit interpretations of GIS. Results indicate that differences in how geographers and data-processors view the technology lead to different implementation patterns in the two sites.

Remote Sensing (LIDAR) for Management of Highway Assets for Safety, 2003

The main objective of this study was to utilize light detection and ranging (LIDAR) technology to obtain highway safety-related information. The safety needs of older drivers in terms of prolonged reaction times were taken into consideration. The tasks undertaken in this study were (1) identification of crashes that older drivers are more likely to be involved in, (2) identification of highway geometric features that are important in such crashes, (3) utilization of LIDAR data for obtaining information on the identified highway geometric features, and (4) assessment of the feasibility of using LIDAR data for such applications. A review of previous research indicated that older drivers have difficulty negotiating intersections, and it was recognized that intersection sight triangles were critical to safe intersection negotiation. LIDAR data were utilized to obtain information on potential sight distance obstructions at six selected intersections located on the Iowa Highway 1 corridor by conducting in-office line-of-sight analysis. Crash frequency, older driver involvement, and data availability were considerations in the selection of the six intersections. Results of the in-office analysis were then validated by visiting the intersections in the field. Sixty-six potential sight distance obstructions were identified by the line-of-sight analysis, out of which 62 (89.8%) were confirmed while four (5.8%) were not confirmed by the video. At least three (4.4%) potential sight distance obstructions were discovered in the video that were not detected by the line-of-sight analysis. The intersection with the highest crash frequency involving older drivers was correctly found to have obstructions located within the intersection sight triangles. Based on research results, it is concluded that LIDAR data can be utilized for identifying potential sight distance obstructions at intersections. The safety of older drivers can be enhanced by locating and rectifying intersections with obstructions in sight triangles.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Laser remote sensing applications (LiDAR) in forest hydrology and the management of river ecosystems.

La utilización de sensores láser desde plataformas aéreas (LiDAR) ofrece nuevas posibilidades en el cartografiado de sistemas fluviales, tanto en áreas densamente cubiertas por vegetación, como en aquellas que presentan una escasa cubierta. La información topográfica de alta resolución que se obtiene a partir de las medidas láser puede ser utilizada en el análisis y estimación de diversas variables hidrológicas, y en el estudio de diferentes componentes del medio fluvial. Entre éstas, cabe citar la vegetación riparia, la morfología fluvial, el régimen hidrológico o el grado de alteración de los ecosistemas debido a las presiones de origen antrópico. La gestión del medio fluvial puede ser mejorada en gran medida gracias a la precisión y fiabilidad de esta información. En muchas ocasiones, el escaso relieve de los valles fluviales y la densa cubierta vegetal que existe en ellos han dificultado la aplicación de otras técnicas de teledetección. Sin embargo, los datos obtenidos mediante altimetría láser son especialmente aconsejables para estos trabajos, mediante análisis numéricos o a través de la simple interpretación de las imágenes obtenidas. Este artículo muestra las posibilidades de uso de los datos LiDAR en hidrología forestal y en la gestión de zonas húmedas, a lo largo de tramos con condiciones climáticas bien diferenciadas. En todas ellas, se comparan los resultados obtenidos mediante la aplicación de distinto software, con el fin de mostrar la mejor metodología de tratamiento de la información láser. Asimismo, se muestra la diferencia con otras técnicas de teledetección, y se muestra la fácil integración de los datos LiDAR con otras herramientas y metodologías de estudio de las variables citadas.

The application of remote sensing to the management of urban wildlife habitats

The project set out with two main aims. The first aim was to determine whether large scale multispectral aerial photography could be used to successfully survey and monitor urban wildlife habitats. The second objective was to investigate whether this data source could be used to predict population numbers of selected species expected to be found in a particular habitat type. Panchromatic, colour and colour infra-red, 1:2500 scale aerial photographs, taken in 1981 and 1984, were used. For the orderly extraction of information from the imagery, an urban wildlife habitat classification was devised. This was based on classifications already in use in urban environments by the Nature Conservancy Council. Pilot tests identified that the colour infra-red imagery provided the most accurate results about urban wildlife habitats in the study area of the Blackbrook Valley, Dudley. Both the 1981 and 1984 colour infra-red photographs were analysed and information was obtained about the type, extent and distribution of habitats. In order to investigate whether large scale aerial photographs could be used to predict likely animal population numbers in urban environments, it was decided to limit the investigation to the possible prediction of bird population numbers in Saltwells Local Nature Reserve. A good deal of research has already been completed into the development of models to predict breeding bird population numbers in woodland habitats. These models were analysed to determine whether they could be used successfully with data extracted from the aerial photographs. The projects concluded that 1:2500 scale colour infra-red photographs can provide very useful and very detailed information about the wildlife habitats in an urban area. Such imagery can also provide habitat area data to be used with population predictive models of woodland breeding birds. Using the aerial photographs, further investigations into the relationship between area of habitat and the breeding of individual bird species were inconclusive and need further research.

Building a Rockfall Database Using Remote Sensing: Techniques for Hazard Management in Canadian Rail Corridors

The section of CN railway between Vancouver and Kamloops runs along the base of many hazardous slopes, including the White Canyon, which is located just outside the town of Lytton, BC. The slope has a history of frequent rockfall activity, which presents a hazard to the railway below. Rockfall inventories can be used to understand the frequency-magnitude relationship of events on hazardous slopes, however it can be difficult to consistently and accurately identify rockfall source zones and volumes on large slopes with frequent activity, leaving many inventories incomplete. We have studied this slope as a part of the Canadian Railway Ground Hazard Research Program and have collected remote sensing data, including terrestrial laser scanning (TLS), photographs, and photogrammetry data since 2012, and used change detection to identify rockfalls on the slope. The objective of this thesis is to use a subset of this data to understand how rockfalls identified from TLS data could be used to understand the frequency-magnitude relationship of rockfalls on the slope. This includes incorporating both new and existing methods to develop a semi-automated workflow to extract rockfall events from the TLS data. We show that these methods can be used to identify events as small as 0.01 m3 and that the duration between scans can have an effect on the frequency-magnitude relationship of the rockfalls. We also show that by incorporating photogrammetry data into our analysis, we can create a 3D geological model of the slope and use this to classify rockfalls by lithology, to further understand the rockfall failure patterns. When relating the rockfall activity to triggering factors, we found that the amount of precipitation occurring over the winter has an effect on the overall rockfall frequency for the remainder of the year. These results can provide the railways with a more complete inventory of events compared to records created through track inspection, or rockfall monitoring systems that are installed on the slope. In addition, we can use the database to understand the spatial and temporal distribution of events. The results can also be used as an input to rockfall modelling programs.