Advances in Remote Sensing

Journal Information
ISSN / EISSN : 2169-267X / 2169-2688
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 170
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Latest articles in this journal

Susan A. Okeyo, Galcano C. Mulaku
Advances in Remote Sensing, Volume 9, 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 9, pp 12-32; doi:10.4236/ars.2020.91002

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 9, 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.
Yawo Konko, Appollonia Okhimambe, Pouwèréou Nimon, Jerry Asaana, Jean Paul Rudant, Kouami Kokou
Advances in Remote Sensing, Volume 9, 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 9, 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.
Muhammad Atif Butt, Atif Ali, Sonia Ijaz, Rashid Mehmood, Syed Amer Mahmood, Ghulam Jafer, Kashif Shafique, Asadullah Khan Ghalib, Rizwan Waheed, Ali Iqtadar Mirza
Advances in Remote Sensing, Volume 8, pp 1-29; doi:10.4236/ars.2019.81001

Niket Shastri, Kamlesh Kamlesh Pathak
Advances in Remote Sensing, Volume 8, pp 30-39; doi:10.4236/ars.2019.81002

Sarra Hihi, Zouhair Ben Rabah, Moncef Bouaziz, Mahmoud Yassine Chtourou, Samir Bouaziz
Advances in Remote Sensing, Volume 8, pp 77-88; doi:10.4236/ars.2019.83005

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.
Peng Wu, Yumin Tan
Advances in Remote Sensing, Volume 8, pp 89-98; doi:10.4236/ars.2019.84006

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.
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