Change Detection Index Research Articles

Wetland identification through remote sensing: Insights into wetness, greenness, turbidity, temperature, and changing landscapes

Wetlands are important in many ways, including hydrological cycles, ecosystem diversity, climate change, and economic activity. Despite the Ramsar Convention's awareness programmes, the importance of wetlands is frequently disregarded in underdeveloped countries. The Ramsar Convention recognises 2491 wetlands worldwide, 19 of which are in Pakistan. The goal of this study is to use satellite sensor technology to identify neglected wetlands in Pakistan. The key goals of this research are to analyse water quality, monitor ecological changes, and comprehend the impact of climate change on the aforementioned wetlands. We used approaches like supervised classification and TCW to identify wetlands. To detect climate-induced changes, a change detection index was used to Quick Bird imagery. TCG and the NDTI were also employed to examine the water quality and ecological changes in these wetlands. Sentinel-2 data between 2016 and 2019 were used in the analysis. Furthermore, watershed analysis was carried out using ASTER DEM data. Modis data was used to calculate the LST (°C) of the selected wetlands, while rainfall (mm) data was collected from ANN databases. According to the study's findings, in 2016, Borith, Phander, Upper Kachura, Satpara, and Rama Lake held 22.73%, 20.79%, 23.01%, 24.63%, and 23.03% water, respectively. In 2019, the water ratios for these lakes were 23.40%, 22.10%, 22.43%, 25.01%, and 24.56%. These findings emphasise the need of taking preventative actions to protect these wetlands in order to improve ecosystem dynamics in the future. As a result, it is critical that the relevant authorities implement the necessary conservation measures.

Identification of time-varying wetlands neglected in Pakistan through remote sensing techniques.

Aside from Ramsar Convention awareness programs, the concept of wetlands is mostly ignored in developing countries. Wetland ecosystems are essential to hydrological cycles, ecosystem diversity, climatic change, and economic activity. Under the Ramsar Convention, there are 2414 wetlands that are internationally recognized, and Pakistan is home to 19 of them. The major goal of this study is to use the satellite image technology to locate Pakistan's underutilized wetlands (Borith, Phander, Upper Kachura, Satpara, and Rama Lakes). The other goals are to understand how these wetlands are affected by climate change, ecosystem change, and water quality. We used analytical techniques including supervised classification and Tasseled Cap Wetness to identify the wetlands. To find changes caused by climate change, Quick Bird high-resolution images was used to create the change detection index. Tasseled Cap Greenness and the Normalized Difference Turbidity Index were also used to assess the water quality and changes in the ecology in these wetlands. Sentinel-2 was used to analyze data from 2010 and 2020. ASTER DEM was also used to do a watershed analysis. The land surface temperature (°C) of a few selected wetlands was calculated using Modis data. Rainfall (mm) data was taken from PERSIANN (precipitation estimation from remotely sensed information using artificial neural networks) databases. Results indicated that in 2010, the water content of Borith, Phander, Upper Kachura, Satpara, and Rama Lakes was 22.83%, 20.82%, 22.26%, 24.40%, and 22.91%. While in 2020, these lakes' water ratios are 21.33%, 20.65%, 21.76%, 23.85%, and 22.59%, respectively. Therefore, the competent authorities must take precautions to ensure that these wetlands are preserved in the future in order to improve the dynamics of the ecosystem.

Adaptive methods of detecting environmental changes using multispectral satellite images on the Earth for example territory Solotvyno

The methods for detecting environmental changes using two multispectral multispectral space images of the Earth, which can be used to assess changes in the ecological and geological environment to control the dynamics of processes in real time, in order to prevent environmental emergencies. The creation and operation of a system of continuous geoecological monitoring based on space information involves the detection of relative changes in the environment on two multispectral space images of the Earth, obtained after a certain period of time. To do this, it is necessary to develop and apply adaptive methods (indices) for detecting relative changes. Three methods are proposed that are adapted to specific images to find the optimal solution that maximizes the relative changes in two different time multispectral space images of the Earth. The first method selects one optimal channel from all channels of the space image, the second method - two optimal channels from all channels and the third - four optimal channels from all channels. There are known methods that assess the presence or absence of changes in two space images at different times, but they do not provide information about the direction of changes. The proposed indices can take positive and negative values, reflecting the trend of changes on the ground. The negative values of the indices obtained in the area near the village of Solotvyno, Tyachiv district, Zakarpattia region, correspond to the areas with the most dynamic changes in the environment of such exogenous processes as karst funnels and lakes, and positive values correspond to increasing vegetation areas. The results of comparing the application of the change detection index on two different multispectral space images of the Earth on four, two channels and one channel showed that increasing the number of channels can give a more reasonable picture of changes, but to concretize these changes requires ground-based observations.

Proposing an effective and inexpensive tool to detect urban surface temperature changes associated with urbanization processes in small cities

A consequence of urbanization is the loss of vegetation cover, replaced by urban areas. This study investigates the evolution of the built-up areas and its influence on increasing the surface temperature through methodology applicable to small and medium-sized municipalities where urbanization process monitoring is limited. For the case study, the municipality of Penedo, located in the State of Alagoas, was selected. Landsat-5 and Landsat-8 images were used to evaluate urban landscape through normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and the land surface temperature (LST). A simple tool that summarizes NDBI and LST changes was proposed - the Change Detection Index (CDI). It was possible to highlight areas where the increase in temperatures is associated with the built area's intensification. Furthermore, the machine learning k-means clustering tool was applied to create groups and analyze the relationship between urbanization and temperature increase, confirming the CDI values and validating this tool. An evident increase in the built area was highlighted by CDI and cluster analysis, indicating a heat island's potential formation at the implantation area of a housing complex in the municipality. The spatial analysis indicates priority areas for intervention by a simple methodology that facilitated dialogue and understanding between stakeholders, associated with a low-cost tool with the potential for reproduction and adaptation, providing indicators to urban studies and decision-makers and handle the lack of monitoring data in small and medium-sized municipalities.

Islanding Detection Scheme for Inverter-Based Distributed Generation Systems Using Cumulative Reactive Power Harmonics

In this paper, a new islanding detection scheme using cumulative reactive power harmonics (CRPH) for inverter-based distributed generation (DG) systems is presented. In the proposed method, first, any changes in the system are detected using a change detection index. Desired harmonic components of the voltage and current signals at DG terminals are extracted by a discrete Kalman filter (DKF). Then, a criterion based on CRPH is defined using the extracted harmonic signatures. The change detection index is based on the difference of measured current and its estimated fundamental component. At the initial moments of any events, reactive power controller of the inverter is disabled just for a short time interval. Since the main grid as the main source of reactive power loses in islanding condition, the reactive harmonic power of the system experiences significant variations. Then, islanding can be discriminated from other similar conditions by processing the captured signals of the mentioned time interval. Performance of the proposed method is assessed using plenty of simulations. Moreover, the superiority of the proposed method is confirmed by performing a comparison with other similar techniques.

A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping

Change detection using Remote Sensing (RS) techniques is valuable in numerous applications, including environmental management and hazard monitoring. Synthetic Aperture Radar (SAR) images have proven to be even more effective in this regard because of their all-weather, day and night acquisition capabilities. In this study, a polarimetric index based on the ratio of span (total power) values was introduced, in which neighbourhood information was considered. The role of the central pixel and its neighbourhood was adjusted using a weight parameter. The proposed index was applied to detect flooded areas in Dongting Lake, Hunan, China, and was then compared with the Wishart Maximum Likelihood Ratio (MLR) test. Results demonstrated that although the proposed index and the Wishart MLR test yielded similar accuracies (accuracy of 94% and 93%, and Kappa Coefficients of 0.82 and 0.86, respectively), inclusion of neighbourhood information in the proposed index not only increased the connectedness and decreased the noise associated with the objects within the produced map, but also increased the consistency and confidence of the results.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities—A case study for Muzzaffarabad, Pakistan

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

Enhanced change detection index for disaster response, recovery assessment and monitoring of accessibility and open spaces (camp sites)

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects. This change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management practices.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m3/m3 for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m3/m3 and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Statistical properties of signal entropy for use in detecting changes in time series data

Detecting changes in an underlying time series model for a system is an important task in many different fields, including econometrics, geophysics and process control. Specifically, in process control, detecting model changes is often the first step for fault detection, plant‐model mismatch assessment and data quality assessment for system identification. Signal entropy, which basically measures the amount of disorder in a given signal, can, not only segment a time series, but can also determine which regions have similar underlying models. Thus, the changes between the input and output signals can be used to determine when model is no longer an accurate representation of the system by comparing the current differential entropy against the historical differential entropy. This paper presents the statistical properties of signal entropy for discrete time systems. An example of the general results is provided by determining the entropy characteristics for first‐order systems driven by white noise. As well, a change detection index is proposed to assess changes in the time series model, which is tested on an experimental system. Copyright © 2013 John Wiley & Sons, Ltd.

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

An improved RST approach for timely alert and Near Real Time monitoring of oil spill disasters by using AVHRR data

Abstract. Information acquired and provided in Near Real Time is fundamental in contributing to reduce the impact of different sea pollution sources on the maritime environment. Optical data acquired by sensors aboard meteorological satellites, thanks to their high temporal resolution as well as to their delivery policy, can be profitably used for a Near Real Time sea monitoring, provided that accurate and reliable methodologies for analysis and investigation are designed, implemented and fully assessed. In this paper, the results achieved by the application of an improved version of RST (Robust Satellite Technique) to oil spill detection and monitoring will be shown. In particular, thermal infrared data acquired by the NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) have been analyzed and a new RST-based change detection index applied to the case of the oil spills that occurred off the Kuwait and Saudi Arabian coasts in January 1991 and during the Lebanon War in July 2006. The results obtained, even in comparison with those achieved by other AVHRR-based techniques, confirm the unique performance of the proposed approach in automatically detecting the presence of oil spill with a high level of reliability and sensitivity. Moreover, the potential of the extension of the proposed technique to sensors onboard geostationary satellites will be discussed within the context of oil spill monitoring systems, integrating products generated by high temporal (optical) and high spatial (radar) resolution satellite systems.

Timber age verification using historical satellite image analysis

Timber inventory data is important for estimating the net present value (NPV) of timberland sales among paper companies, timber investment and management organizations (TIMO), institutional investors, and individual investors. Timber age in an inventory GIS database can be quickly verified through a series of historical satellite image analyses based on image differencing techniques. The normalized difference vegetation index (NDVI), tassel-cap greenness, wetness, simple differencing of band 5, and a vegetation conditions index were used collectively as an individual change detection index, and simple averaging was employed in the integration of individual results. With respect to timberlands of more than 200,000 acres in the states of Washington and Oregon, harvest years from satellite image analysis and timber ages in the inventory GIS database were compared, and the areas of age mismatch were summarized for field verification. This practice is not a complete solution for inventory verification, but it can reduce significant time in on-site verification by effectively offering a list of potential age-discrepancy tracts. With the support of fieldwork confirmation, the proposed practice can be a very effective and efficient means of practicing due diligence for timberlands in the forestry industry.

Automatic change detection by evidential fusion of change indices

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological, and climatic modeling. We propose a way to improve mono-index change detection by a fusion of multi-index change detection results. This fusion is performed in the framework of the Dempster-Shafer evidence theory, which is particularly suited to the representation of imprecision and ignorance at the “no change”/“change” class border. Depending on the change detection index considered, we also need to determine the class number and features. This is done using the a contrario theory approach rather than classical statistical tests. The proposed algorithm is applied to forest fire damage evaluation based on three popular change indices: normalized difference values, texture evolution, and mutual information (MI). We find that change index fusion is effective at reducing both false alarm and misdetection levels, due to the complementary nature of these indices.

Effects of climate change on snow accumulation and melting in the Broye catchment (Switzerland)

The study used a daily step conceptual hydrological model to examine the effects of climate change on snowfall accumulation and on snow cover melting in the Broye catchment (moderate relief- altitude from 400 to 1500 m a.s.l.). Five elevation bands representing a range of climatic conditions were used together with three realistic climate change scenarios based loosely on GCM's predictions and which reflect feasible changes by extending time periods. For a very moderate climate change (rise in air temperature of ca 1 °C), possibly in a near future, the reduction of snow cover duration, mean water equivalent and monthly maximum water equivalent is the most sensitive in the lower part of the catchment and during the first and last months of the snow season. In the higher part of the basin and during the colder months January and February, similar reduction rates can be expected in case of larger climate changes. The floods due to the melting of snow cover are lower. Sometimes rainfall, considered as snow in the present day conditions, generates additional floods during the winter season. For winter sports resorts below 1500 m a.s.l., even the very moderate climatic change scenario (temperature rise around 1 °C) leads to economically very difficult conditions. Finally, a climatic change detection index based on snow cover duration is proposed.