Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.This work was supported by the National Science Foundation China (61701396 and D010701), the Science Foundation of Hunan Province (Grant No. 2016JJ6100), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), and the project from the China Postdoctoral Science Foundation (2015M572658XB).Peer Reviewe

Operational Wildfire Monitoring and Disaster Management Support Using State-of-the-art EO and Information Technologies

Fires have been one of the main driving forces in the evolution of plants and ecosystems, determining the current structure and composition of the Landscapes. However, significant alterations in the fire regime have occurred in the recent decades, primarily as a result of socioeconomic changes, increasing dramatically the catastrophic impacts of wildfires as it is reflected in the increase during the 20th century of both, number of fires and the annual area burnt. Therefore, the establishment of a permanent robust fire monitoring system is of paramount importance to implement an effective environmental management policy. Such an integrated system has been developed in the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA). Volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters in the framework of the operational GMES projects SAFER and LinkER addressing fire emergency response and emergency support needs for the entire European Union. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA’s in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integra

Remote sensing of vegetation fires and its contribution to a fire management information system

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the type of information remote sensing can provide to the fire community. It considers first fire management information needs in the context of fire management information system. An introduction to remote sensing then precedes the description of fire information obtainable from remote sensing data (such as vegetation status, active fire detection and burned areas assessment). Finally, operational examples in five African countries illustrate how the information can be used in practice

Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture

L'abstract è presente nell'allegato / the abstract is in the attachmen

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

International audienceCoastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring. A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management

8. Remote Sensing Of Vegetation Fires And Its Contribution To A Fire Management Information System

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then precedes a description of fire information obtainable from remote sensing data (such as vegetation status, active fire detection and burned areas assessment). Finally, operational examples in five African countries illustrate the practical use of remotely sensed fire information. As indicated in previous chapters, fire management usually comprises activities designed to control the frequency, area, intensity or impact of fire. These activities are undertaken in different institutional, economic, social, environmental and geographical contexts, as well as at different scales, from local to national. The range of fire management activities also varies considerably according to the management issues at stake, as well as the available means and capacity to act. Whatever the level, effective fire management requires reliable information upon which to base appropriate decisions and actions. Information will be required at many different stages of this fire management system. To illustrate this, we consider a typical and generic description of a fire management loop , as provided in Figure 8.1. Fire management objectives result from fire related knowledge . For example, they may relate to sound ecological reasons for prescribed burning in a particular land management context, or to frequent, uncontrolled fires threatening valuable natural or human resources. Whatever the issues, appropriate objectives require scientific knowledge (such as fire impact on ecosystems components, such as soil and vegetation), as well as up-to date monitoring information (such as vegetation status, fire locations, land use, socioeconomic context, etc.). Policies, generally at a national and governmental level, provide the official or legal long term framework (e.g. five to ten years) to undertake actions. A proper documentation of different fire issues, and their evolution, will allow their integration into appropriate policies, whether specific to fire management, or complementary to other policies in areas such as forestry, rangeland, biodiversity, land tenure, etc. Strategies are found at all levels of fire management. They provide a shorter-term framework (e.g. one to five years) to prioritise fire management activities. They involve the development of a clear set of objectives and a clear set of activities to achieve these objectives. They may also include research and training inputs required, in order to build capacity and to answer specific questions needed to improve fire management. The chosen strategy will result from a trade-off between priority fire management objectives and the available capacity to act (e.g. institutional framework, budget, staff, etc.), and will lead towards a better allocation of resources for fire management operations to achieve specific objectives. One example in achieving an objective of conserving biotic diversity may be the implementation of a patch-mosaic burning system (Brockett et al., 200 1 ) instead of a prescribed block burning system, based on an assumption that the former should better promote biodiversity in the long-term than the latter (Parr & Brockett, 1999). This strategy requires the implementation of early season fires to reduce the size of later season fires. The knowledge of population movements, new settlements or a coming El Nino season, should help focus the resources usage, as these factors might influence the proportion as well as the locations of area burned. Another strategy may be to prioritise the grading of fire lines earlier than usual based on information on high biomass accumulation. However, whatever the strategies, they need to be based on reliable information

8. Remote Sensing Of Vegetation Fires And Its Contribution To A Fire Management Information System

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then precedes a description of fire information obtainable from remote sensing data (such as vegetation status, active fire detection and burned areas assessment). Finally, operational examples in five African countries illustrate the practical use of remotely sensed fire information. As indicated in previous chapters, fire management usually comprises activities designed to control the frequency, area, intensity or impact of fire. These activities are undertaken in different institutional, economic, social, environmental and geographical contexts, as well as at different scales, from local to national. The range of fire management activities also varies considerably according to the management issues at stake, as well as the available means and capacity to act. Whatever the level, effective fire management requires reliable information upon which to base appropriate decisions and actions. Information will be required at many different stages of this fire management system. To illustrate this, we consider a typical and generic description of a fire management loop , as provided in Figure 8.1. Fire management objectives result from fire related knowledge . For example, they may relate to sound ecological reasons for prescribed burning in a particular land management context, or to frequent, uncontrolled fires threatening valuable natural or human resources. Whatever the issues, appropriate objectives require scientific knowledge (such as fire impact on ecosystems components, such as soil and vegetation), as well as up-to date monitoring information (such as vegetation status, fire locations, land use, socioeconomic context, etc.). Policies, generally at a national and governmental level, provide the official or legal long term framework (e.g. five to ten years) to undertake actions. A proper documentation of different fire issues, and their evolution, will allow their integration into appropriate policies, whether specific to fire management, or complementary to other policies in areas such as forestry, rangeland, biodiversity, land tenure, etc. Strategies are found at all levels of fire management. They provide a shorter-term framework (e.g. one to five years) to prioritise fire management activities. They involve the development of a clear set of objectives and a clear set of activities to achieve these objectives. They may also include research and training inputs required, in order to build capacity and to answer specific questions needed to improve fire management. The chosen strategy will result from a trade-off between priority fire management objectives and the available capacity to act (e.g. institutional framework, budget, staff, etc.), and will lead towards a better allocation of resources for fire management operations to achieve specific objectives. One example in achieving an objective of conserving biotic diversity may be the implementation of a patch-mosaic burning system (Brockett et al., 200 1 ) instead of a prescribed block burning system, based on an assumption that the former should better promote biodiversity in the long-term than the latter (Parr & Brockett, 1999). This strategy requires the implementation of early season fires to reduce the size of later season fires. The knowledge of population movements, new settlements or a coming El Nino season, should help focus the resources usage, as these factors might influence the proportion as well as the locations of area burned. Another strategy may be to prioritise the grading of fire lines earlier than usual based on information on high biomass accumulation. However, whatever the strategies, they need to be based on reliable information

Geoinformatic and Hydrologic Analysis using Open Source Data for Floods Management in Pakistan

There is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakistan

Land Cover Mapping and Change Analysis in Tropical Humid-highlands: Case of Ndakaini Water Reservoir in Central Kenya

We successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya.   Landsat imageries (TM and ETM+) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords: Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlands DOI: 10.7176/JNSR/10-8-03 Publication date: April 30th 202

Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PD