Monitoring the condition of savanna riparian zones in Northern Australia using high spatial resolution imagery and a field based rapid assessment method

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.

Open-source mapping and services for Web-based land-cover validation

Monitoring land-cover changes on sites of conservation importance allows environmental problems to be detected, solutions to be developed and the effectiveness of actions to be assessed. However, the remoteness of many sites or a lack of resources means these data are frequently not available. Remote sensing may provide a solution, but large-scale mapping and change detection may not be appropriate, necessitating site-level assessments. These need to be easy to undertake, rapid and cheap. We present an example of a Web-based solution based on free and open-source software and standards (including PostGIS, OpenLayers, Web Map Services, Web Feature Services and GeoServer) to support assessments of land-cover change (and validation of global land-cover maps). Authorised users are provided with means to assess land-cover visually and may optionally provide uncertainty information at various levels: from a general rating of their confidence in an assessment to a quantification of the proportions of land-cover types within a reference area. Versions of this tool have been developed for the TREES-3 initiative (Simonetti, Beuchle and Eva, 2011). This monitors tropical land-cover change through ground-truthing at latitude / longitude degree confluence points, and for monitoring of change within and around Important Bird Areas (IBAs) by Birdlife International and the Royal Society for the Protection of Birds (RSPB). In this paper we present results from the second of these applications. We also present further details on the potential use of the land-cover change assessment tool on sites of recognised conservation importance, in combination with NDVI and other time series data from the eStation (a system for receiving, processing and disseminating environmental data). We show how the tool can be used to increase the usability of earth observation data by local stakeholders and experts, and assist in evaluating the impact of protection regimes on land-cover change.

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.

Impact of protected forests on rural household fuel tree planting and deforestation: Chiro District, Ethiopia

This study aims at exploring the potential impact of forest protection intervention on rural households’ private fuel tree planting in Chiro district of eastern Ethiopia. The study results revealed a robust and significant positive impact of the intervention on farmers’ decisions to produce private household energy by growing fuel trees on their farm. As participation in private fuel tree planting is not random, the study confronts a methodological issue in investigating the causal effect of forest protection intervention on rural farm households’ private fuel tree planting through non-parametric propensity score matching (PSM) method. The protection intervention on average has increased fuel tree planting by 503 (580.6%) compared to open access areas and indirectly contributed to slowing down the loss of biodiversity in the area. Land cover/use is a dynamic phenomenon that changes with time and space due to anthropogenic pressure and development. Forest cover and land use changes in Chiro District, Ethiopia over a period of 40 years was studied using remotely sensed data. Multi temporal satellite data of Landsat was used to map and monitor forest cover and land use changes occurred during three point of time of 1972,1986 and 2012. A pixel base supervised image classification was used to map land use land cover classes for maps of both time set. The result of change detection analysis revealed that the area has shown a remarkable land cover/land use changes in general and forest cover change in particular. Specifically, the dense forest cover land declined from 235 ha in 1972 to 51 ha in 1986. However, government interventions in forest protection in 1989 have slowed down the drastic change of dense forest cover loss around the protected area through reclaiming 1,300 hectares of deforested land through reforestation program up to 2012.

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

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.

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Análise das mudanças espaciais e temporais nos padrões de paisagem na região do oeste da Bahia sobre a formação do grupo urucuia (1988-2011) e suas implicações para a conservação do Cerrado

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós Graduação em Geografia, 2015.

Utilização de técnica de detecção de mudança aplicada ao sensoriamento espectral para rádios cognitivos

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Environmental quality of life index incorporationg MCA & GIS : case study Lisbon Metropolitan Area, Portugal

The study of Quality of Life (Qol) has been conducted on various scales throughout the years with focus on assessing overall quality of living amongst citizens. The main focus in these studies have been on economic factors, with the purpose of creating a Quality of Life Index (QLI).When it comes down to narrowing the focus to the environment and factors like Urban Green Spaces (UGS) and air quality the topic gets more focused on pointing out how each alternative meets this certain criteria. With the benefits of UGS and a healthy environment in focus a new Environmental Quality of Life Index (EQLI) will be proposed by incorporating Multi Criteria Analysis (MCA) and Geographical Information Systems (GIS). Working with MCA on complex environmental problems and incorporating it with GIS is a challenging but rewarding task, and has proven to be an efficient approach among environmental scientists. Background information on three MCA methods will be shown: Analytical Hierarchy Process (AHP), Regime Analysis and PROMETHEE. A survey based on a previous study conducted on the status of UGS within European cities was sent to 18 municipalities in the study area. The survey consists of evaluating the current status of UGS as well as planning and management of UGS with in municipalities for the purpose of getting criteria material for the selected MCA method. The current situation of UGS is assessed with use of GIS software and change detection is done on a 10 year period using NDVI index for comparison purposes to one of the criteria in the MCA. To add to the criteria, interpolation of nitrogen dioxide levels was performed with ordinary kriging and the results transformed into indicator values. The final outcome is an EQLI map with indicators of environmentally attractive municipalities with ranking based on predefinedMCA criteria using PROMETHEE I pairwise comparison and PROMETHEE II complete ranking of alternatives. The proposed methodology is applied to Lisbon’s Metropolitan Area, Portugal.

Landscape Patterns of Permafrost Disturbance and Degradation in the Canadian High Arctic

Intensification of permafrost disturbances such as active layer detachments (ALDs) and retrogressive thaw slumps (RTS) have been observed across the circumpolar Arctic. These features are indicators of unstable conditions stemming from recent climate warming and permafrost degradation. In order to understand the processes interacting to give rise to these features, a multidisciplinary approach is required; i.e., interactions between geomorphology, hydrology, vegetation and ground thermal conditions. The goal of this research is to detect and map permafrost disturbance, predict landscape controls over disturbance and determine approaches for monitoring disturbance, all with the goal of contributing to the mitigation of permafrost hazards. Permafrost disturbance inventories were created by applying semi-automatic change detection techniques to IKONOS satellite imagery collected at the Cape Bounty Arctic Watershed Observatory (CBAWO). These methods provide a means to estimate the spatial distribution of permafrost disturbances for a given area for use as an input in susceptibility modelling. Permafrost disturbance susceptibility models were then developed using generalized additive and generalized linear models (GAM, GLM) fitted to disturbed and undisturbed locations and relevant GIS-derived predictor variables (slope, potential solar radiation, elevation). These models successfully delineated areas across the landscape that were susceptible to disturbances locally and regionally when transferred to an independent validation location. Permafrost disturbance susceptibility models are a first-order assessment of landscape susceptibility and are promising for designing land management strategies for remote permafrost regions. Additionally, geomorphic patterns associated with higher susceptibility provide important knowledge about processes associated with the initiation of disturbances. Permafrost degradation was analyzed at the CBAWO using differential interferometric synthetic aperture radar (DInSAR). Active-layer dynamics were interpreted using inter-seasonal and intra-seasonal displacement measurements and highlight the importance of hydroclimatic factors on active layer change. Collectively, these research approaches contribute to permafrost monitoring and the assessment of landscape-scale vulnerability in order to develop permafrost disturbance mitigation strategies.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Detection of Change in Fluorescence Between Reactive Cyan and the Yellow Fluorophores Usinga-SiC:H Multilayer Transducers

Optical colour sensors based on multilayered a-SiC:H heterostructures can act as voltage controlled optical filters in the visible range. In this article we investigate the application of these structures for Fluorescence Resonance Energy Transfer (FRET) detection, The characteristics of a-SiC:H multilayered structure are studied both theoretically and experimentally in several wavelengths corresponding to different fluorophores. The tunable optical p-i'(a-SiC:H)-n/p-i(a-Si:H)-n heterostructures were produced by PECVD and tested for a proper fine tuning in the violet, cyan and yellow wavelengths. The devices were characterized through transmittance and spectral response measurements, under different electrical bias and frequencies. Violet, cyan and yellow signals were applied in simultaneous and results have shown that they can be recovered under suitable applied bias. A theoretical analysis supported by numerical simulation is presented.

Detection of Change in Fluorescence Between Reactive Cyan and the Yellow Fluorophores Using a-SiC:H Multilayer Transducers

The transducer consists of a semiconductor device based on two stacked -i-n heterostructures that were designed to detect the emissions of the fluorescence resonance energy transfer between fluorophores in the cyan (470 nm) and yellow (588 nm) range of the spectrum. This research represents a preliminary study on the use of such wavelength-sensitive devices as photodetectors for this kind of application. The device was characterized through optoelectronic measurements concerning spectral response measurements under different electrical and optical biasing conditions. To simulate the fluorescence resonance energy transfer (FRET) pairs, a chromatic time-dependent combination of cyan and yellow wavelengths was applied to the device. The generated photocurrent was measured under reverse and forward bias to read out the output photocurrent signal. A different wavelength-biasing light was also superimposed. Results show that under reverse bias, the photocurrent signal presents four separate levels, each one assigned to the different wavelength combinations of the FRET pairs. If a blue background is superimposed, the yellow channel is enhanced and the cyan suppressed, while under red irradiation, the opposite behavior occurs. So, under suitable biasing light, the transducer is able to detect separately the cyan and yellow fluorescence pairs. An electrical model, supported by a numerical simulation, supports the transduction mechanism of the device.