Processing of Landsat 8 Imagery and Ground Gamma-Ray Spectrometry for Geologic Mapping and Dose-Rate Assessment, Wadi Diit along the Red Sea Coast, Egypt

Maximum Likelihood (MLH) supervised classification of atmospherically corrected Landsat 8 imagery was applied successfully for delineating main geologic units with a good accuracy (about 90%) according to reliable ground truth areas, which reflected the ability of remote sensing data in mapping poorly-accessed and remote regions such as playa (Sabkha) environs, subdued topography and sand dunes. Ground gamma-ray spectrometric survey was to delineate radioactive anomalies within Quaternary sediments at Wadi Diit. The mean absorbed dose rate (D), annual effective dose equivalent (AEDE) and external hazard index (Hex) were found to be within the average worldwide ranges. Therefore, Wadi Diit environment is said to be radiological hazard safe except at the black-sand lens whose absorbed dose rate of 100.77 nGy/h exceeds the world average. So, the inhabitants will receive a relatively high radioactive dose generated mainly by monazite and zircon minerals from black-sand lens.

Use of Landsat 8 OLI Images to Assess Groundwater Potential Areas in the Bamun Plateau: Cameroon Volcanic Line (CVL)

The problem of groundwater supply in the Bamun plateau situated in the Cameroon Volcanic Line exists and no proper solution has been found so far. This investigation intends to find the suitable groundwater potential zones by overlaying the geomorphologic map, lineament map, lineament density map and lithological map, using visual interpretation of Landsat imagery. The results reveal that about 1921 structural elements, ranging in size, from 30 m to 5.845 km with an average length of 671 m in the field. The total length of the mapped lineaments is approximately 1289 km. The most important lineament (5.845 km length) diagonally crosses the study area in the direction NNE-SSW. In addition to this trend, all others are smaller than 14 km. More than 92% of lineaments are less than 5 km in size and only 1.3% of them are larger than 10 km. Small lineaments are thus the most numerous. According to their directions, the lineaments listed are grouped into 18 directional classes of 10-degree intervals. The rosette of their directions highlights the preferred directions NE-SW, N-S, E-W, NNE-SSW and ENE-WSW. Most of the lineaments clusters in the central part of the area are N20° - 30°E and N60° - 70°E trending lineaments. In this study, the NE-SW trend dominates the structural trend followed by NW-SE and N-S. This can be an indication of the directions of groundwater movement in the area. Alluvial plain and valley have moderate to very good groundwater potential that occurs all over the study area. Porosity of the volcanic rocks varies greatly, but it is everywhere more porous than the underlying, unweathered bedrock. There are essentially three classes (low, average and high) of groundwater potential zones. Hight potential zones are observed around the localities of the Khogham, Mbatpit and Mbam massifs on the one hand and Manswen, Njikwop, Mfelap, Foumban, Njindaré, Nkoundem and Ngwen jigoumbé localities on the other hand. About 13% of the area has good groundwater potential around the mountains while 58% is moderately good which corresponds to high to moderate lineament densities situated at average altitude 1200 m and about 31% of the area has poor groundwater potential corresponding to low lineament density areas. Moreover, this work has helped develop a detailed lineament map that can be used for mining and hydrological prospecting campaigns.

Migration and Spatiotemporal Land Cover Change: A Case of Bosomtwe Lake Basin, Ghana

Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.

Spatiotemporal pattern of urban forest leaf area index in response to rapid urbanization and urban greening

Rapid urbanization and urban greening have caused great changes to urban forests in China. Understanding spatiotemporal patterns of urban forest leaf area index(LAI) under rapid urbanization and urban greening is important for urban forest planning and management. We evaluated the potential for estimating urban forest LAI spatiotemporally by using Landsat TM imagery. We collected three scenes of Landsat TM(thematic mapper)images acquired in 1997, 2004 and 2010 and conducted a field survey to collect urban forest LAI. Finally, spatiotemporal maps of the urban forest LAI were created using a NDVI-based urban forest LAI predictive model.Our results show that normalized differential vegetation index(NDVI) could be used as a predictor for urban forest LAI similar to natural forests. Both rapid urbanization and urban greening contribute to the changing process of urban forest LAI. The urban forest has changed considerably from 1997 to 2010. Urban vegetated pixels decreased gradually from 1997 to 2010 due to intensive urbanization.Leaf area for the study area was 216.4, 145.2 and173.7 km~2 in the years 1997, 2004 and 2010, respectively.Urban forest LAI decreased sharply from 1997 to 2004 and increased slightly from 2004 to 2010 because of numerous greening policies. The urban forest LAI class distributions were skewed toward low values in 1997 and 2004. Moreover, the LAI presented a decreasing trend from suburban to downtown areas. We demonstrate the usefulness of TM remote-sensing in understanding spatiotemporal changing patterns of urban forest LAI under rapid urbanization and urban greening.

Coastal evolution of Yancheng, northern Jiangsu, China since the mid-Holocene based on the Landsat MSS imagery

Since the 1970s, remote sensing images have provided new information for the delineation and analysis of coastline changes, especially focusing on the short timescale changes. This paper, based on the Landsat MSS imagery, focuses on the coastline evolution of Yancheng, northern Jiangsu, China since the mid-Holocene. A zebra stripe image, which could reveal the ancient coastal evolution of Yancheng, was extracted from a Landsat MSS image. Based on the extracted black-white stripes, 19 surface sediment samples were recovered and analyzed to recognize the sedimentary characteristics of these stripes. It shows that most sand and silty sand samples appear on the white stripes, while silt and silty clay samples are on the black stripes. Sandy and muddy sediments present an alternating distri- bution pattern on the Yancheng coastal plain. A historical coastline map was drawn according to the previous research achievements of the paleo-coastal sand barriers and paleo-coastlines, and was superimposed on the zebra stripe image. The trend of the extracted zebra stripes is consistent with the historical coastlines, and it should be the symbol of the Yancheng coastline evolution. On the basis of ten sets of black-white stripes and previous research results, we divided the progression of Yancheng coastal evolution into three stages （i.e., the early stable stage （6500 a BP-AD 1128）, the rapid deposition stage （AD 1128-1855） and the adjustment stage （AD 1855-present））. Ten sets of black-white stripes were identified as the characteristic pattern of the coastline evolution on the Yancheng coastal plain.

Land Use Land Cover Dynamics of Upper Benue River Basin, Nigeria

This study examined land use land cover (LULC) dynamics in Upper Benue River Basin, Nigeria. The study makes use of primary and secondary data. Landsat Imageries for the years 1981, 2001 and 2021 were used in the study. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps. Markov Cellular Automata model was used to predict the status of LULC of the catchment for year 2070. The findings of the study reveal remarkable changes in the land use land cover of the Upper Benue River Basin. The land cover has witnessed downward trend in the percentage area covered by vegetation and bare surface resulting in 15.4% and 2.6% losses respectively. The result of the findings reveals that the built-up area and rock outcrop has shown significant gains of 15.2% and 2.9% of the study area respectively. Water body has been stable with 0% change, though, it witnessed a marginal decline in 2001. The land use land cover change observed in the Upper Benue River Basin was as a result of anthropogenic factors characterized by deforestation, expansion of agricultural lands, overgrazing among others. Based on the findings, the study recommended controlled grazing activity, deforestation and indiscriminate fuelwood exploitation and improved agronomic practices in the basin.

Remote estimates of suspended particulate matter in global lakes using machine learning models

Suspended particulate matter(SPM)in lakes exerts strong impact on light propagation,aquatic ecosystem productivity,which co-varies with nutrients,heavy metal and micro-pollutant in waters.In lakes,SPM exerts strong absorption and backscattering,ultimately affects water leaving signals that can be detected by satellite sensors.Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy.There are still rooms for model accuracy improvements,and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters.We assembled more than 16,400 in situ measured SPM in lakes from six continents(excluding the Antarctica continent),of which 9640 samples were matched with Landsat overpasses within±7 days.Seven machine learning algorithms and two simple regression methods(linear and partial least squares models)were used to estimate SPM in lakes and the performance were compared.To overcome the problem of imbalance datasets in regression,a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise(SMOGN)was adopted in this study.Through comparison,we found that gradient boosting decision tree(GBDT),random forest(RF),and extreme gradient boosting(XGBoost)models demonstrated good spatiotemporal transferability with SMOGN processed dataset,and has potential to map SPM at different year with good quality of Landsat land surface reflectance images.In all the tested modeling approaches,the GBDT model has accurate calibration(n=6428,R^(2)=0.95,MAPE=29.8%)from SPM collected in 2235 lakes across the world,and the validation(n=3214,R^(2)=0.84,MAPE=38.8%)also exhibited stable performance.Further,the good performances were also exhibited by RF model with calibration(R^(2)=0.93)and validation(R^(2)=0.86,MAPE=24.2%)datasets.We applied GBDT and RF models to map SPM of typical lakes,and satisfactory result was obtained.In addition,the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors(L5-TM,L7-ETM+,and L8-OLI),thus the model has the potential to map SPM of lakes for monitoring temporal variations,and tracks lake water SPM dynamics in approximately the past four decades(1984-2021)since Landsat-5/TM was launched in 1984.

A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯cation.This work proposes a cloud detection model based on the Cloud Detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial attention.Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and detection.In addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions.Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet.Averaged over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,respectively.Those results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

Greenhouse area detection in Guanzhong Plain,Shaanxi,China:spatio-temporal change and suitability classification

The extensive use of greenhouses has brought soared economic benefits for farming practitioners in China and an overview of the spatio-temporal distribution of greenhouses is of great interest to agricultural practitioners and decision-makers.In this study,Landsat image based greenhouse maps in Guanzhong Plain,Shaanxi,China were made using random forest classification algorithm through visual interpretation on the Google Earth Engine.The 7-year's changes in greenhouse areas were investigated(i.e.2000,2003,2006,2010,2013,2015 and 2019)with yearly overall accuracy more than 90%.The results showed that the total area of greenhouses in Guanzhong Plain demonstrated an increasing trend,from 5.92 km2 in 2000 to 194.42 km2 in 2019 with a considerable growth between 2010 and 2015.The dominant drivers for the increase are largely attributed to the government policy as well as economic profitability.The distribution of greenhouse shifts to central and eastern regions of Guanzhong Plain.Greenhouses preferentially expand to the area near to rural roads,main rivers,and high elevation,with more than 45%greenhouses distributed within 1 km of the county rural road.The principal component analysis based suitability evaluation showed that a total of 38.44%of the area was suitable for greenhouse.