Remote Sensing [Working Title]最新文献

{"title":"Application of UAV Remote Sensing in Monitoring Banana Fusarium Wilt","authors":"H. Ye, Wenjiang Huang, Shanyu Huang, Chaojia Nie, Jiawei Guo, B. Cui","doi":"10.5772/intechopen.99950","DOIUrl":"https://doi.org/10.5772/intechopen.99950","url":null,"abstract":"Fusarium wilt poses a current threat to worldwide banana plantation areas. To treat the Fusarium wilt disease and adjust banana planting methods accordingly, it is important to introduce timely monitoring processes. In this chapter, the multispectral images acquired by unmanned aerial vehicle (UAV) was used to establish a method to identify which banana regions were infected or uninfected with Fusarium wilt disease. The vegetation indices (VIs), including the normalised difference vegetation index (NDVI), normalised difference red edge index (NDRE), structural independent pigment index (SIPI), red-edge structural independent pigment index (SIPIRE), green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), anthocyanin reflectance index (ARI), and carotenoid index (CARI), were selected for deciding the biophysical and biochemical characteristics of the banana plants. The relationships between the VIs and those plants infected or uninfected with Fusarium wilt were assessed using the binary logistic regression method. The results suggest that UAV-based multispectral imagery with a red-edge band is effective to identify banana Fusarium wilt disease, and that the CIRE had the best performance.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data","authors":"Ronak Jain","doi":"10.5772/intechopen.99046","DOIUrl":"https://doi.org/10.5772/intechopen.99046","url":null,"abstract":"Geological studies have been performed using the Band Ratios (BR), Relative Band Depth (RBD), Mineral Indices (MI), Principal Component Analysis (PCA), Independent Component Analysis (ICA), lithological and mineral classification techniques from Short-Wave Infrared (SWIR) and Thermal Infrared (TIR) data. The chapter aims to delineate various geological units present in the area using the combination of SWIR and TIR ASTER bands through the Feature-Oriented Principal Component Selection (FPCS) technique. Different BRs and RBDs were applied to map the minerals having Al-OH and Mg-OH compounds with the chemical composition of clay (kaolinite, smectite), mica (sericite, muscovite, illite), ultramafic (lizardite, antigorite, chrysotile), talc, and carbonate (dolomite) from SWIR bands. The MI was used to map quartz-rich, mafic/ultramafic, and carbonate rocks using TIR bands. The BRs, RBDs, and MIs mapped the geological units but every single greyscale image showed a variety of features. To compile these features False Color Composite (FCC) was prepared by the combination of RBDs and MIs in the R:G:B channels which demarked various geological units to a larger extent present in the region. To overcome the limitation, the FPCS technique was applied with the integration of all BRs, RBDs, and MIs. The FPCS technique extracts valuable information from different input bands and shifts the information in the first few bands. The generated eigenvalues and eigenvectors represented the retrieved information in the specific band. The loadings of the eigenvector were used for the selection of the different brands to create the FCC for the delineation of geological strata. The best discrimination was made by the selection of FPCS1, FPCS3, and FPCS6 which differentiated all the geological units like ultramafics, dolomites, thin bands of talc, and muscovite and illite (as phyllite and mica-schist), silica-rich rocks (as quartzite), and granite outcrops.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115512625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Enhancement Methods for Remote Sensing: A Survey","authors":"N. H. Kaplan, I. Erer, D. Kumlu","doi":"10.5772/intechopen.98527","DOIUrl":"https://doi.org/10.5772/intechopen.98527","url":null,"abstract":"The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests","authors":"H. Omar, Thirupathi Rao Narayanamoorthy, Norsheilla Mohd Johan Chuah, Nur Atikah Abu Bakar, M. A. Misman","doi":"10.5772/INTECHOPEN.98952","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.98952","url":null,"abstract":"Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Remote Sensing of Planetary Space Environment","authors":"F. He, Z. Yao, Yong Wei","doi":"10.5772/intechopen.98427","DOIUrl":"https://doi.org/10.5772/intechopen.98427","url":null,"abstract":"Planetary science is the scientific investigations of the basic characteristics and the formation and evolution processes of the planets, moons, comets, asteroids and other minor bodies of the solar system, the exoplanets, and the planetary systems. Planetary scientific research mainly depends on deep space exploration, and it is highly interdisplinary and is built from Earth science, space science, astronomy and other relevant disciplines. Planetary space, a critical region of mass and energy exchange between the planet and the interplanetary space, is an integral part of the planetary multi-layer coupling system. Atmospheres of different compositions and plasmas of different densities and energies exist in planetary space, where mass transportation at different temporal and spatial scales and various energy deposition and dissipation processes occur. Optical remote sensing overcomes the difficulties of capturing global views and distinguishing spatiotemporal variations in in-situ particle and field detections. This chapter introduces the principles and applications of optical remote sensing in planetary science. The first ground-based planetary observatory in China, the Lenghu Observation Center for Planetary Sciences, will be introduced in detail. Future development of optical remote sensing platforms in Chinese planetary exploration program will also be introduced.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133772477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lidar Observations in South America. Part I - Mesosphere and Stratosphere","authors":"E. Landulfo, A. Cacheffo, Alexandre Calzavara Yoshida, Antonio Arleques Gomes, F. Lopes, G. A. Moreira, Jonatan João da Silva, V. Andrioli, A. Pimenta, Chi Wang, Jiyao Xu, M. Martins, P. Batista, H. Barbosa, D. Gouveia, B. B. González, F. Zamorano, E. Quel, C. Pereira, E. Wolfram, F. Casasola, Pablo Facundo Orte, J. Salvador, J. Pallotta, L. Otero, M. Prieto, P. Ristori, S. Brusca, J. R. Estupiñan, E. S. Barrera, J. Antuna-Marrero, R. Forno, M. Andrade, J. Hoelzemann, A. Guedes, C. Sousa, Daniel C.F. dos S. Oliveira, Ediclê de Souza Fernandes Duarte, Marcos Paulo Araújo da Silva, Renata Silva Santos","doi":"10.5772/INTECHOPEN.95038","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.95038","url":null,"abstract":"South America covers a large area of the globe and plays a fundamental function in its climate change, geographical features, and natural resources. However, it still is a developing area, and natural resource management and energy production are far from a sustainable framework, impacting the air quality of the area and needs much improvement in monitoring. There are significant activities regarding laser remote sensing of the atmosphere at different levels for different purposes. Among these activities, we can mention the mesospheric probing of sodium measurements and stratospheric monitoring of ozone, and the study of wind and gravity waves. Some of these activities are long-lasting and count on the support from the Latin American Lidar Network (LALINET). We intend to pinpoint the most significant scientific achievements and show the potential of carrying out remote sensing activities in the continent and show its correlations with other earth science connections and synergies. In Part I of this chapter, we will present an overview and significant results of lidar observations in the mesosphere and stratosphere. Part II will be dedicated to tropospheric observations.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lidar Observations in South America. Part II - Troposphere","authors":"E. Landulfo, A. Cacheffo, Alexandre Calzavara Yoshida, Antonio Arleques Gomes, F. Lopes, G. A. Moreira, Jonatan João da Silva, V. Andrioli, A. Pimenta, Chi Wang, Jiyao Xu, M. Martins, P. Batista, H. Barbosa, D. Gouveia, B. B. González, F. Zamorano, E. Quel, C. Pereira, E. Wolfram, F. Casasola, Pablo Facundo Orte, J. Salvador, J. Pallotta, L. Otero, M. Prieto, P. Ristori, S. Brusca, J. R. Estupiñan, E. S. Barrera, J. Antuna-Marrero, R. Forno, M. Andrade, J. Hoelzemann, A. Guedes, C. Sousa, Daniel C.F. dos S. Oliveira, Ediclê de Souza Fernandes Duarte, Marcos Paulo Araújo da Silva, Renata Silva Santos","doi":"10.5772/INTECHOPEN.95451","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.95451","url":null,"abstract":"In Part II of this chapter, we intend to show the significant advances and results concerning aerosols’ tropospheric monitoring in South America. The tropospheric lidar monitoring is also supported by the Latin American Lidar Network (LALINET). It is concerned about aerosols originating from urban pollution, biomass burning, desert dust, sea spray, and other primary sources. Cloud studies and their impact on radiative transfer using tropospheric lidar measurements are also presented.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129751641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India, Using MODIS Time Series Data of 2001-2011","authors":"D. Dutta, Akanksha Balha, Prabir Kumar Das, Pragyan Jain, Libeesh Lukose, W. Akram","doi":"10.5772/intechopen.94282","DOIUrl":"https://doi.org/10.5772/intechopen.94282","url":null,"abstract":"The forest area of Assam State is known for its rich biodiversity. In the present study, the disturbance regime within the Assam forest area caused by periodic flood and forest fire, was assessed using the Moderate Resolution Imaging Spectroradiometer (MODIS) time-series (2001–2011) data. The MODIS Global Disturbance Index (MGDI) images were generated using MODIS derived Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) images. The temporal intensity of flood and forest fire in sixteen representative forests was analyzed to develop the MGDI based thresholds for detecting the disturbed area. The threshold for the non-instantaneous disturbance, i.e. flood, was found to be 107% whereas it was 111% for instantaneous disturbance, i.e. forest fire. The thresholds were applied on the MGDI images to delineate disturbed caused by flood and fire, separately for each year. The time-series disturbance areas were integrated over the years (2001–2011) to generate the classified disturbance prone maps.","PeriodicalId":430576,"journal":{"name":"Remote Sensing [Working Title]","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131293083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}