Advances in Remote Sensing最新文献

{"title":"Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana","authors":"Y. Twumasi, E. Merem, J. Namwamba, O. S. Mwakimi, T. Ayala-Silva, D. B. Frimpong, Z. H. Ning, A. Asare-Ansah, Jacob B. Annan, J. Oppong, P. Loh, F. Owusu, Valentine Jeruto, B. Petja, R. Okwemba, Joyce McClendon-Peralta, Caroline O. Akinrinwoye, Hermeshia J. Mosby","doi":"10.4236/ars.2021.104009","DOIUrl":"https://doi.org/10.4236/ars.2021.104009","url":null,"abstract":"This \u0000study employs Landsat-8 Operational Land Imager (OLI) thermal infrared \u0000satellite data to compare land surface temperature of two cities in Ghana: \u0000Accra and Kumasi. These cities have human populations above 2 million and the \u0000corresponding anthropogenic impact on their environments significantly. Images were \u0000acquired with minimum cloud cover (ere used. The shapefiles of Accra and Kumasi were used to extract from the \u0000full scenes to subset the study area. Thermal band data numbers were converted \u0000to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To \u0000determine the density of green on a patch of land, normalized difference \u0000vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 \u0000and Band 5. Land surface emissivity (LSE) was also calculated to determine the \u0000efficiency of transmitting thermal energy across the surface into the \u0000atmosphere. Results of the study show variation of temperatures between \u0000different locations in two urban areas. The study found Accra to have \u0000experienced higher and lower dry season and wet season temperatures, \u0000respectively. The temperature ranges corresponding to the dry and wet seasons \u0000were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to \u000030.9693oC respectively. Results \u0000of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from \u000026.4142oC to -0.898728oC. Among the reasons for the cities of Accra \u0000and Kumasi recorded higher than corresponding rural areas’ values can be \u0000attributed to the urban heat islands’ phenomenon.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123162209","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":"Fully Polarimetric Land Cover Classification Based on Markov Chains","authors":"G. Koukiou, V. Anastassopoulos","doi":"10.4236/ars.2021.103003","DOIUrl":"https://doi.org/10.4236/ars.2021.103003","url":null,"abstract":"A novel \u0000land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. \u0000The Cameron coherent target decomposition (CTD) is employed to \u0000characterize land cover pixel by pixel. Cameron’s CTD is employed since it \u0000provides a complete set of elementary \u0000scattering mechanisms to describe the physical properties of the \u0000scatterer. The novelty of the proposed land classification approach lies on the \u0000fact that the features used for classification are not the types of the \u0000elementary scatterers themselves, but the \u0000way these types of scatterers alternate from pixel to pixel on the SAR image. Thus, transition \u0000matrices that represent local Markov models are used as classification \u0000features for land cover classification. The classification rule employs only \u0000the most important transitions for decision making. The Frobenius inner product \u0000is employed as similarity measure. Ten different types of land cover are used \u0000for testing the proposed method. In this aspect, the classification performance \u0000is significantly high.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129238004","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":"Hyperspectral Reflectance Characteristics of Cyanobacteria","authors":"Terrence Slonecker, Brittany Bufford, J. Graham, K. Carpenter, Dan Opstal, N. Simon, Natalie C. Hall","doi":"10.4236/ars.2021.103004","DOIUrl":"https://doi.org/10.4236/ars.2021.103004","url":null,"abstract":"Potentially harmful cyanobacterial blooms are an emerging environmental \u0000concern in freshwater bodies worldwide. Cyanobacterial blooms are generally \u0000caused by high nutrient inputs and warm, still waters and have been appearing \u0000with increasing frequency in water bodies used for drinking water supply and \u0000recreation, a problem which will likely worsen with a warming climate. \u0000Cyanobacterial blooms are composed of genera with known biological pigments and \u0000can be distinguished and analyzed via hyperspectral image collection technology \u0000such as remote sensing by satellites, airplanes, and drones. Here, we utilize \u0000hyperspectral microscopy and imaging spectroscopy to characterize and \u0000differentiate several important bloom-forming cyanobacteria genera obtained in \u0000the field during active research programs conducted by US Geological Survey and \u0000from commercial sources. Many of the cyanobacteria genera showed differences in \u0000their spectra that may be used to identify and predict their occurrence, \u0000including peaks and valleys in spectral reflectance. Because certain cyanobacteria, such as Cylindrospermum or Dolichospermum, \u0000are more prone to produce cyanotoxins than others, the ability to differentiate these species may help target high priority \u0000waterbodies for sampling. These spectra may also be used to prioritize \u0000restoration and research efforts to control \u0000cyanobacterial harmful algal blooms (CyanoHABs) and improve water \u0000quality for aquatic life and humans alike.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114616297","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":"Fully Polarimetric Land Cover Classification Based on Hidden Markov Models Trained with Multiple Observations","authors":"Konstantinos Karachristos, G. Koukiou, V. Anastassopoulos","doi":"10.4236/ars.2021.103007","DOIUrl":"https://doi.org/10.4236/ars.2021.103007","url":null,"abstract":"A land \u0000cover classification procedure is presented utilizing the information content \u0000of fully polarimetric SAR images. The Cameron coherent target decomposition \u0000(CTD) is employed to characterize each pixel, using a set of canonical \u0000scattering mechanisms in order to describe the physical properties of the \u0000scatterer. The novelty of the proposed classification approach lies on the use \u0000of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. \u0000The motivation to this approach is the investigation of the alternation between \u0000scattering mechanisms from SAR pixel to pixel. Depending on the observations-scattering mechanisms and \u0000exploiting the transitions between the scattering mechanisms we decide \u0000upon the HMM-land cover type. The classification process is based on the \u0000likelihood of observation sequences been \u0000evaluated by each model. The performance of the classification approach \u0000is assessed my means of fully polarimetric SLC SAR data from the broader area of Vancouver, Canada and was found \u0000satisfactory, reaching a success from 87% to over 99%.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128022108","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":"Crop Calendar Mapping of Bangladesh Rice Paddy Field with ALOS-2 ScanSAR Data","authors":"Md. Rahedul Islam","doi":"10.4236/ars.2021.103008","DOIUrl":"https://doi.org/10.4236/ars.2021.103008","url":null,"abstract":"Rice \u0000paddy mapping with optical remote sensing is challenging in Bangladesh due to \u0000the heterogeneous cropping pattern, fragmented field size and cloud cover during the growing period. The \u0000high-resolution Synthetic Aperture Radar (SAR) sensor is the potential \u0000alternate to mapping rice area in Bangladesh. \u0000The L-band SAR sensor onboard Advanced Land Observing Satellite (ALOS) acquires multi-polarization and \u0000multi-temporal images are a very useful tool for rice area mapping. In \u0000this study, we used ALOS-2 ScanSAR dual (HH + HV) \u0000polarized time series data in the study area. We used orthorectification and \u0000slope corrected backscatter (sigma-naught) images and median filtering (3 × 3) \u0000window for image processing. The unsupervised classification with the k-means++ \u0000algorithm is used for initial clustering (20 categories) of images over the \u0000study area. The GPS location of rice paddy field with cropping pattern over \u0000study area uses for classifying the different rice-growing season from the \u0000k-means clustering data. The result is compared with the moderate resolution \u0000imaging spectroradiometer (MODIS) based rice area and national statistical \u0000agricultural yearbook statistics. The results show that, based on the MODIS \u0000based rice map, the rice fields can be mapped with a conditional Kappa value of \u00000.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The \u0000large commission error primarily came from confusion between wet season Aus \u0000rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their \u0000similar backscatter amplitudes and temporal similarities in the rice growing \u0000season. The relatively high rice mapping accuracy in this study indicates that \u0000the ALOS/PALSAR-2 data could provide useful information in rice cropping \u0000management in subtropical regions such Bangladesh.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127298775","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":"Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images","authors":"A. R. Hajalalaina, Arisetra Razafinimaro, Nicolas Ratolotriniaina","doi":"10.4236/ars.2021.103005","DOIUrl":"https://doi.org/10.4236/ars.2021.103005","url":null,"abstract":"Satellite images are considered reliable data that preserve land cover \u0000information. In the field of remote sensing, these images allow relevant \u0000analyses of changes in space over time through the use of computer tools. In \u0000this study, we have applied the “discriminant” change detection algorithm. In \u0000this, we have verified its effectiveness in multi-temporal studies. Also, we \u0000have determined the change in forest dynamics in the Ikongo district of \u0000Madagascar between 2000 and 2015. During the treatments, we have used the \u0000Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ \u0000for 2015. Thus, analyses carried out have allowed us to note that between \u00002000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests \u0000degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest \u0000conserved in 2010 disappeared. Furthermore, we have found that the discriminant \u0000algorithm is considerably efficient in terms of monitoring the dynamics of \u0000forest cover change.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"1083 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133241877","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":"Estimation of Poverty Based on Remote Sensing Image and Convolutional Neural Network","authors":"Peng Wu, Yumin Tan","doi":"10.4236/ars.2019.84006","DOIUrl":"https://doi.org/10.4236/ars.2019.84006","url":null,"abstract":"Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087501","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":"Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method","authors":"Zouhaier Ben Rabah, Hedi Garbia, E. Karray, Kais Tounsi, A. Kallel, B. Solaiman","doi":"10.4236/ars.2019.84007","DOIUrl":"https://doi.org/10.4236/ars.2019.84007","url":null,"abstract":"Near-InfraRed and Visible (Vis-NIR) \u0000spectroscopy is a promising tool allowing to quantify soil properties. It shows \u0000that information encoded in hyperspectral data can be useful after signal \u0000processing and model calibration steps, in \u0000order to estimate various soil properties throughout appropriate statistical \u0000models. However, one of the problems encountered in the case of hyperspectral \u0000data is related to information redundancy between different spectral bands. \u0000This redundancy is at the origin of multi-collinearity in the explanatory \u0000variables leading to unstable regression coefficients (and, difficult to \u0000interpret). Moreover, in hyperspectral spectrum, the information concerning the \u0000chemical specificity is spread over several wavelengths. Therefore, it is not \u0000wise to remove this redundancy because this removal affects both relevant and \u0000irrelevant hyperspectral information. In this study, the faced challenge is to \u0000optimize the estimation of some soil properties by exploiting all the spectral \u0000richness of the hyperspectral data by providing complementary rather than \u0000redundant information. To this end, a new reliable approach based on \u0000hyperspectral data analysis and partial least squares regression is proposed.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130126227","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":"Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model","authors":"S. Hihi, Zouhair Ben Rabah, Moncef Bouaziz, Mahmoud Yassine Chtourou, S. Bouaziz","doi":"10.4236/ars.2019.83005","DOIUrl":"https://doi.org/10.4236/ars.2019.83005","url":null,"abstract":"Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. Multispectral data Sentinel_2 are used to study saline soils in southern Tunisia. 34 soil samples were collected for ground truth data in the investigated region. A moderate correlation was found between electrical conductivity and the spectral indices from SWIR. Different spectral indices were used from original bands of Sentinel_2 data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Sentinel_2 original bands showed that SWIR bands (b11 and b12) and the salinity index SI have the highest correlation with EC. Based on these results and combining these remotely sensed variables into a regression analysis model yielded a coefficient of determination R2 = 0.48 and an RMSE = 4.8 dS/m.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"69 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087458","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":"Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes","authors":"F. Asal","doi":"10.4236/ARS.2019.82004","DOIUrl":"https://doi.org/10.4236/ARS.2019.82004","url":null,"abstract":"Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.","PeriodicalId":130010,"journal":{"name":"Advances in Remote Sensing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484009","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}