Multi-Source Ocean Remote Sensing Data Fusion and Applications

This paper aims to measure and characterise urban sprawl development in Kuala Lumpur city using leapfrog geospatial indices. The researcher utilised remote sensing satellite data such as Landsat and Spot images for two different times. The remote sensing application was subsequently integrated with GIS database to detect changes and analyse the pattern of growth for urban areas in Kuala Lumpur. From the finding of land use change detection, the leapfrog sprawl index was calculated by using geospatial indices formula. The results proved that Kuala Lumpur is the most highly developed city in Malaysia with new development is leapfrogging towards the periphery of the city and infill development pattern seemingly increase vastly filling up the leapt areas cause by leapfrog sprawl. Improper planning will create this type of urban sprawl that is predicted to expand beyond the border towards other states locating adjacent to Kuala Lumpur which is now called Greater Kuala Lumpur. The current scenario has become an absolute threat towards Malaysia planning goal to achieve sustainable urban planning development.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

As one of the efficient means of data acquisition, remote sensing data play an important role in many areas of life through the development of technology and the improvement of spatial resolution. In this paper, the application of remote sensing data on dynamic monitoring of coastline in the Yellow River Delta was analyzed. The results indicate that the using of remote sensing data was more scientific and efficient than the traditional methods, and the analysis results can provide a theoretical basis for the ecological protection in the Yellow River Delta.

Purpose: Covering current state of the art in the field of application of remotely sensed data in crop quality improvement. Methodology/Approach: Systematic literature review using novel text mining techniques. Findings: Relevance of topic, measured by number of relevant studies, is rising, best performing input data types and modelling techniques are identified. Research Limitation/Implication: Review to a certain point of time in a rapidly evolving field of research. Originality/Value of paper: There was no similar review article on the topic at the time of conducting this research.

Geographic Information Systems (GIS) and Remote Sensing (RS) applications are becoming an important issue for the territorial management, governmental and research projects, and for many fields of our society. A characteristic of such applications is the displaying of successive layers of information that, in some cases, may overlap areas of the displayed images that are eventually never showed to the final user of the application. Even though these overlapped areas are of null interest, the coding of these images considers the complete area of the image, and thus the coding performance of the compression system is penalized. This paper introduces a novel use of the Region Of Interest (ROI) coding techniques to overcome the drawbacks of the map overlapping in GIS and RS applications. The proposed approach is based on a ROI coding method defined for the JPEG2000 standard that efficiently improves the coding performance and keeps JPEG2000 compliance.

This paper provides an overview of recent developments in remote and proximal sensing technologies and their basic applicability to various aspects of forest operations. It categorises these applications according to the technologies used and considers their deployment platform in terms of their being space-, airborne or terrestrial. For each combination of technology and application, a brief review of the state-of-the-art has been described from the literature, ranging from the measurement of forests and single trees, the derivation of landscape scale terrain models down to micro-topographic soil disturbance modelling, through infrastructure planning, construction and maintenance, to forest accessibility with ground and cable based harvesting systems. The review then goes on to discuss how these technologies and applications contribute to reducing impacts on forest soils, cultural heritage sites and other areas of special value or interest, after which sensors and methods necessary in autonomous navigation and the use of computer vision on forest machines are discussed.

This paper provides an overview of recent developments in remote and proximal sensing technologies and their basic applicability to various aspects of forest operations. It categorises these applications according to the technologies used and considers their deployment platform in terms of their being space-, airborne or terrestrial. For each combination of technology and application, a brief review of the state-of-the-art has been described from the literature, ranging from the measurement of forests and single trees, the derivation of landscape scale terrain models down to micro-topographic soil disturbance modelling, through infrastructure planning, construction and maintenance, to forest accessibility with ground and cable based harvesting systems. The review then goes on to discuss how these technologies and applications contribute to reducing impacts on forest soils, cultural heritage sites and other areas of special value or interest, after which sensors and methods necessary in autonomous navigation and the use of computer vision on forest machines are discussed.

The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

With the development of science and technology, remote sensing technology is changing the face of traditional surveying and mapping industry gradually. Remote sensing technology refers to a modern comprehensive technology that receives electromagnetic wave information from various ground objects on the earth's surface from high altitude or outer space, and scans, photographs, transmits and processes the information, thereby remotely controlling, measuring and identify various ground objects and phenomena on the earth's surface. In recent years, with the country's investment in satellite industry, remote sensing has gradually attracted people's attention, and colleges and universities have gradually strengthened the importance of remote sensing courses. Based on introducing the characteristics of the course of remote sensing principle and application, combining with the requirements of the course construction scheme and the teaching status of higher vocational colleges, this paper discusses the construction ideas of the course of remote sensing principle and application from the aspects of optimizing the course content, changing the teaching methods, and strengthening the practice links.

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

Increased population, unhealthy agricultural practices, indiscriminate land clearing and illegal structures have led to an increase of erosion in Nigeria and Lagos State in particular. This research focused on identifying land use/land cover changes in Eti-Osa LGA of Lagos State and estimating the actual erosion risk using Remote Sensing and Geography Information System. In addition, this research evaluated the perception of communities within the study area with the view to understanding the risk involved in erosion. Maximum Likelihood Algorithm was the classification method applied on the Landsat imageries (1986-2016) to identify the changes on the land use/land cover types. Analysis of Variance (ANOVA) was used to evaluate the perception of communities within the study area and Revised Universal Soil Loss equation (RUSLE) model was used to estimate the actual erosion risk. The result showed that the sediment yield of the study area was estimated to be between 0 to 48ton/ha/yr. The estimated soil losses were higher in Eti-Osa West compared to other parts of Iru/Victoria Island, and Ikoyi/Obalende areas which recorded low losses. Land uses mostly affected by very high and severe erosion are the bare soils and the crop lands having about 3% to 4% respectively. It can be concluded that rainfall, lack of cover for the surface soil were the major causes of soil loss in the study area.

Flood is one of the common destructive acts of nature which is spread globally and causes extensive loss to lives and properties. Therefore, the management of flood is very important in order to limit the damages it causes. Applications of remote sensing and GIS techniques have been used extensively with various combination and permutations to find out causes and spatial extent of flood and provide solutions to minimize its effects. The present study is about the Jiadhal River which is one of the major sub-tributaries of the Brahmaputra River. Flood occurs in this river basin due to the shifting of the river course during the rainy season and also because of the embankment failure. So, an attempt has been made in this paper with an objective to identify the flood hazard zones of the Jiadhal River basin area using remote sensing and geographical information system. Elevation data from SRTM DEM have been processed for altitudinal zones, slope class, flow accumulation, drainage, and proximity to drainage. Data on land use and land cover and geomorphologic unit from different sources have been integrated with geomorphometric data using weighted overlay method in ArcGIS 10.3. Thus, a flood hazard zone map has been created based on the weighted overlay method. The flood hazard zones thus demarcated are broadly divided into five categories – (a) flood-free zones, (b) low hazard zones, (c) moderate hazard zones, (d) high hazard zones, and (e) severe hazard zones.KeywordsFlood managementRemote sensingGISJiadhal River

These instructions give you basic guidelines for preparing papers for conference proceedings. In China , the techonology of spectral information , especially the IR spectral information are attractive in airborne remote sensing field. The main recent progresses of some typical airborne spectral remote sensing systems are introduced in this article .

Tropical forest management requires could be improved through the use of current technologies including remote sensing and Geographic Information System (GIS). In this paper, we characterize and evaluate forest management patterns and relate this to modern technologies such as geographical information systems and remote sensing. We further examine the application of these modern technologies in tropical forestry and conservation. To achieve this, we carried out a comprehensive survey of published scientific literature obtained through Web of Science, Mendeley, Researchgate and Google Scholar. We observed that, the relationships between forestry management, modern technologies have shifted over time. These have depended on how management activities such as planting and harvesting, interact with other drivers and disturbances (fire, pests and diseases) to influence the adaptive capacity of forests. Forest management and new technologies are interrelated because the technologies support management actions; hence contribute to global forest resources management and conservation.