scholarly journals Geostatistical Downscaling of Coarse Scale Remote Sensing Data and Integration with Precise Observation Data for Generation of Fine Scale Thematic Information

2013 ◽  
Vol 29 (1) ◽  
pp. 69-79 ◽  
Author(s):  
No-Wook Park
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Fine Scale ◽  
Coarse Scale ◽  
Sensing Data ◽  
Thematic Information

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

Validation of GEMS L2 products using ground-based remote sensing data including PANDORA measurements

2020 ◽  
Author(s):  
KangHo Bae ◽  
Chang-Keun Song ◽  
Sang-Seo Park ◽  
Sang-Woo Kim ◽  
Jhoon Kim ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Public Service ◽  
Trace Gases ◽  
Remote Sensing Data ◽  
Observation Data ◽  
South East Asian Region ◽  
Sensing Data ◽  
East Asian Region ◽  
Asian Region ◽  
Ambient Surface

<p>Launch of the Geostationary Environmental Monitoring Spectrometer (GEMS) is scheduled in early 2020 to support public service and science related to air quality and climate by providing diurnal variation of concentrations of trace gases and aerosols with high spatial/temporal resolution over Asian region. We will introduce GEMS validation methodology in parallel with a strategy for integration of existed independent measurements like as low-orbit satellite, ground-based remote sensing, and ambient surface observation data. As collections of nearly real-time and quality-assured data from existing ground-based networks are still in great needs for GEMS validation, efforts to expand observational infra-structure have been going on. Currently, two PANDORA instruments started to be in operation at Seoul and Ulsan in Korea, and PANDORA Asian Network initiated by NIER, Korea will be expanded into South East Asian region beyond Korea, China and Japan in addition. In this study, we especially try to validate the initial L2 product of GEMS gathered during IOT period by utilizing PANDORA data and other ground remote sensing data as well so that availability and feasibility of those ground observations could be assessed for GEMS validation.</p><p> </p><p>Keywords: GEMS validation, ground-based remote sensing data, PANDORA</p>

scholarly journals DROUGHT AND FINE FUEL MOISTURE CODE EVALUATION: AN EARLY WARNING SYSTEM FOR FOREST/LAND FIRE USING REMOTE SENSING APPROACH

2014 ◽  
Vol 9 (2) ◽  
Author(s):  
Yenni Vetrita ◽  
Indah Prasasti ◽  
Nanik Suryo Haryani ◽  
M Priyatna ◽  
M Rokhis Khomarudin
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Early Warning ◽  
Warning System ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Fuel Moisture ◽  
Forest Land ◽  
Sensing Data ◽  
Two Parameters

This study evaluated two parameters of fire danger rating system (FDRS) using remote sensing data i.e. drought code (DC) and fine fuel moisture code (FFMC) as an early warning program for forest/land fire in Indonesia. Using the reference DC and FFMC from observation data, we calculated the accuracy, bias, and error. The results showed that FFMC from satellite data had a fairly good correlation with FFMC observations (r=0.68, bias=7.6, and RMSE=15.7), while DC from satellite data had a better correlation with FFMC observations (r=0.88, bias=49.91, and RMSE=80.22). Both FFMC and DC from satellite and observation were comparable. Nevertheless, FFMC and DC satellite data showed an overestimation values than that observation data, particularly during dry season. This study also indicated that DC and FFMC could describe fire occurrence within a period of 3 months before fire occur, particularly for DC. These results demonstrated that remote sensing data can be used for monitoring and early warning fire in Indonesia.

scholarly journals Application of Remote Sensing, GIS, and GPS Technology on Environment and Earth's Natural Resources Management An Introduction

2007 ◽  
Vol 6 (1) ◽  
pp. 96-117
Author(s):  
N. Nandini ◽  
Aboud S. Jumbe ◽  
Sucharita Tandon ◽  
Sunita N.
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Crop Monitoring ◽  
Sensing Data ◽  
Data Generator ◽  
Information Updates ◽  
Thematic Information ◽  
Meteorological Satellite

Remote sensing data have been used to derive thematic information of various natural resources and environment.The type and level of information extracted depends on the expertise of the analyst and what he/she is looking for in the data.An application in remote sensing is the practical use to which a series of aerial satellite images are put. The application of remote sensing or earth observation techniques to atmospheric, Earth and environmental sciences can vary according to the final user's requirements.The utilization of remote sensing data can be broadly classified into three categories as a baseline data generator for a variety of environmental resources; as a tool to monitor change detection, Environmental monitoring, and for mapping purposes. Different environmental applications require different frequencies of information updates for monitoring to be effective. Environment phenomena such as weather systems, natural hazards, and other rarely extreme events such as tsunamis; pollution or oceanographic events are very dynamic and rapidly develop over minutes and hours. Therefore for satellite data to be useful in their analysis imaging frequency and data delivery has to be atleast several times a day. At present only low spatial resolution meteorological satellite data can meet this need. Other applications such as crop monitoring require better spatial detail but rates of change occur only over a matter of weeks and therefore image updates need not be more frequent than weekly or monthly. This data can be processed, refined, and managed with the use of advanced tools such as Geographic Information System(GIS) and Geographic Positioning System(GPS).

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

scholarly journals Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

2020 ◽  
Vol 12 (6) ◽  
pp. 972 ◽  
Author(s):  
Yinyi Cheng ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Jining Yan
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Data Integration ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Integration Framework ◽  
Sensing Data ◽  
Storage Method

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

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