Fusion of Radar and Optical Data for Land Cover Classification Using Machine Learning Approach

This study evaluates the advantages of combining traditional space borne optical data with longer wavelengths of radar for land cover mapping. Land cover classification was carried out using Optical, radar data and combination of both for the Bardiya district using Random Forest algorithm. The fusion of optical and radar shows better land cover discrimination with 96.98% overall accuracy in compared to using radar data and optical data separately with overall accuracy of 69.2% and 95.89% respectively. Additionally, the qualitative result demonstrates that the combined utilization of optical and radar imagery yields useful land cover information over those obtained using either type of image on its own.

A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands

Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ecosystems is constrained by unique challenges specific to their environmental conditions, leading to high inaccuracies when applying biomass estimation techniques developed for other ecosystems. This study reviews and highlights advantages and limitations of the various techniques and sensors, including optical, RADAR and LiDAR, that have been extensively used to estimate AGB and assess CS with RS data. Other new methods are introduced and discussed as well. Finally, the study highpoints the need for further work to fill the gaps and overcome limitations in using these emerging techniques for precision carbon management. Geo-spatial technologies are shown to be a valuable tool for estimating carbon sequestered especially in difficult and remote areas such as arid land.

Using remote sensing for monitoring of reforestation in boreal forest

Оценка лесовосстановления по спутниковым снимкам и создание системы мониторинга является важной задачей на сегодняшний день. Российские и зарубежные ученые проводят исследования в этом направлении, но анализ лесовосстановления является сложной темой исследования в отличие от выявления вырубок и гарей по спутниковым снимкам. Лесовосстановление также является сложным, многофакторным процессом, зависящим от множества факторов. Данная статья описывает мировой опыт создания различных методик для мониторингалесовосстановления, используя различные подходы анализа данных и сенсоры, установленные на спутниках. В рамках статьи рассмотрено применение оптических, радарных снимков и данных, полученных с лидарных сенсоров. Это попытка структурировать накопленный опыт в данной сфере и сгруппировать разработанные методики для анализа их преимуществ и недостатков. Тип сенсора определяет длительность периода мониторинга. Радарные данные позволяют определять процесс лесовосстановления до 60 лет в отличие от оптических сенсоров, которые имеют значительно меньший период оценки лесовосстановления. Применение радарных данных может быть ограничено стоимостью работы и сложностью обработки радарных данных, поэтому использование тех или иных методик может иметь финансовые ограничения. Данный обзор показывает все основные методы оценки лесовосстановления. Assessment of reforestation using satellite images and creation of a monitoring system is an important task today. Russian and foreign scientists are conducting research in this direction, but the analysis of reforestation is a complex topic of research in contrast to the detection of cuttings and burned areas by satellite images. The process of reforestation is a complex, multi-factor process depending on many factors. This article describes the world experience of creating different methods for monitoring forest regeneration and uses different approaches to data analysis and sensors installed on satellites. In the framework of article was considered using of optical, radar images and data obtained from lidar sensors. This is an attempt to structure the accumulated experience in this field and group the developed methods to analyze their advantages and disadvantages. Data from different sensors have different monitoring period. Radar data allow determining the process of reforestation up to 60 years in contrast to optical sensors, which have a much shorter period of reforestation assessment. The using of radar data were limited by the cost of operation and complexity of radar data processing and using of certain techniques may have financial limitations. This review showed all the main methods of assessment of reforestation.

Real-Time Hindrance Detection Prototype in Internal Atmosphere Expending Ultrasonic Sensors

Currently, ultrasonic sensors are extensively used in voluminous applications, for instance: robotics solicitations, unmanned vehicles, medical presentations, etc. In which remoteness measurement of an object is one of the most important issues. This can be achieved using a variety of sensors: Optical, Radar, IR, Ultrasonic, etc. Measuring distance using ultrasonic sensors might be the cheapest and its reliability is probably acceptable. In this paper we talk about the employments of these sensors to plan a convenient gadget helping the outwardly debilitated stay away from obstructions on their ways by sounds.

Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot

Since the launch of Landsat-1 in 1972, the scientific domain of geo-information has been incrementally shaped through different periods, due to technology evolutions: in devices (satellites, UAV, IoT), in sensors (optical, radar, LiDAR), in software (GIS, WebGIS, 3D), and in communication (Big Data). Land Cover and Disaster Management remain the main big issues where these technologies are highly required. Data fusion methods and tools have been adapted progressively to new data sources, which are augmenting in volume, variety, and in quick accessibility. This Special Issue gives a snapshot of the current status of that adaptation, as well as looking at what challenges are coming soon.