Advances in Remote Sensing

Journal Information
ISSN / EISSN : 2169-267X / 2169-2688
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Former Publisher:
Total articles ≅ 174
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Latest articles in this journal

Sviatlana I. Guliaeva, Ilya I. Bruchkousky, Leonid V. Katkovsky
Advances in Remote Sensing, Volume 10, pp 25-46; doi:10.4236/ars.2021.102002

In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by insect outbreaks is of great relevance for preventing the further spread of pests. Forests of Belarus are largely affected by conifers dieback caused by the bark beetle. The aim of the study was to identify drying out conifers using a TripleSat satellite multispectral image of a woodland area in Belarus based on preliminary airborne measurements. Spectrometers operating in a spectral range of 400 - 900 nm were used in airborne measurements, resulting in distinguishing various drying out stages with an accuracy of 27% - 74% for aerial data. In this study, a supervised classification of the TripleSat image based on the method of linear discriminant analysis (LDA) was performed. The input data for LDA algorithm is a set of remote sensing vegetation indices. Results of the study demonstrate that about 90% of the test site is at the green-attack stage that is confirmed by ground surveys of this area.
Ngoumou Paul Claude, Assembe Stephane Patrick, Owono Amougou Olivier Ulrich Igor, Meying Arsene, Yandjimaing Justine, Ngoh Jean Daniel, Pepogo Man-Mvele Augustin Didier
Advances in Remote Sensing, Volume 10, pp 1-24; doi:10.4236/ars.2021.101001

Geophysical surveying is crucial in the investigation of mineral resources in poorly exposed areas such as SE-Cameroon, a region known for its gold mineral potential. In this paper, gravity survey is carried out in the Batouri area, SE-Cameroon based on land gravity data from the Centre-south Cameroon. Therefore, an analytical polynomial separation program, based on least-square fitting of a third-degree polynomial surface to the Bouguer anomaly map, was used to separate the regional/residual components in gravity data. This technique permitted to better understand the disposition of the deep and near surface structures responsible of the observed anomalies in the Batouri area. Spectral analysis and 2.5D modelling of two profiles P1 (SW-NE) and P2 (N-S) selected from the residual anomaly map provided depths to basement. These depths constrain the gravity models along the profiles, indicating a variable thickness of the sedimentary infill with an approximate anomaly of -33 mGal. The 2.5D model of the basement shows a gravity body, with a signature suggesting two close and similar masses, which characterize the quartz-bearing formations associated here to granite and gneiss. Our work highlights a main heavy gravity: Gwé-Batouri anomaly, containing the major part of auriferous deposits located along the NE-SW direction. Further, three tectonic sub-basins bounded by normal faults have been highlighted at Guedal, Gwé, and Bélimban, in the south of Guedal-Bélimban depression. They are associated with the extension tectonics, more or less vertical tangential cuts and accidents that have affected the region. A correlation with previous results from tectonic, lithological and gold mineralization activities proves the relevance of the study and the need to intensify geophysical surveying in the area.
Yawo Konko, Appollonia Okhimambe, Pouwèréou Nimon, Jerry Asaana, Jean Paul Rudant, Kouami Kokou
Advances in Remote Sensing, Volume 09, pp 85-100; doi:10.4236/ars.2020.92005

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats.
Stephane Patrick Assembe, Theophile Ndougsa Mbarga, Françoise Enyegue A. Nyam, Paul Claude Ngoumou, Arsene Meying, Daniel Herve Gouet, Alain Zanga, Jean Daniel Ngoh
Advances in Remote Sensing, Volume 09, pp 53-84; doi:10.4236/ars.2020.92004

A semi-regional study was carried out in the Yaounde-Sangmelima area, a densely vegetated tropical region of southern Cameroon located in the Central Africa Fold Belt (CAFB)/Congo Craton (CC) transition zone. Towards structural lineaments and predictive hydrothermal porphyry deposits mapping, an integrated analysis of Landsat-8 OLI data, aeromagnetic, geological and mineral indices maps was performed. The Remote sensing using False colour composite images involving bands combinations and Crosta method (features oriented principal components analysis) enabled the mapping of the gneisses and schists domains without a clear differentiation between the Yaounde and Mbalmayo schists; despite the reflectance anomalies evidenced NW of Akonolinga, hydrothermal alterations in the study area failed to be detected. Besides, aeromagnetics depicted a moderately fractured northern zone (the CAFB) contrasting with a high densely fractured zone (the CC, known as Ntem complex). The Ntem complex displays signatures of a meta-igneous, an intrusive complex, greenstone relics south of Sangmelima and hydrothermal activity. Indeed, CET porphyry analysis tool detected many porphyry centres. In general, the study revealed many lineaments including contacts, fractures faults zones and strike-slips. The major aeromagnetics structures are SW-NE to WSW-ENE and WNW-ESE to NW-SE while those from Landsat-8 are NE-SW, WNW-ESE, NW-SE, WSW-ENE and NW-ESE to NNW-SSE. Together, these structures depict trans-compressions or trans-tensions corresponding to a broad NE-SW strike-slips channel that affect both the CAFB and the Ntem Complex, and they control the intrusions thus confirming a pervasive hydrothermal activity within the Ntem Complex. The proximity or coincidence of these porphyry centres with some mapped Iron-Gold affiliated mineral indices and porphyry granites indicate the possible occurrence of many hydrothermal ore deposits. These results show the high probability for the Ntem complex to host porphyry deposits so they may serve to boost mineral exploration in the Yaounde-Sangmelima region and in the entire southern Cameroon as well.
Yaw A. Twumasi, Edmund C. Merem, John B. Namwamba, Olipa S. Mwakimi, Tomas Ayala-Silva, Kamran Abdollahi, Ronald Okwemba, Onyumbe E. Ben Lukongo, Caroline O. Akinrinwoye, Joshua Tate, et al.
Advances in Remote Sensing, Volume 09, pp 33-52; doi:10.4236/ars.2020.91003

The study aimed to assess the potential of using Remote Sensing (RS) da-ta to evaluate the changes of urban green spaces in Lagos, Nigeria. Land-sat Thematic Mapper and Landsat 8 (Operational Land Imager) data pair of May 4, 1986, December 12, 2002 and January 1, 2019 covering Lagos Government Authority (LGA) were used for this study. Supervised image classification technique using Maximum Likelihood Classifier (MLC) was used to create base map which was then used for ground truthing. Ran-dom Forest (RF) classification technique using RF classifier was utilized in this study to generate the final land use land cover map. RF is an en-semble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification). Lagos census population data was also used in this study to model population projection. Extrapolation of the model was used to predict data for the years, 2020 and 2040. Re-sults of the study revealed a reduction of urban green spaces due to agri-culture and settlement. While the remote mapping revealed the gradual dispersion of ecosystem degradation indicators spread across the state, there exists clusters of areas vulnerable to environmental hazards across Lagos. To mitigate these risks, the paper offered recommendations rang-ing from the need for effective policy to green planning education for city managers, developers and risk assessment. These measures will go a long way in helping sustainability and management of land resources in Lagos.
Gayane Faye, Fama Mbengue, Lacina Coulibaly, Mamadou Adama Sarr, Modou Mbaye, Amath Tall, Dome Tine, Omar Marigo, Mouhamadou Moustapha Mbacke Ndour
Advances in Remote Sensing, Volume 09, pp 101-115; doi:10.4236/ars.2020.93006

The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to discriminate millet, maize and peanut crops. Training plots were used in order to analyse temporal variation of the three crops’ signals. The NDVI (Normalized Difference Vegetation Index) was able to differentiate crops only at the end of the rainy season (October). The optical data as well as the radar ones could not easily discriminate the three crops during the growing season, because in that period vegetation cover is low, and soil contribution to the signals (due to roughness and moisture) was more important than that of real vegetation. However, the ratio of VH/VV (VH: incident signal in vertical polarization and reflected signal in horizontal polarization; VV: incident signal in vertical polarization and reflected signal in horizontal polarization) gave a difference between millet and the two other crops at the beginning cultural season (July 11). Difference appears from the second third of September when the harvest of cereals crops (millet and maize) began. From middle of October, the peanut signal dropped sharply thus facilitating the differentiation of peanut from the two other crops. This analysis led to the identification of data that have could be used to discriminate these crops (useful data). Classification of the combined useful data gave an overall high accuracy of 82%, with 96%, 61% and 65% for peanut, maize and millet, respectively. The non-agricultural areas (water, natural vegetation, habit, bare soil) were well classified with an accuracy greater than 90%.
Kouakou Hervé Kouassi, Zilé Alex Kouadio, Arthur Brice Konan-Waidhet, Affessi Christian Serge Affessi
Advances in Remote Sensing, Volume 09, pp 117-126; doi:10.4236/ars.2020.94007

In order to better identify spatially the areas at risk of flooding for the riparian populations of Grand-Bassam during strong floods, a study aimed at developing hazard and vulnerability maps from RADAR Sentinel-1 and optical images Sentinel-2 has been put in place. The flood hazard study highlighted the flooded areas in Grand-Bassam. These areas represent 747.7 ha, or 1.02% of the total surface. The vulnerability map produced using the maximum likelihood method identified eight (8) land use classes. These are the classes Water, Dense forest, Secondary forest, Swamp forest, Industrial crops, Food crops, Habitats and bare soils. It made it possible to highlight the socio-economic interests of Grand-Bassam. The flood risk map developed from the intersection of the themes of the vulnerability map and that of the hazard has enabled the recognition of risk areas which are located near the source of the risk (Comoé River) and at low altitudes. These are Moossou, Petit Paris, Quartier Phare and Quartier France.
Susan A. Okeyo, Galcano C. Mulaku
Advances in Remote Sensing, Volume 09, pp 1-11; doi:10.4236/ars.2020.91001

Crop insurance, though clearly needed, has not taken root in Kenyan agriculture, and what little exists is indemnity based, meaning that a farmer is compensated only based on assessed crop damage or harvest shortfall. This is often cumbersome and expensive for the average subsistence farmer. A better approach is to use index based insurance, whereby an agreed index is computed and the farmer is compensated or not compensated depending on its value. Remote sensing technology, which is now widely available globally, provides such an index, the Normalized Difference Vegetation Index (NDVI), which is an acknowledged indicator of crop health at different stages of crop growth. This paper reports on a study carried out in mid-2019 to investigate the possibility of applying remote sensing in this way to enable crop insurance for maize farmers in Migori County, Kenya. Sentinel 2 imagery from May 2017 (taken as the insurance year) was acquired, classified and NDVI generated over the study area. An 8 Km × 8 Km grid was overlaid and average NDVI computed per such grid cell. Similar imagery for May 2016 was acquired and similarly processed to provide reference NDVI averages. For any grid cell then, if Ap be the insurance year NDVI and Ar the reference NDVI, the insurance index was computed as (Ap - Ar), and farmer compensation would be triggered if this value was negative. Results show that out of about 85 small holder farms in the study area, 30 would have qualified for such compensations. These results are recommended for further refining and pilot testing in the study area and similar maize growing areas.
Abdlhamed Jamil, Abdulmohsen Al-Shareef, Amer Al-Thubaiti
Advances in Remote Sensing, Volume 09, pp 12-32; doi:10.4236/ars.2020.91002

Fahmy F. F. Asal
Advances in Remote Sensing, Volume 08, pp 51-75; doi:10.4236/ars.2019.82004

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.
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