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Introduction: Crops are vulnerable to precipitation and heat extremes during late spring through summer. Methods: We analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041-2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap. Results: Identified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type. Discussion: The information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels. [ABSTRACT FROM AUTHOR]

IntroductionCrops are vulnerable to precipitation and heat extremes during late spring through summer.MethodsWe analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041-2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap.ResultsIdentified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type.DiscussionThe information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. [ABSTRACT FROM AUTHOR]

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing.

Tracking atmospheric rivers (ARs) across their lifecycles is a field of recent interest with a multitude of emerging methodologies. The CONNected‐objECT (CONNECT) algorithm is adapted for the tracking of global midlatitude AR lifecycles and associated precipitation by implementing a seeded region growing segmentation algorithm, creating the AR‐CONNECT algorithm. To facilitate the permissiveness of the methodology, AR‐CONNECT is without hard‐coded geometric criteria yet is still shown to extract synoptic‐scale elongated objects >99.99% of the time. One of the consequences of the methodology is the ability to occasionally track atmospheric water vapor anomalies before evolving into AR geometries, effectively tracking AR genesis further back than other studies. With the aid of subdaily satellite‐derived rain data, we investigate the climatology, trends, and patterns of AR lifecycles from 1983–2016 and compare with other AR tracking studies. We find that AR frequency, genesis, and terminus locations are in generally good agreement with other AR tracking methodologies, though with key differences, and that AR frequencies in each hemisphere are determined by the number of AR hotspots. Furthermore, we uncover evidence that certain AR characteristics, such as frequency, areal extent, and duration, show evidence of increasing trends. Midlatitude precipitation uncovered by AR‐CONNECT shows contributions up to 50% over land and 65% over the ocean. Trend analysis of AR precipitation shows an increase in precipitation associated with ARs propagated by the Southern Jet Stream and ARs that traverse over the Sahara Desert, among others, but is determined not to be a driver of changes in global precipitation. Key Points: A region growing segmentation technique is used to track atmospheric rivers lifecycles by their higher‐intensity coresThe areal extent and duration of atmospheric river lifecycle are observed to be increasing over the study period of 1983–2016Precipitation from atmospheric rivers constitutes up to 50%/65% of the rainfall over the land/oceans and is shown to be increasing globally [ABSTRACT FROM AUTHOR]

Software reuse--the application of existing software artifacts in the development of a new system--has been claimed to dramatically improve system development productivity and quality. These claims have been particularly pronounced with respect to the reuse of object-oriented (OO) software artifacts. However, the empirical evaluations of these claims are relatively sparse and often inconsistent. This paper begins to address the gap in the literature. A verbal-protocol study was conducted in which analysts created a model for a problem (the target) and were given an example problem and solution (the source) to reuse. The results show little support for reuse in OO analysis. First, reuse had no effect on the quality of the OO analysis models. Subjects given a highly reusable example produced solutions that were no better than those of subjects in the control group. Second, the degree of similarity between the source and target problems did have an effect on the reuse process, although it did not impact the reuse outcome. Subjects given the example with the most similarity to the target problem quickly recognized the reuse potential, attempted a fair amount of reuse, but made several errors stemming from lazy copying. Subjects given an example with a lesser (but still significant) degree of similarity were often unable to recognize the reuse potential, and thus engaged in less rescue activity. Thus, the characteristics of the source-target comparison that facilitate noticing the reuse potential of the source do not necessarily help in applying the source solution to the target problem. These results suggest that the claims associated with reuse should be treated with a healthy dose of skepticism. [ABSTRACT FROM AUTHOR]

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Object-oriented analysis (OOA) has become a popular method for analyzing system requirements. Unfortunately, however, none of the current versions of OOA has included a validation technique tailored to the object-oriented approach. Most, instead, merely recommend document reviews without specifying the kinds of problems to look for. This paper explores the question by applying a natural language parser to a requirements document, extracting candidate objects, methods, and associations, composing them into an object model diagram, and then comparing the results with those determined by manual OOA. To do this, we have adapted an automated natural language parser and used it to examine several high-level system descriptions. The results indicate that, with a modest amount of effort, the technique can give valuable feedback to the analyst. [ABSTRACT FROM AUTHOR]

Results of theoretical investigations are presented, forming of class structure of electromechanics transformers of energy is shown, the generation of the field models on the basis of principles of the object-oriented analysis is considered.

A new approach, based on object-oriented programming, for search and classification of protein patterns in a protein database is developed. Aiming at an improvement of the analysis of protein sequences and structures, certain basic patterns - such as motifs and fingerprints - are used. An unknown protein sequence can be classified by searching for those fingerprints from the database, assessing matches by estimating the statistical significance. The current implementation of the model algorithm for searching is a powerful tool that uses the PRINTS database as an example, however it does not support the option of adding of new features due to the conservative design of the program and the lack of publicly available code. A new version of the PRINTS database has recently been developed and this will require adding new features in the future. A novel object-oriented model for the implementation of the algorithm is proposed. This model is used to build a web application prototype, written in Python--the most widely used programming language in bioinformatics at present. The result of this study is a maintainable software with open source code that can easily be extended with new functionalities. [ABSTRACT FROM AUTHOR]

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification. [ABSTRACT FROM AUTHOR]

Carbon-based fuels burned at industrial facilities account for a large proportion of greenhouse gas emissions, and an up-to-date spatiotemporally detailed inventory is essential for a better understanding of global carbon emission patterns. The Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product offers a quantitative estimation of the temperatures of sub-pixel heat sources, providing the potential for detecting thermal anomalies from industrial sectors across the globe. However, identifying subcategories of various industrial heat sources is challenging because there are scarcely any stable and typical characteristics for their classification at a single thermal anomaly scale. Specifically, these nighttime thermal anomalies exhibit a strong spatiotemporal heterogeneity (e.g., fluctuations in retrieved temperature, spatial shifts in position, and presence of false positives), even in industrial heat sources that do not vary through time. Here, we demonstrate an object-oriented approach to robustly segment and accurately classify various industrial heat sources from a time-series of the VIIRS Nightfire product. The approach operates from the cluster level of spatially adjacent nighttime thermal anomalies (i.e., nighttime-heat-source objects rather than individual thermal anomalies) to generate fingerprint-like characteristics and to address the challenge of spatiotemporal heterogeneity. Specifically, the spatial-aggregation characteristic of nighttime thermal anomalies from continuously operating industrial heat sources and the temporal-aggregation characteristics of biomass burnings were incorporated to differentiate industrial nighttime-heat-source objects from ubiquitous biomass burnings. Subsequently, the similarity of the thermal signals of nighttime thermal anomalies from identical industrial heat sources was used to generate highly recognizable characteristics for their identification. A spatiotemporally detailed inventory of industrial heat sources across the globe was then established from this object-oriented classification. The inventory included a total of 15,199 industrial heat sources, representing 49.52% of all higher confidence nighttime thermal anomalies in the VIIRS Nightfire product. Validation of the results showed that only 218 objects (1.43%) were biomass burnings or active volcanoes that were misclassified as industrial heat sources. Further validation of sub-categories indicated an overall classification accuracy of ~ 77%. Our findings suggest that the VIIRS Nightfire product has great potential for monitoring the global distribution and dynamics of industrial heat sources, and combined with the object-oriented approach developed here the methodology is simple, robust, and cost-effective. [ABSTRACT FROM AUTHOR]

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale.

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = -0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to -0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments. [ABSTRACT FROM AUTHOR]

Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular and neurodegenerative diseases among the elderly. Consequently, detection and characterization of WMHs are of significant clinical importance. We propose a novel approach for WMH segmentation from multi-contrast MRI where both voxel-based and lesion-based information are used to improve overall performance in both volume-oriented and object-oriented metrics. Our segmentation method (AMOS-2D) consists of four stages following a 'generate-and-test' approach: pre-processing, Gaussian white matter (WM) modelling, hierarchical multi-threshold WMH segmentation and object-based WMH filtering using support vector machines. Data from 28 subjects was used in this study covering a wide range of lesion loads. Volumetric T1-weighted images and 2D fluid attenuated inversion recovery (FLAIR) images were used as basis for the WM model and lesion masks defined manually in each subject by experts were used for training and evaluating the proposed method. The method obtained an average agreement (in terms of the Dice similarity coefficient, DSC) with experts equivalent to inter-expert agreement both in terms of WMH number (DSC = 0.637 vs. 0.651) and volume (DSC = 0.743 vs. 0.781). It allowed higher accuracy in detecting WMH compared to alternative methods tested and was further found to be insensitive to WMH lesion burden. Good agreement with expert annotations combined with stable performance largely independent of lesion burden suggests that AMOS-2D will be a valuable tool for fully automated WMH segmentation in patients with cerebrovascular and neurodegenerative pathologies. [ABSTRACT FROM AUTHOR]

The study applies object-oriented analysis to the extraction of urban canyons and introduces the concept of directed urban canyon which is then being experimentally applied in urban meteorological modeling. Summary of current approaches for describing urban canyon geometry is provided. Then a new theoretical approach to canyon delineation and three-level hierarchical classification is presented. The study discloses an original methodology based on triangular irregular network (TIN) designed to allow extraction of directed and undirected urban canyons from cartographic data and estimation of their geometric characteristics. Obtained geometric properties of canyons are coupled with land cover data into the database, which is then applied in micro-to-local scale temperature and wind modeling. Using URB_MOS model and the derived database we refined COSMO_RU modeling results which facilitated the decrease of the mean root square error of temperature forecast from 4.0 to 2.1 degrees. Estimation of possible wind accelerations along canyons using the derived database is also presented. Results and future perspectives are summarized in the concluding part. [ABSTRACT FROM AUTHOR]

Results of theoretical investigations are presented, forming of class structure of electromechanics transformers of energy is shown, the generation of the field models on the basis of principles of the object-oriented analysis is considered. References 5, table 1, figures 3. [ABSTRACT FROM AUTHOR]

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale. [ABSTRACT FROM AUTHOR]

Enterprises have now realised that in order to increase the competitiveness, they not only need to improve efficiencies within the enterprise, but also need to improve the performance of the entire supply chain. Because of the increasingly stringent environmental regulations, enterprises are interested in establishing green supply chains. The objective of this paper is to demonstrate that environmental impact data sharing is a key success factor in developing a green supply chain. The tool set of IDEF (Integration Definition) is applied to describe the overall flow of the data-sharing process. An object-oriented analysis approach is used to represent the architecture of the collaborative data-sharing model. Finally, a case study in the textile/clothing industry is used to illustrate the process and the impact of environmental data sharing in a green supply chain. [ABSTRACT FROM PUBLISHER]

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. [Copyright &y& Elsevier]

This study builds on research indicating significant variability among topics covered in the teaching of systems analysis and design, and identifies topics that practitioners consider important. Shannon's entropy is used to analyse the opinions and measure the agreement or disagreement among survey respondents. The findings indicate agreement regarding which traditional topics are not important and agreement among which object-oriented and structured analysis subtopics are important. The results indicate enough variance to cause concern that IS graduates may not have the knowledge, skills, and abilities desired by their potential employers. This analysis provides a basis for future comparisons. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe a management process to better control scuba diving on coral reefs. While the dive industry permits growth in local tourism revenues it can also create a degradation of the coral reefs if there are too many divers. Consequently appropriate management is essential. In this study, a case analysis of Kenting National Park was undertaken. The authors describe a process-based reference model and information system to better manage diving through collaboration by the different units, namely the divers, dive centers, the park office, the nuclear power plant bodies, the park police and the coastal guards. The management tool set of IDEF0 (Integration Definition for Function Modeling 0) was applied to describe the overall diving service workflow. An object-oriented analysis approach was then applied to represent the design of the collaborative model. A prototype web-based platform was constructed to demonstrate the model for in situ information that potentially could monitor diving and improve communication channels between the actors. [Copyright &y& Elsevier]

To support community development, there is a need for community information systems. The objective of the study is to develop such systems for supporting community development. To start, documents were reviewed and data of related agencies was compared. The survey of 400 samples was conducted and then 32 key informants were interviewed. The results showed that the essential requirement pattern of geographic-based community information systems consists of 13 aspects of community information. After that, the system was developed using an agile approach. Finally, the system was evaluated and it showed that users were satisfied with the overall system. [ABSTRACT FROM AUTHOR]

Abstract: Lately, rapid development of new cities in developing countries (such as India, China, and Vietnam) has accelerated the growth of the motorcycle industry in these markets. In the past, with respect to the motorcycle industry, the lack of adequate systems to control the quantity of overloaded polluting products has led to serious environmental problems. In order to reduce damage to the environment, this paper presents a process-based reference model and information system that can be used by the motorcycle industry to better manage and control the quantity and quality of the supply network. The IDEF (integration definition) tool set is applied to describe the overall operational workflow. An object-oriented, analytical approach is used to represent the architecture of the collaborative model. A prototype platform is constructed to demonstrate the effectiveness of the model in controlling the supply network of materials and to show the improved communications. [Copyright &y& Elsevier]

Abstract: There has been a lot of interest in ethnography within human–computer interaction over the last two decades, and its relevance within systems development is today beyond question. However, one of the challenges reported is that ethnography generates findings and knowledge with such contextual richness that it can be hard to transfer into system design. In the light of recent years'' push for the use of ethnography within the area of mobile human–computer interaction, this challenge has resurfaced and is of renewed importance to the research field. In this article we describe an interdisciplinary combination of ethnography with a structured software engineering method supporting the transition from collected data to design and implementation. We explore this combination through two case studies of mobile system development for supporting distributed work activities within industrial process control. We show that when developing mobile systems ethnographic data is a highly valuable source of input for developing object-oriented models by providing contextual richness, and that in turn, objected-oriented analysis is a highly valuable method for working with ethnographic field data in systems development by supporting the creation of abstract models. Combining the two, we have a method where ethnographic field studies inform core system design. [Copyright &y& Elsevier]

Abstract: The floodplain forests bordering the Amazon River have outstanding ecological, economic, and social importance for the region. However, the original distribution of these forests is not well known, since they have suffered severe degradation since the 16th century. The previously published vegetation map of the Amazon River floodplain (Hess et al., 2003), based on data acquired in 1996, shows enormous difference in vegetation cover classes between the regions upstream and downstream of the city of Manaus. The upper floodplain is mostly covered by forests, while the lower floodplain is predominantly occupied by grasses and shrubs. This study assesses deforestation in the Lower Amazon floodplain over a ~30 year period by producing and comparing a historical vegetation map based on MSS/Landsat images acquired in the late 1970s with a recent vegetation map produced from TM/Landsat images obtained in 2008. The maps were generated through the following steps: 1) normalization and mosaicking of images for each decade; 2) application of a linear mixing model transformation to produce vegetation, soil and shade fraction-images; and 3) object-oriented image analysis and classification. For both maps, the following classes were mapped: floodplain forest, non-forest floodplain vegetation, bare soil and open water. The two maps were combined using object-level Boolean operations to identify time transitions among the mapped classes, resulting in a map of the land cover change occurred over ~30 years. Ground information collected at 168 ground points was used to build confusion matrices and calculate Kappa indices of agreement. A survey strategy combining field observations and interviews allowed the collection of information about both recent and historical land cover for validation purposes. Kappa values (0.77, 0.75 and 0.75) indicated the good quality of the maps, and the error estimates were used to adjust the estimated deforested area to a value of 3457 km2 ± 1062 km2 (95% CI) of floodplain deforestation over the ~30 years. [Copyright &y& Elsevier]

Abstract: Recognition and classification of landslides is a critical requirement in pre- and post-disaster hazard analysis. This has been primarily done through field mapping or manual image interpretation. However, image interpretation can also be done semi-automatically by creating a routine in object-based classification using the spectral, spatial and morphometric properties of landslides, and by incorporating expert knowledge. This is a difficult task since a fresh landslide has spectral properties that are nearly identical to those of other natural objects, such as river sand and rocky outcrops, and they also do not have unique shapes. This paper investigates the use of a combination of spectral, shape and contextual information to detect landslides. The algorithm is tested with a 5.8m multispectral data from Resourcesat-1 and a 10m digital terrain model generated from 2.5m Cartosat-1 imagery for an area in the rugged Himalayas in India. It uses objects derived from the segmentation of a multispectral image as classifying units for object-oriented analysis. Spectral information together with shape and morphometric characteristics was used initially to separate landslides from false positives. Objects recognised as landslides were subsequently classified based on material type and movement as debris slides, debris flows and rock slides, using adjacency and morphometric criteria. They were further classified for their failure mechanism using terrain curvature. The procedure was developed for a training catchment and then applied without further modification on an independent catchment. A total of five landslide types were detected by this method with 76.4% recognition and 69.1% classification accuracies. This method detects landslides relatively quickly, and hence has the potential to aid risk analysis, disaster management and decision making processes in the aftermath of an earthquake or an extreme rainfall event. [Copyright &y& Elsevier]

Abstract: An object-oriented mapping approach based on subspace analysis of airborne hyperspectral images was investigated in this paper. Hyperspectral features were extracted based on subspace learning approaches, in order to reduce the redundancy of spectral space and extract the characteristic images for the further object-oriented classification. In this paper, three kinds of spectral feature extraction (FE) methods were utilized to obtain the subspace of airborne hyperspectral data: (1) unsupervised FE, such as PCA (principal component analysis), ICA (independent component analysis) and MNF (maximum noise fraction); (2) supervised FE, e.g. DBFE (decision boundary feature extraction), DAFE (discriminant analysis feature extraction) and NWFE (nonparametric weighted feature extraction); and (3) linear mixture analysis. Afterwards, the extracted subspace features were fed into the object-based classification system. The FNEA (fractal net evolution approach) was utilized to extract objects from the subspace images and SVM (support vector machines) was then used to classify the object-based features. Experiments were conducted on two airborne hyperspectral datasets: (1) the AVIRIS dataset over the northwest Indiana''s Pine with 220 spectral bands (agricultural region), and (2) the ROSIS dataset over Pavia University, northern of Italy with 102 spectral bands (urban region). Results revealed that the proposed object-based approach could give significantly higher accuracies than the traditional pixel-based subspace classification. [Copyright &y& Elsevier]