Earth Environmental Monitoring Research Articles

A Siamese Swin-Unet for image change detection

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

Research on Remote Sensing User Demand Mining Analysis Method

A method of mining and analyzing remote sensing user needs is presented in this paper. With the increasing number and types of satellites in China, satellites are more and more widely used in national economic construction, which can be used in land and resources census, emergency natural disaster relief, urban construction management, earth environment monitoring and so on. Therefore, the number of remote sensing users expand quickly and the specific content of remote sensing observation needs and remote sensing image data needs is diverse. How to find out the user’s remote sensing resources and data use rules from the large number of remote sensing needs is a key problem. The difficulty lies in that the rules representing the relationship between demand parameters are recessive rather than explicit, which can only be found by complex data analysis, especially under the condition of large amount of data. Data mining technology can find potential as well as valuable rules and knowledge from massive data. It is an effective tool to analyze massive data and an effective technical means to solve the above problems. In this paper, the association rules mining algorithm is adopted to mine and analyze mass of remote sensing user demand data, and find the potential association rules between the demand source, regional scope, time, demand type and other parameters.

Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

A NOVEL INDEX FOR TEMPORAL STABILITY ANALYSIS IN SPACE AND TIME OF SAR-DERIVED SCENES

Abstract. Detecting temporal changes is one of the most important applications of satellite sensors. In recent years, the increasing availability of regular time-series of SAR imagery, provided by the Sentinel-1 mission of the European Space Agency (ESA), has drawn increasing attention to these techniques, especially in earth environment monitoring and risk management. Within this paper, a coherent change detection analysis for evaluating the risk due to movements of dunes and sand sheets in desertic areas is proposed. To this purpose, we introduce a novel, coherence-based index, named Temporal Stability Index (TSI), that is suited for characterizing the percentage of stability of a target with time. TSI maps can be generated over areas as wide as hundreds of kilometers, in a short time, and mostly by exploiting available software tools (plus some simple coding). The information provided is complementary to the average of the short-term coherence, here shown. Results of analysis performed on two desertic regions (the United Arab Emirates and Egypt) document the usefulness of TSI for the identification of dune movements and areas subject to sand accumulation, supporting risk mitigation measures.

Deep learning in environmental remote sensing: Achievements and challenges

Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.

Active Asteroid (6478) Gault: A Blue Q-type Surface below the Dust?

Abstract We present near-infrared spectroscopy of the sporadically active asteroid (6478) Gault collected on the 3 m NASA/Infrared Telescope Facility observatory in late 2019 March/early April. Long-exposure imaging with the 0.5 m Near Earth Environment Monitoring T05 telescope and previously published data simultaneously monitored the asteroid activity, providing context for our measurements. We confirm that Gault is a silicate-rich (Q- or S-type) object likely linked to the (25) Phocaea collisional family. The asteroid exhibits substantial spectral variability over the 0.75–2.45 μm wavelength range, from unusual blue (s′ = −13.5 ± 1.1% μm−1) to typical red (s′ = +9.1 ± 1.2% μm−1) spectral slope, that does not seem to correlate with activity. Spectral comparisons with samples of ordinary chondrite meteorites suggest that the blue color relates to the partial loss of the asteroid dust regolith, exposing a fresh, dust-free material at its surface. The existence of asteroids rotating close to rotational break-up limit and having similar spectral properties as Gault further supports this interpretation. Future spectroscopic observations of Gault, when the tails dissipate, will help further testing of our proposed hypothesis.

A New Paradigm in Earth Environmental Monitoring with the CYGNSS Small Satellite Constellation

A constellation of small, low-cost satellites is able to make scientifically valuable measurements of the Earth which can be used for weather forecasting, disaster monitoring, and climate studies. Eight CYGNSS satellites were launched into low Earth orbit on December 15, 2016. Each satellite carries a science radar receiver which measures GPS signals reflected from the Earth surface. The signals contain information about the surface, including wind speed over ocean, and soil moisture and flooding over land. The satellites are distributed around their orbit plane so that measurements can be made more often to capture extreme weather events. Innovative engineering approaches are used to reduce per satellite cost, increase the number in the constellation, and improve temporal sampling. These include the use of differential drag rather than propulsion to adjust the spacing between satellites and the use of existing GPS signals as the science radars’ transmitter. Initial on-orbit results demonstrate the scientific utility of the CYGNSS observations, and suggest that a new paradigm in spaceborne Earth environmental monitoring is possible.

Is the vision of the Earth Observation Summit realizable?

Abstract At the Earth Observation Summit in 2003 at the US Department of State, environmental ministers from more than 30 countries joined three US cabinet secretaries to plan the creation of a system for international sharing of data about the atmosphere, the oceans and the land. The meeting grew in part out of commitments by leaders at a G-8 summit meeting in France to build an integrated global earth environmental monitoring system. Opportunities and problems both figure prominently in implementation of the Summit's vision. The challenges include who pays for infrastructure, training, and administration; whether to control data access; how to include the private sector; and whether problems of collective action will plague the effort.

Calibration Systems for Earth's Environment Monitoring and Plans for Related Studies on Onformation Science

Calibration Systems for Earth's Environment Monitoring and Plans for Related Studies on Onformation Science

Verifiacation Experiment concerning Earth Environment Monitoring by Microwave Radiometer and Future Technical Items

Verifiacation Experiment concerning Earth Environment Monitoring by Microwave Radiometer and Future Technical Items

Global Monitoring of Earth Environment from Space

Global Monitoring of Earth Environment from Space