A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCL

In this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

Image classification accuracy from LANDSAT data for tropical rain forest in Sumatra: An evaluation of ERDAS imagine software

Abstract not available

Forest tree species diversity: Assessment, analysis and interpretation in the presence of non-identification and non-observance

Abstract not available

SOCIAL STRUCTURE OF ASIAN ELEPHANTS (ELEPHAS MAXIMUS) IN SRI LANKA

Asian elephants (Elephas maximus) are critically endangered and live in fragmented populations spread across 13 countries. Yet in comparison to the African savannah elephant (Loxodonta africana), relatively little is known about the social structure of wild Asian elephants because the species is mostly found in low visibility habitat. A better understanding of Asian elephant social structure is critical to mitigate human-elephant conflicts that arise due to increasing human encroachments into elephant habitats. In this dissertation, I examined the social structure of Asian elephants at three sites: Yala, Udawalawe, and Minneriya National Parks in Sri Lanka, where the presence of large open areas and high elephant densities are conducive to behavioral observations. First, I found that the size of groups observed at georeferenced locations was affected by forage availability and distance to water, and the effects of these environmental factors on group size depended on site. Second, I discovered that while populations at different sites differed in the prevalence of weak associations among individuals, a core social structure of individuals sharing strong bonds and organized into highly independent clusters was present across sites. Finally, I showed that the core social structure preserved across sites was typically composed of adult females associating with each other and with other age-sex classes. In addition, I showed that females are social at all life stages, whereas males gradually transition from living in a group to a more solitary lifestyle. Taking into consideration these elements of Asian elephant social structure will help conservation biologists develop effective management strategies that account for both human needs and the socio-ecology of the elephants.

Efeitos diretos e indiretos de parâmetros dos semivariogramas referentes aos atributos químicos do solo sobre a produtividade de grãos de soja

In order to add value to soybens crops, and hence the marketing, medium and large producers have been using precision agriculture techniques (PA), as the Remote Sensing, Geographic Information Systems (GIS) and positioning satellite, to assist the management of crops. Thus, given the economic relevance of that culture to the southwest of Paraná State and Brazil, scientific studies to increase their productivity and profitability are of main importance. The objective of this study was to evaluate the correlation between the chemical soil properties and soybean yield for each estimated parameter of semivariogram (range, nugget and level effect), and the deployment of these correlations in direct and indirect effects, aiming to improve the mapping process of spatial variability of soil chemical properties for use in PA. The hypothesis is that not all attributes of soil used to estimate the semivariogram parameters has a direct effect on productivity, and that even in groups of plants within a larger area it is possible to estimate the parameters of the semivariograms. The experiment was conducted in a commercial area of 19.7 ha, located in the city of Pato Branco - PR, central geographic coordinates 26º 11 '35 "South, 52 43' 05" West longitude, and average altitude of 780 m. The area is planted with soybeans for over 30 years, currently being adopted to cultivate Brasmax Target RR - Don Mario 5.9i, with row spacing of 0.50 m and 13 plants m-1, totaling 260,000 plants ha-1. For georeferencing of the area of study and sampling points was used a couple of topographic ProMarkTM3 receptors, making a relative positioning to obtain the georeferenced coordinates. To collect data (chemical analyzes of soil and crop yield) were sampled 10 blocks in the experimental area, each with an area of 20 m2 (20 meters long x 1 meter wide) containing two spaced adjacent rows of 0.5 m. Each block was divided into 20 portions of 1 m2, and from each were collected four subsamples at a distance of 0.5 m in relation to the lines of blocks, making up a sample depth for 0-10 cm a sample to 10-20 cm for each plot, totaling 200 samples for each depth. The soybean crop was performed on the blocks depending on maturity, and in each block was considered a bundle at each meter. In the data analysis, it was performed a diagnosis of multicollinearity, and subsequently a path analysis of the main variables according to the explanatory variables (range of chemical attributes: pH, K, P, Ca, etc.). The results obtained by the path analysis of the parameters of the semivariogram of soil chemical properties, indicated that only the Fe, Mg, Mn, organic matter (OM), P and Saturation by bases (SB) exerted direct and indirect effects on soybean productivity, although they have not presented spatial variability, indicating that the distribution of blocks in the area was unable to identify the spatial dependence of these elements, making it impossible to draw up maps of the chemical attributes for use in PA.

Improved models for estimating temporal changes in carbon sequestration in above-ground biomass of mixed-species environmental plantings

Plantings of mixed native species (termed 'environmental plantings') are increasingly being established for carbon sequestration whilst providing additional environmental benefits such as biodiversity and water quality. In Australia, they are currently one of the most common forms of reforestation. Investment in establishing and maintaining such plantings relies on having a cost-effective modelling approach to providing unbiased estimates of biomass production and carbon sequestration rates. In Australia, the Full Carbon Accounting Model (FullCAM) is used for both national greenhouse gas accounting and project-scale sequestration activities. Prior to undertaking the work presented here, the FullCAM tree growth curve was not calibrated specifically for environmental plantings and generally under-estimated their biomass. Here we collected and analysed above-ground biomass data from 605 mixed-species environmental plantings, and tested the effects of several planting characteristics on growth rates. Plantings were then categorised based on significant differences in growth rates. Growth of plantings differed between temperate and tropical regions. Tropical plantings were relatively uniform in terms of planting methods and their growth was largely related to stand age, consistent with the un-calibrated growth curve. However, in temperate regions where plantings were more variable, key factors influencing growth were planting width, stand density and species-mix (proportion of individuals that were trees). These categories provided the basis for FullCAM calibration. Although the overall model efficiency was only 39-46%, there was nonetheless no significant bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average, but estimates at any particular location will be uncertain, with either under- or over-prediction possible. When compared with the un-calibrated yield curves, predictions using the new calibrations show that early growth is likely to be more rapid and total above-ground biomass may be higher for many plantings at maturity. This study has considerably improved understanding of the patterns of growth in different types of environmental plantings, and in modelling biomass accumulation in young (<25. years old) plantings. However, significant challenges remain to understand longer-term stand dynamics, particularly with temporal changes in stand density and species composition. © 2014.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

"Last-Mile" preparation for a potential disaster - Interdisciplinary approach towards tsunami early warning and an evacuation information system for the coastal city of Padang, Indonesia

Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of smallscale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socioeconomic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity. © Author(s) 2009.

Plant Migrations Impact on Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change

Forests have a prominent role in carbon storage and sequestration. Anthropogenic forcing has the potential to accelerate climate change and alter the distribution of forests. How forests redistribute spatially and temporally in response to climate change can alter their carbon sequestration potential. The driving question for this research was: How does plant migration from climate change impact vegetation distribution and carbon sequestration potential over continental scales? Large-scale simulation of the equilibrium response of vegetation and carbon from future climate change has shown relatively modest net gains in sequestration potential, but studies of the transient response has been limited to the sub-continent or landscape scale. The transient response depends on fine scale processes such as competition, disturbance, landscape characteristics, dispersal, and other factors, which makes it computational prohibitive at large domain sizes. To address this, this research used an advanced mechanistic model (Ecosystem Demography Model, ED) that is individually based, but pseudo-spatial, that reduces computational intensity while maintaining the fine scale processes that drive the transient response. First, the model was validated against remote sensing data for current plant functional type distribution in northern North America with a current climatology, and then a future climatology was used to predict the potential equilibrium redistribution of vegetation and carbon from future climate change. Next, to enable transient calculations, a method was developed to simulate the spatially explicit process of dispersal in pseudo-spatial modeling frameworks. Finally, the new dispersal sub-model was implemented in the mechanistic ecosystem model, and a model experimental design was designed and completed to estimate the transient response of vegetation and carbon to climate change. The potential equilibrium forest response to future climate change was found to be large, with large gross changes in distribution of plant functional types and comparatively smaller changes in net carbon sequestration potential for the region. However, the transient response was found to be on the order of centuries, and to depend strongly on disturbance rates and dispersal distances. Future work should explore the impact of species-specific disturbance and dispersal rates, landscape fragmentation, and other processes that influence migration rates and have been simulated at the sub-continent scale, but now at continental scales, and explore a range of alternative future climate scenarios as they continue to be developed.

A GEOBIA methodology for fragmented agricultural landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

Avaliação ambiental de manguezais adjacentes aos campos petrolíferos de Macau e Serra (RN), como subsídio às medidas mitigadoras ao processo erosivo

Among the various effects caused by the climate change and human intervention, the mangrove ecosystem changes through of the years has been worth mentioning, which hasn t known which are the pros and cons for the adjacent coastal and estuarine environments yet. It happens due to the present dynamism in these areas, besides of the difficult understanding of the processes associated with evolution. This study aimed to environmentally evaluate adjacent mangroves from the Macau and Serra oil fields, located on Rio Grande do Norte northern coast, to support the mitigating actions related to the containment of the erosive process, as well as, according to the principles of the Clean Development Mechanism (CDM), to assess the amount of atmospheric carbon sequestered by the studied ecosystem. An inventory was conducted through mangrouve mapping which has supplied this research, especially regarding to the structural characterization of mangrove areas. To understand the local mangrove behavior in a greater level detail, techniques of remote sensing, GIS and GPS were used to make an analogy between the current and past states of the mangrove studied, allowing to make anticipated projections for the future impacts or changes in that region. This study combined data from multispectral LANDSAT 5 TM, Landsat 7 ETM+ with radar microwave data from SAR RADARSAT-1, which increased the interpretation capacity of the data from optical sensor systems. The interpretations have been supported by the data field, representing a better and innovative methodology for the environmental and taxonomic characterization of mangrove forests considered. The results reveal that mangroves of the Ponta do Tubarão Sustainable Development Reserve are biologically representative areas and providing a variety of benefits, especially for local communities, constituting the priority sites for actions development aimed at conservation. They also have been showing the necessity to make mitigating measures in order to recover degraded areas through reforestation or creating new areas of mangrove, as currently 7.1% of the mangrove forests studied are dead or in an advanced state of decomposition. The amount of atmospheric carbon sequestered proved very significant when analyzed for the whole area, which is able to sequester atmospheric 4,294,458 Ton CO2 per year

Typologies and spatialization of agricultural production systems in Rondônia, Brazil: linking land use, socioeconomics and territorial configuration.

The current Amazon landscape consists of heterogeneous mosaics formed by interactions between the original forest and productive activities. Recognizing and quantifying the characteristics of these landscapes is essential for understanding agricultural production chains, assessing the impact of policies, and in planning future actions. Our main objective was to construct the regionalization of agricultural production for Rondônia State (Brazilian Amazon) at the municipal level. We adopted a decision tree approach, using land use maps derived from remote sensing data (PRODES and TerraClass) combined with socioeconomic data. The decision trees allowed us to allocate municipalities to one of five agricultural production systems: (i) coexistence of livestock production and intensive agriculture; (ii) semi-intensive beef and milk production; (iii) semi-intensive beef production; (iv) intensive beef and milk production, and; (v) intensive beef production. These production systems are, respectively, linked to mechanized agriculture (i), traditional cattle farming with low management, with (ii) or without (iii) a significant presence of dairy farming, and to more intensive livestock farming with (iv) or without (v) a significant presence of dairy farming. The municipalities and associated production systems were then characterized using a wide variety of quantitative metrics grouped into four dimensions: (i) agricultural production; (ii) economics; (iii) territorial configuration, and; (iv) social characteristics. We found that production systems linked to mechanized agriculture predominate in the south of the state, while intensive farming is mainly found in the center of the state. Semi-intensive livestock farming is mainly located close to the southwest frontier and in the north of the state, where human occupation of the territory is not fully consolidated. This distributional pattern reflects the origins of the agricultural production system of Rondônia. Moreover, the characterization of the production systems provides insights into the pattern of occupation of the Amazon and the socioeconomic consequences of continuing agricultural expansion.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

Current procedures for flood risk estimation assume flood distributions are stationary over time, meaning annual maximum flood (AMF) series are not affected by climatic variation, land use/land cover (LULC) change, or management practices. Thus, changes in LULC and climate are generally not accounted for in policy and design related to flood risk/control, and historical flood events are deemed representative of future flood risk. These assumptions need to be re-evaluated, however, as climate change and anthropogenic activities have been observed to have large impacts on flood risk in many areas. In particular, understanding the effects of LULC change is essential to the study and understanding of global environmental change and the consequent hydrologic responses. The research presented herein provides possible causation for observed nonstationarity in AMF series with respect to changes in LULC, as well as a means to assess the degree to which future LULC change will impact flood risk. Four watersheds in the Midwest, Northeastern, and Central United States were studied to determine flood risk associated with historical and future projected LULC change. Historical single framed aerial images dating back to the mid-1950s were used along with Geographic Information Systems (GIS) and remote sensing models (SPRING and ERDAS) to create historical land use maps. The Forecasting Scenarios of Future Land Use Change (FORE-SCE) model was applied to generate future LULC maps annually from 2006 to 2100 for the conterminous U.S. based on the four IPCC-SRES future emission scenario conditions. These land use maps were input into previously calibrated Soil and Water Assessment Tool (SWAT) models for two case study watersheds. In order to isolate effects of LULC change, the only variable parameter was the Runoff Curve Number associated with the land use layer. All simulations were run with daily climate data from 1978-1999, consistent with the 'base' model which employed the 1992 NLCD to represent 'current' conditions. Output daily maximum flows were converted to instantaneous AMF series and were subsequently modeled using a Log-Pearson Type 3 (LP3) distribution to evaluate flood risk. Analysis of the progression of LULC change over the historic period and associated SWAT outputs revealed that AMF magnitudes tend to increase over time in response to increasing degrees of urbanization. This is consistent with positive trends in the AMF series identified in previous studies, although there are difficulties identifying correlations between LULC change and identified change points due to large time gaps in the generated historical LULC maps, mainly caused by unavailability of sufficient quality historic aerial imagery. Similarly, increases in the mean and median AMF magnitude were observed in response to future LULC change projections, with the tails of the distributions remaining reasonably constant. FORE-SCE scenario A2 was found to have the most dramatic impact on AMF series, consistent with more extreme projections of population growth, demands for growing energy sources, agricultural land, and urban expansion, while AMF outputs based on scenario B2 showed little changes for the future as the focus is on environmental conservation and regional solutions to environmental issues.

Volcanic processes and human exposure as elements to build a risk model for Volcan de Fuego, Guatemala

The activity of Fuego volcano during the 1999 - 2013 eruptive episode is studied through field, remote sensing and observatory records. Mapping of the deposits allows quantifying the erupted volumes and areas affected by the largest eruptions during this period. A wide range of volcanic processes results in a diversity of products and associated deposits, including minor airfall tephra, rockfall avalanches, lava flows, and pyroclastic flows. The activity can be characterized by long term, low level background activity, and sporadic larger explosive eruptions. Although the background activity erupts lava and ash at a low rate (~ 0.1 m3/s), the persistence of such activity over time results in a significant contribution (~ 30%) to the eruption budget during the studied period. Larger eruptions produced the majority of the volume of products during the studied period, mainly during three large events (May 21, 1999, June 29, 2003, and September 13, 2012), mostly in the form of pyroclastic flows. A total volume of ~ 1.4 x 108 m3 was estimated from the mapped deposits and the estimated background eruption rate. Posterior remobilization of pyroclastic flow material by stream erosion in the highly confined Barranca channels leads to lahar generation, either by normal rainfall, or by extreme rainfall events. A reassessment of the types of products and volumes erupted during the decade of 1970's allows comparing the activity happening since 1999 with the older activity, and suggests that many of the eruptive phenomena at Fuego may have similar mechanisms, despite the differences in scale between. The deposits of large pyroclastic flows erupted during the 1970's are remarkably similar in appearance to the deposit of pyroclastic flows from the 1999 - 2013 period, despite their much larger volume; this is also the case for prehistoric eruptions. Radiocarbon dating of pyroclastic flow deposits suggests that Fuego has produced large eruptions many times during the last ~ 2 ka, including larger eruptions during the last 500 years, which has important hazard implications. A survey was conducted among the local residents living near to the volcano, about their expectations of possible future crises. The results show that people are aware of the risk they could face in case of a large eruption, and therefore they are willing to evacuate in such case. However, their decision to evacuate may also be influenced by the conditions in which the evacuation could take place. If the evacuation represents a potential loss of their livelihood or property they will be more hesitant to leave their villages during a large eruption. The prospect of facing hardship conditions during the evacuation and in the shelters may further cause reluctance to evacuate. A short discussion on some of the issues regarding risk assessment and management through an early warning system is presented in the last chapter.