Remote Sensing Research Articles

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation.

Unveiling Groundwater Potential in Hangu District, Pakistan: A GIS-Driven Bivariate Modeling and Remote Sensing Approach for Achieving SDGs

Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and Frequency Ratio (FR) models to delineate Groundwater Potential Zones (GWPZs) in the Hangu District, a hydrologically stressed riverine region in northern Pakistan, to support the Sustainable Development Goals (SDGs). Ten key variables, including elevation, slope, aspect, distance to drainage (DD), rainfall, land use/land cover, Normalized Difference Vegetation Index, lithology, and road proximity, were incorporated into the Geographic information system (GIS) environment. The FR model outperformed the WoE model, achieving success and prediction rates of 89% and 93%, compared to 82% and 86%. The GWPZs-FR model identified 23% (317 km2) as high potential, located in highly fractured pediment fans below 550 m, with gentle slopes (<5 degrees), DD (within 200 m), and high rainfall in areas of natural trees and vegetation on valley terrace deposits. The research findings significantly support multiple SDGs, with estimated achievement potentials of 37.5% for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions of 10% for SDG 2 and 5% for SDG 3. This approach provides valuable insights for policymakers, offering a framework for managing groundwater resources and advancing sustainable practices in similar hydrologically stressed regions.

Methods of Calculation of Daily Flooded Areas in the Volga Delta During the Flood Periods Based on the Remote Sensing Data

We developed methods for calculating the daily flooded areas in the Volga delta (VD) for the entire flood period using satellite data. The methodology is based on the construction of dependences of the flooding areas of the hydrographic network and interchannel spaces of VD (Ftotal) on the means of daily water levels in the channels of watercourses (НР). Ftotal for individual flood dates were determined using satellite images. Data on НР were taken at hydrological stations (h/s) on the same dates from the State Water Cadastre. These dependencies were used to calculate the daily flooding areas of DV with high accuracy for flood periods of different water contents (high-water, low-water and medium-water contents). This method was developed in two modifications: 1 – modification for the case of the sufficient number of satellite images to cover all the main changes in the course of each phase of the flood, 2 – modification for the case of the insufficient number of satellite images to cover all the main changes in the course of each phase of the flood. We conducted a comparative analysis of daily Ftotal obtained with high accuracy using modification 1 for floods of different water contents (high-water, low-water, medium-water contents). We revealed how floods of different water contents differ in characteristics including timing of passage, amplitude, area of maximum flooding area, duration of the rise phase, decline phase and flood plateau phase. Such calculations have never been conducted before. The results of calculations by this method allow us to identify the spatial-temporal patterns of the VD flooding under different types of water contents. Our method enables to predict the dynamics of floods and to calculate the water balance of the Volga delta.

Application of UAVs and Remote Sensing Technologies for Atmospheric CO2 Capturing: A Study Application of UAVs and Remote Sensing in CO2 Reductions

Human activities are a significant contributor to climate change, with rising levels of CO₂ in the atmosphere. Several carbon capture and sequestration (CCS) methods have been developed to address this issue. Uncrewed Aerial Vehicles (UAVs) and remote sensing technologies are emerging as significant improvements to the efficiency and effectiveness of atmospheric carbon capture initiatives. This research examines using UAVs and remote sensing technologies to monitor, quantify, and manage atmospheric CO₂ levels. Furthermore, the study explores the implications of integrating robotic-drone technology, emphasizing their ability to contribute to a sustainable future. These technologies, incorporating modern data collection and analysis methodologies, provide promising answers for climate change mitigation and long-term environmental sustainability.

Rural Settlement Dynamics in a Rapidly Urbanizing Landscape: Insights from Satellite Remote Sensing and Archaeological Field Surveys in Zanzibar, Tanzania

ABSTRACT In Africa, rapid urbanization covers archaeological sites and limits the utility of traditional archaeological field survey methods. This is not only a crisis for archaeological heritage conservation, but it also distinctly impacts anthropological understandings of African urban trajectories since much evidence for precolonial urbanism lies within areas of rapid expansion. However, high-resolution multitemporal satellite data may facilitate reconstructions of urban growth in African cities, enabling archaeological surveys to target undeveloped areas for prospection within the interstices of modern urban development. This paper describes an application of satellite remote sensing for archaeological prospection within the rapidly urbanizing hinterland of Zanzibar Stone Town, a UNESCO World Heritage site. Survey results reveal settlement trajectories around the city over the last millennium, drawing attention to the role of rural agricultural land as a factor in the emergence of precolonial urbanism and the continued significance of rural places as urbanization progressed into the Colonial era.

Application of Remote Sensing in Analyzing Mangrove Forest Changes and Drivers in Mui Ca Mau National Park, Vietnam

Mangroves are vital in coastal ecology and for sustaining and securing coastal communities. However, several factors such as human activities and climate change have changed mangroves' quantity and quality. This research analyses land-use/land cover (LULC) changes focusing on mangroves in Mui Ca Mau National Park (MCMNP) from 1995-2022. Collected Landsat imagery in 1995, 2003, and 2022 were applied to perform this research. Overall accuracy and Kappa of land cover classification were 88.5% and 0.86 in 2003, and 87.6% and 0.85 in 2022, respectively. The classified results showed that the mangrove area had increased significantly from 4,316.1 hectares in 1995 to 8,741.3 and 10,764.1 hectares in 2003 and 2022, respectively. In addition, the drivers of mangrove change were identified and analyzed. This study showed the important role of policy in mangrove conservation and the sustainable use of natural resources. The study provided useful information for policy-making in terms of forest conservation and management.

Determination of Forest Health using Remote Sensing Techniques in Gashaka-Gumti National Park, Northeast, Nigeria

This study was conducted in order to determine the health status of forest vegetation in Gashaka-Gumti National Park. Landsat images were downloaded from the USGS website. The images were pre-processed using radiometric correction since the reflectance values were needed for computing spectral indices, the digital numbers were converted to radiance and reflectance, and analysis was carried out using a raster calculator. The range of NDVI, GNDVI, ARVI and MSI were used for health assessment. Utilizing NDVI, GNDVI, ARVI, and MSI as assessment tools revealed moderate to good health in most forest regions, with higher ARVI, GNDVI, and NDVI indicating healthier vegetation and elevated MSI values suggesting areas under moisture stress. the average values of NDVI, GNDVI, ARVI, and MSI over three decades indicate a potential decline in overall vegetation health, reduced green vegetation, changes in vegetation conditions, and a decrease in moisture stress, suggesting a potential increase in greening and photosynthetic activities in plants. These trends highlight the dynamic nature of the forest ecosystem over the studied period. Positive correlations between ARVI, GNDVI, and NDVI across years indicate a consistent vegetation pattern, while negative correlations with MSI suggest potential inverse relationships, providing valuable insights into forest health dynamics. Higher values of ARVI, GNDVI, and NDVI generally signify healthier vegetation, whereas elevated MSI values may indicate areas experiencing moisture stress, emphasizing the importance of monitoring these indices for sustainable forest management. The study recommends the sustained use of NDVI, GNDVI, ARVI, and MSI for forest health monitoring in the study area, implementing integrated pest management based on identified stress conditions, utilize spatial maps for strategic timber harvest planning, developing climate-resilient management considering moisture stress, and invest in research for enhanced assessment precision and understanding of ecosystem dynamics.

Climate Change and Urban Flooding: Assessing Remote Sensing Data and Flood Modeling Techniques: A Comprehensive Review

This review critically examines the current understanding of climate change impacts on urban flooding, synthesizing recent findings to provide a comprehensive overview of how rising temperatures, sea-level rise, and increased extreme weather events influence urban floods. Additionally, it discusses various flood modeling techniques, outlining their advantages and disadvantages. Covering a broad spectrum of studies published between 2007 and 2024, selected for their relevance and methodology, this review highlights several key findings. Climate change significantly exacerbates urban flood disasters, with remote sensing emerging as an indispensable tool for climate change and flood monitoring and prediction. Despite these advancements, notable gaps remain in the literature. There is a conspicuous lack of long-term impact studies and limited discourse on the efficacy of existing mitigation strategies. Additionally, the review underscores the deficiency of real-time flood monitoring systems specifically designed for urban areas. These gaps highlight an urgent need for targeted research and the development of adaptive management practices to enhance flood prediction and management in urban settings. Future research must focus on advancing real-time monitoring systems, integrating remote sensing technologies more effectively, and developing comprehensive flood models. The review also emphasizes the importance of interdisciplinary approaches that merge hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers. It underscores the critical need for innovative flood modeling techniques and adaptive management strategies to tackle the challenges posed by climate change. This synthesis not only enriches the current body of knowledge, but also provides clear directions for future investigations, emphasizing the imperative for improved prediction, preparedness, and response mechanisms in urban flood management.

HCA-RFLA: A SAR Remote Sensing Ship Detection Based on Hierarchical Collaborative Attention Method and Gaussian Receptive Field-Driven Label Assignment Strategy

Ensuring safety at sea has become a primary focus of marine monitoring, driving the increasing adoption of ship detection technology in the maritime industry. Detecting small ship targets in SAR images presents challenges, as they occupy only a small portion of the image and exhibit subtle features, reducing detection efficiency. To address these challenges, we propose the HCA-RFLA algorithm for ship detection in SAR remote sensing. To better capture small targets, we design a hierarchical collaborative attention (HCA) mechanism that enhances feature representation by integrating multi-level features with contextual information. Additionally, due to the scarcity of positive samples for small targets under IoU and center sampling strategies, we propose a label assignment strategy based on Gaussian receptive fields, known as RFLA. RFLA assigns positive samples to small targets based on the Gaussian distribution between feature points and ground truth, increasing the model’s sensitivity to small samples. The HCA-RFLA was experimentally validated using the SSDD, HRSID, and SSD datasets. Compared to other state-of-the-art methods, HCA-RFLA improves detection accuracy by 6.2%, 4.4%, and 3.6%, respectively. These results demonstrate that HCA-RFLA outperforms existing algorithms in SAR remote sensing ship detection.

QAGA-Net: enhanced vision transformer-based object detection for remote sensing images

PurposeVision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.Design/methodology/approachTo address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.FindingsWe verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.Originality/valueThis paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

An Integrated Approach to Riverbed Morphodynamic Modeling Using Remote Sensing Data

River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this context, remote sensing emerges as a potent tool, providing crucial data and the ability to monitor temporal changes. In this paper, an integrated approach combining remote sensing and morphodynamic modeling is proposed to assess river systems comprehensively. By utilizing multispectral or RGB optical imagery from satellites or UAVs for river classification and remotely derived bathymetry, echo sounder data for ground truth, and photogrammetric modeling of emerged areas, we outline a procedure to create an integrated and continuous digital terrain model (DTM) of a riverbed, paying particular attention to the wet–dry interface. This method enables us to identify the river centerline, its width, and its slope variations. Additionally, by applying a linear morphodynamic model that considers the spatial variability of river morphology commonly found in estuarine environments, it is possible to predict the wavelength and migration rate of sediment bars. This approach has been successfully applied to recreate the DTM and monitor the morphodynamics of the seaward reach of the Roya River (Italy).

Climatic and ecological factors responsible for distribution of Phlebotomine Sand flies and occurrence of Cutaneous Leishmaniasis, in Himachal Pradesh India

Background & objectives: Subalpine valley along the Sutlej River has been identified as an endemic focus for Cutaneous Leishmaniasis (CL) in Himachal Pradesh (H.P.). The present study was undertaken with aim to find out the climatic and ecological determinants responsible for distribution of sand flies and occurrence of Cutaneous Leishmaniasis in Himachal Pradesh. Methods: Collections of sand flies were made in the Shimla, Kullu, and Nichar districts of H.P. during 2017 and 2019. The climatic data were procured from Indian Meteorological centre, Shimla (H.P). The satellite images (Indian remote sensing satellite (IRS)-Linear imaging self-scanner (LISS)-IV sensor multispectral data) having resolution of 5.8m were procured from National Remote Sensing Centre Hyderabad, India. The relationship between the incidence of CL and Land use was analysed. Results: A total of 332 sand flies were collected. The transmission of CL was favoured by temperatures exceeding 20°C over a period of six months, specifically from April to September. The valley with an elevation ranging between 1000 to 1250 meters reported 90% of the CL cases. The correlation between CL incidence and land use patterns revealed a notable rise in barren land (8%) and scrub land (5%) from 2013 to 2017. Interpretation & conclusion: The findings reveals that specific meteorological conditions and land cover play significant role in determining the presence of sand flies in Himachal Pradesh. To effectively control CL, it is recommended to implement control programs primarily between June and September, focusing particularly on regions with elevations ranging from 1000 to 1250 meters.

Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments

Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors.

Integrating Remote Sensing and GIS-Based Map Analysis in Determining Spread of Built-Ups and Land-Use Dynamics of Terrain of Onitsha Metropolis, Anambra State, Nigeria

Land scarcity in most cases hampers development and encourages the misuse of land. The suitability of land must be considered before appropriating or allocating land for any use. Land supports the livelihood of every being on the Earth and therefore determines survival, success, and sustainability (sustainable living). This study aimed at integrating remote sensing and GIS-based analysis to determine the rate at which built-up areas have spread across the terrain of Onitsha Metropolis, Anambra State, Nigeria, and the dynamics of other land uses. This research involved both primary and secondary data. The primary data included measurements, direct field observations, and key informant interviews to understand people’s perceptions of the land use in the area. The secondary data included satellite images of the area obtained from USGS and analyzed using ArcGIS 10.2 for variations in the terrain of the Onitsha Metropolis; to determine the land use and land cover change (LULCC) of the Onitsha Metropolis over 40 years, published and unpublished articles and books were also consulted. The geological analysis of the study showed that the area of the Ogwashi/Asaba formation is 318.57 km2; the areas of the Nanka sands and Bende-Ameke are 423.07 km2 and 259.42 km2, respectively. The Nanka sands and Bende-Ameke formations are best suited for engineering construction purposes, while the Ogwashi/Asaba formation is suitable for agriculture and should be designated as a buffer zone or park. However, due to the unavailability of land as a result of the growing population and the proximity of the area to the city center, the area is being encroached upon, and a large area (about 30.40%) has been converted to built-up areas as of 2022. Forecast analysis showed that if the trend continues, 158.28 km2 (49.68%) of the alluvium soils of the Ogwashi/Asaba formation will be covered with buildings by 2072. The geology and the terrain of the Onitsha Metropolis determine the soil characteristics and the land use suitability; mapping the geological formations and overlaying these with the land use and land cover change of the area revealed the extent of the encroachment on the Ogwashi/Asaba formation, which must be discouraged.

Monitoring Hazards in Dam Environments Using Remote Sensing Techniques: Case of Kulekhani-I Reservoir in Nepal

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects.

Advances in Optical and Thermal Remote Sensing of Vegetative Drought and Phenology

In recent decades, remote sensing of vegetative drought and phenology has gained considerable attention from researchers, leading to a significant increase in research activity in this area. While new drought indices are being proposed, there is also growing attention on how variations in phenology affect drought detection. This review begins by exploring the crucial role of satellite optical and thermal remote sensing technologies in monitoring vegetative drought. It presents common methods after revisiting the foundational concepts. Then, the review examines remote sensing of land surface phenology (LSP) due to its strong connection with vegetative drought. Subsequently, we investigate vegetative drought detection techniques that consider phenological variability and recommend approaches to improve the detection of vegetative drought, emphasizing the necessity to incorporate phenological metrics. Finally, we suggest potential future work and directions. Unlike other review papers on remote sensing of vegetative drought, this review uniquely surveys the comprehensive advancements in both detecting vegetative drought and estimating LSP through optical and thermal remote sensing. It also highlights the necessity and potential applications for these practices.

Assessment of Critical Environmental Risk Zones Around Wetlands in Colombo, Sri Lanka, Using Satellite Remote Sensing and Multi-criteria Decision Tools

Colombo, Sri Lanka’s commercial capital, is recognized as one of the first 18 global sites that were awarded the Ramsar Wetland City accreditation, and is a prime example of the intersection between urbanization and wetland conservation. Thus, this study aims to conduct an environmental risk assessment that can provide insights into the changes in biophysical properties driven by urbanization over the 2000–2020 period. The study used Geographical Information Systems and a remote sensing approach to assess critical environmental risk zones around urban freshwater lakes in the Colombo district of Sri Lanka, including Thalangama, an environmental protection area experiencing sub-urbanization. Satellite data (2000–2020) were employed, and spectral indices, including NDVI, NDBI, NDWI, NDMI, RVI and LST, were calculated. Intersecting areas with multiple risk factors were determined, and distances from these zones to lakes were calculated to generate spatially explicit risk maps using inverse distance weighting. The building density has consistently increased from 2000 to 2020 in all three sites, with ‘very high’ density areas expanding notably. NDVI reveals a gradual increase in ‘very high’ categories until 2015. Plant-water stress analysis shows significant water stress in 2015, and water body areas have decreased after 2010 in all the sites. Multi-criteria assessment identifies critical environmental risk zones around urban lakes, indicating high vulnerability to environmental damage and degradation. Overall, the study emphasizes the importance of leveraging data and risk analysis to inform decision-making processes in environmental management. By doing so, policymakers can enhance the effectiveness of conservation measures and promote the long-term sustainability of natural resources.

Earth System Sciences: Integrating Remote Sensing and Hybrid Deep Learning to Develop a Legal Framework for Surrogacy Rights

Earth System Sciences: Integrating Remote Sensing and Hybrid Deep Learning to Develop a Legal Framework for Surrogacy Rights

Surface Water Monitoring Using GIS and Remote Sensing applications: Case study in Bau Sau, Cat Tien National Park

This study was conducted at Bau Sau, Cat Tien National Park, in Vietnam. Bau Sau is Vietnam's second wetland site out of a total of 9 Ramsar sites that have been recognized by the Ramsar Convention Secretariat in Vietnam. The extraction of surface water is carried out in three methods, including object-based classification and the calculation of NDWI and MNDWI indices. After calculating the overall accuracy of all three methods, the method with the highest percentage will be chosen for extracting the surface water boundary for the study area. With an overall accuracy of 90%, the NDWI index was used for extracting the water surface map in the period 2015–2024. The results also showed that the surface water area in Bau Sau was 42.15 hectares in 2015, 41.89 hectares in 2020, and 41.51 hectares in 2024. The fluctuation in the surface water area during 2015-2024 is clearly visible, showing a decrease of 0.64 hectares. The fluctuation is attributed to the vegetation communities, with the proliferation of Cyperus cephalotes being the most notable. Cat Tien National Park managers need to pay attention and take early measures in the near future to avoid impacting the habitat of some animal species in this area.

Aproximación al conocimiento de las estructuras de almacenaje en el asentamiento semiurbano de la Antigua Ciudad de Quilmes (Tucumán-Argentina)

The role of storage strategies throughout history is unquestionable. The old city of Quilmes offers features that indicate a possible emphasis placed on the construction of structures oriented to that end. The objectives of this research are: (1) to carry out the first systematic survey of the semi-urban system of the Old City of Quilmes; (2) differentiate between structures of diverse function through morphological clues; (3) identify and record structures traditionally associated with storage; and (4) estimate the area occupied by the aforementioned structures and their volumetric capacity. For this, a non-invasive geoarchaeological approach is proposed through the use of remote sensors and digital models, accompanied by statistical analysis. The set of information has made it possible to establish that the structures similar to storage use would cover a total area estimated at 26,146 m2 with an estimated volume of 43,761 m3 and probably contributed to improving food security during the period of indigenous resistance (1535-1667). This constitutes the first systematic approach to this subject.