JournalTOCs

Remote Sensing in Earth Systems Sciences
Number of Followers: 6

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2520-8195 - ISSN (Online) 2520-8209
Published by Springer-Verlag

[2467 journals]

Comparison and Validation of Elevation Data at Selected Ground Control
Points and Terrain Derivatives Derived from Different Digital Elevation
Models
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  Abstract: Abstract Digital elevation model (DEM) is a precisely defined 3D representation of terrain surface. DEM from satellite imageries have grown in popularity over the last decade, with a wide range of applications. It gives hydrologists and geomorphologists a powerful tool to investigate the relationship between basic geomorphic processes and landforms. The grid size, spatial variation, and vertical accuracy are among the most important characteristics of elevation data sources while determining the parametric analysis of geomorphic metrics. Using ground control points as a reference level, researchers can determine the elevation accuracy of the present generation of global DEM of different resolutions. The current research is focusing primarily on DEM-based comparative analysis under two different categories of elevation data and topographic attributes for the selection of the most accurate DEM in the area. Some significant DEM (ASTER, SRTM, Cartosat, and ALOS) are used in this study, and different descriptive error statistics (RMSE, MAE, and SD) are used to estimate their best quality. The validation suggests that the Cartosat-1(10 m) shows relatively high vertical accuracy (RMSE = 46.0; MAE = 13.0; SD = 46.0) and SRTM has the lowest (RMSE = 52.0; MAE = 19.0; SD = 52.7). The study area has a gradually undulating topography and a drainage network of 6th order. ALOS, SRTM, and Cartosat have lower mean elevation values than ASTER; also, there are visible differences in stream parameters as well. The area is structurally controlled, as indicated by the mean bifurcation ratio, which ranges from 3.8 to 4.4. As per the visual analysis, the contours generated from DEM with similar contour intervals (CI) do not align with each other. All DEM performed well but their statistical values indicate that the effect of resolution impacts hard on the output generated.
  PubDate: 2023-01-28
Wetlands Mapping with Deep ResU-Net CNN and Open-Access Multisensor and
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  Abstract: Wetlands are a valuable ecosystem that provides various services to flora and fauna. This study developed and compared deep and shallow learning models for wetland classification across the climatically dynamic landscape of Alberta’s Parkland and Grassland Natural Region. This approach to wetland mapping entailed exploring multi-temporal (combination of spring/summer and fall months over 4 years—2017 to 202) and multisensory (Sentinel 1 and 2 and Advanced Land Observing Satellite, ALOS) data as input in the predictive models. This input image consisted of S1 dual-polarization vertical-horizontal bands, S2 near-infrared and shortwave infrared bands, and ALOS-derived topographic wetness index. The study explored the ResU-Net deep learning (DL) model and two shallow learning models, namely random forest (RF) and support vector machine (SVM). We observed a significant increase in the average F1-score of the ResNet model prediction (0.82) compared to SVM and RF prediction of 0.69 and 0.69, respectively. The SVM and RF models showed a significant occurrence of mixed pixels, particularly marshes and swamps confused for upland classes (such as agricultural land). Overall, it was evident that the ResNet CNN predictions performed better than the SVM and RF models. The outcome of this study demonstrates the potential of the ResNet CNN model and exploiting open-access satellite imagery to generate credible products across large landscapes. Graphical
  PubDate: 2023-01-20
Retrieval of Land Surface Temperature from Landsat 8 OLI and TIRS: A
Comparative Analysis Between Radiative Transfer Equation-Based Method and
Split-Window Algorithm
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  Abstract: Abstract The system of observation and capturing the earth resource features have been improving with the scientific revolution and technological development in remote sensing techniques. In comparison with the previous Landsat series, Landsat 8 OLI and TIRS (Operational Land Imager and Thermal Infrared Sensor) is the latest applications of thermal infrared sensor for the Landsat project offers two adjacent thermal bands that has a great advantage for retrieving land surface temperature. In this study, an effort was made to compare two different approaches of land surface temperature retrieval method from TIRS data including the radiative transfer equation (RTE) and the split-window algorithm (SWA). The objective of this study was to estimate land surface temperature from TIRS data of Landsat 8 using different techniques and compare with actual ground temperature for pre-monsoon, monsoon, and post-monsoon season to determine accurate technique and thermal band. In this regard, twelve ground stations such as New Delhi, Noida, Ghaziabad, Bulandshahr, Gurugram, Faridabad, Muradnagar, Safdarjung airport, Indira Gandhi international airport, Rajiv Chowk, Dadri, and Kirti Nagar were marked on Landsat 8 product with Path 146 and Row 40. Based on analysis, the result shows that the radiative transfer equation (RTE) using band 10 has highest accuracy with the lowest root mean square error (1.0334 ℃, 1.5189 ℃, and 1.4197 ℃, respectively for pre-monsoon, monsoon, and post-monsoon), while RTE using band 11 and split-window algorithm (SWA) using band 10 and 11 has lower accuracy with higher root mean square error (> 2.0 ℃ in all cases). Thus, it is recommended that for those methods LST retrieval using single band, band 10 using RTE has higher accuracy than band 11 and split-window algorithm.
  PubDate: 2022-12-01
  DOI: 10.1007/s41976-022-00079-0
Performance Evaluation of Google Earth Engine Based Precipitation
Datasets Under Different Climatic Zones over India
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  Abstract: Abstract Satellite as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting and hydrological and climate studies. However, the accuracy of satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions, i.e., CHIRPS, ERA5, TRMM, GPM, and TerraClim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) gridded data in six climate zones in India. The statistics such as RMSE, R2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet and dry, tropical wet, and semi-arid zones. TerraClim and ERA5 showed high accuracy in humid subtropical and montane regions, respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India.
  PubDate: 2022-11-21
  DOI: 10.1007/s41976-022-00077-2
Spatial and Temporal Analysis of Rain-NDVI Relationship in Lower and
Middle Casamance from 1982 to 2019
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  Abstract: Abstract Contrary to claims of widespread irreversible degradation of vegetation and landscapes in West Africa, a recent increase in seasonal vegetation indices of Sahelian areas has been observed, which has been interpreted as a consequence of the rainfall recovery after the major droughts (1968–1994). This paper aims at revealing the climatic drivers of such greening in Casamance. A multi-scalar and multi-satellite remote sensing approach was implemented to study the temporal trends of vegetation activity and their relationships with rainfall in Lower and middle Casamance. The datasets used are the NOAA NDVI (GIMMS) from 1982 to 2015, the MODIS NDVI (MOD13Q1) from 2000 to 2019, and rainfall data from the Ziguinchor station from 1982 to 2017. These two NDVI time series were studied, on the one hand, with a classification method to discretize the different vegetations according to the rhythms and intensities of their vegetation activity throughout the year and, on the other hand, with Mann–Kendall’s correlation to reveal the trends. Almost three-quarters (72.5%) of the pixels show a significant positive trend (regreening) between 1982 and 2015. The simple correlation between NDVI and rainfall is very low (r2 = 0.17) but both lagged correlation (r2 = 0.86) and the correlation between NDVI and cumulated rainfall of longer periods (r2 = 0.75) are strong. In other terms, after 1998 stronger rainfall in July and August give stronger NDVI in October and November. While a rainfall positive trend since the 1980s appears to be the main causal factor for the increase in vegetation indices, negative trends were also locally observed that are not explained by the rainfall-vegetation relationship and thus hypothetically a human-induced change.
  PubDate: 2022-11-12
  DOI: 10.1007/s41976-022-00078-1
Forest Fire Characterization Using Landsat-8 Satellite Data in Dalma
Wildlife Sanctuary
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  Abstract: Abstract A forest fire has caused a loss of biodiversity and forest heterogeneity and resulted in forest degradation and fragmentation. Remote sensing techniques have been widely used for locating and delineating forest fires. The present study has employed Landsat-8 satellite data during 2014–2020 for spatiotemporal analysis of forest fire in Dalma Wildlife Sanctuary (DWS). Normalized burn ratio (NBR) has been used to delineate forest fire-affected locations along with visual interpretation techniques. The results showed that an extensive area was burnt and deforested due to forest fire in DWS during 2014–2020. The burned areas due to forest fires within the notified forest boundary in DWS were 12.11 km2, 25.5 km2, 22.45 km2, 9.11 km2, 24.44 km2, 10.09 km2, and 1 km2 during 2014, 2015, 2016, 2017, 2018, 2019, and 2020, respectively, whereas burned areas outside notified boundary were 2.24 km2, 4.15 km2, 1.48 km2, 3.29 km2, 3.31 km2, 1.9 km2, and 0.1 km2. According to visual image interpretation, the highest burned area was found in 2015 (25.5 km2), whilst the least affected was found in 2020 (1 km2), and fires were mainly seen in the degraded forests and open forest regions. The present study revealed that forest fire is more dominant in Asanbani, Pardih, Bhelaipahari, Gobargushi, Bamri, Andharjhor, Somadih, Koira, Tetla, Bochkamkocha, Sah, Rbera, and Jamdih locations/beats in DWS. Thereby, these forest beats need attention from forest managers to control fire-mediated forest degradation for the conservation and restoration of forests in DWS.
  PubDate: 2022-10-07
  DOI: 10.1007/s41976-022-00076-3
Impact of Urbanization and Spatio-temporal Estimation of Land Surface
Temperature in a Fast-growing Coastal Town in Kerala, Western Coast of
Peninsular India
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  Abstract: Abstract The large-scale degradation of the environment has happened due to the rapid urbanization and non-scientific developments around major towns in the world. The main problem related to this scenario is the population density and fast socio-economic development in the area. The current study is an effort to investigate the spatio-temporal variations in the urban growth, land use/land cover, and the land surface temperature (LST) of a coastal town on the western coast of peninsular India that experiences a tropical climate. US-based Landsat imageries of 1988, 1997, 2001, 2014, 2016, and 2018 have been used for the investigation. Multiband analyses were performed for the estimation of geospatial indicators. The study revealed a noticeable decrease in vegetated areas and barren lands over 30 years. The built-up area increased from 1 to 22% of the total study area during these years. The average land surface temperature has been augmented from 22.2 to 29.2 °C in the study area from 1988 to 2018. Normalized difference built-up index (NDBI) showed a significant positive correlation with land surface temperature (LST) from 1988 to 2018, Whereas, normalised difference vegetation index (NDVI) recorded a significant negative correlation with LST each year. The present study revealed the need for increasing the green cover and making the developments more sustainable and environmentally friendly. The spatio-temporal database generated through the study can be used as input for future development and conservation planning and management.
  PubDate: 2022-08-09
  DOI: 10.1007/s41976-022-00075-4
The Assessment of Meteorological Drought Impact on the Vegetation Health
Index
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  Abstract: Abstract One of the critical consequences of drought is the reduction of water resources and reduction of agricultural production. Therefore, it is essential to evaluate the relationship between meteorological drought and vegetation. To investigate this relationship, meteorological data from 28 rain gauge and remote sensing stations located in Lorestan Province and its neighboring regions were used in this study. First, the standardized precipitation index (SPI) was calculated between 1987 and 2017 using meteorological data, and then, the vegetation health index (VHI) was calculated using satellite images for the same years. The correlation between SPI and VHI was computed by Pearson’s correlation coefficient. The results showed that the highest Pearson’s correlation coefficient was 0.77, belonging to the SPIs for October and November with 9- and 12-month time periods. Multivariate linear regression was also performed between the SPI and vegetation health index (VHI), and the results showed that SPI was significantly correlated with VHI at a 5% level over 9- and 12-month periods. Finally, a confusion matrix was used to evaluate the compatibility of the SPI and VHI drought classes.
  PubDate: 2022-07-25
  DOI: 10.1007/s41976-022-00074-5
Using Multi-decadal Satellite Records to Identify Environmental Drivers of
Fire Severity Across Vegetation Types
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  Abstract: Abstract To date, most studies of fire severity, which is the ecological damage produced by a fire across all vegetation layers in an ecosystem, using remote sensing have focused on wildfires and forests, with less attention given to prescribed burns and treeless vegetation. Our research analyses a multi-decadal satellite record of fire severity in wildfires and prescribed burns, across forested and treeless vegetation, in western Tasmania, a wet region of frequent clouds. We used Landsat satellite images, fire history mapping and environmental predictor variables to understand what drives fire severity. Remotely-sensed fire severity was estimated by the Delta Normalised Burn Ratio (ΔNBR) for 57 wildfires and 70 prescribed burns spanning 25 years. Then, we used Random Forests to identify important predictors of fire severity, followed by generalised additive mixed models to test the statistical association between the predictors and fire severity. In the Random Forests analyses, mean summer precipitation, mean minimum monthly soil moisture and time since previous fire were important predictors in both forested and treeless vegetation, whereas mean annual precipitation was important in forests and temperature seasonality was important in treeless vegetation. Modelled ΔNBR (predicted ΔNBRs from the best-performing generalised additive mixed model) of wildfire forests was higher than modelled ΔNBR of prescribed burns. This study confirms that western Tasmania is a valuable pyrogeographical model for studying fire severity of wet ecosystems under climate change, and provides a framework to better understand the interactions between climate, fire severity and prescribed burning.
  PubDate: 2022-07-07
  DOI: 10.1007/s41976-022-00070-9
Change Detection and Feature Extraction Using High-Resolution Remote
Sensing Images
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  Abstract: Abstract Change detection using high temporal resolution remote sensing satellite data for identifying changes on the Earth’s surface is critical in urban applications, including vacant land site monitoring. Physical ground surveys, for monitoring the vacant site, are a time-consuming process. Results of analysis of satellite data for identifying changes vary, based on the image interpretation skills and satellite data resolution. The application of computer vision tools and libraries for change detection using image interpretation has shown some excellent results. It can be further enhanced by adding machine learning techniques. This study focuses on integration of binary change detection with machine learning techniques for identifying the change detection and for monitoring the vacant sites in an urban area. Edge detection technique coupled with principal component analysis and k-means clustering for generating change map successfully depicts the changes. Change detection results are further enhanced by adding feature type information derived using machine learning–based classifiers. Random forest classifiers are used to classify and identify different land use classes within the urban area: water bodies, cropland, built-up, roads, and bare land. The approach is evaluated on different areas, giving an overall accuracy of 88.2%, precision of 84.8%, and an F1 score of 81.6% for classification. The classification results are integrated with change detection results to identify changes where the bare land is transformed into built-up by identifying buildings/houses. The work will be helpful in urban planning bodies having multiple vacant land sites for monitoring.
  PubDate: 2022-06-17
  DOI: 10.1007/s41976-022-00073-6
Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal
Crop Classification Using Big Data Reduction Strategy
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  Abstract: Accurate crop classification can support analyses of food security, environmental, and climate changes. Most of the current research studies have focused on applying available algorithms to classify dominant crops on the landscape using one source of remotely sensed data due to geoprocessing constraints (e.g., big data access, availability, and processing power). In this research, we compared four classification algorithms, including the support vector machine (SVM), random forest (RF), regression tree (CART), and backpropagation network (BPN), to select a robust and efficient classification algorithm able to classify accurately many crop types. We used multiple sources of satellite images such as Sentinel-1 (S1) and Sentinel-2 (S2) and developed a new cropping classification method for a study site in the Bekaa valley, Lebanon, fully implemented on Google Earth Engine Platform, which minimized those geoprocessing constraints. The algorithm selection was based on their popularity, availability, simplicity, similarity, and diversity. In addition, we adopted different strategies that included changing the number of crops. The first strategy is to reduce the number of collected S2 images thereafter S1; the second strategy is to use S2 images separately and then combining S2 and S1. This study results proved that the RF is the most robust algorithm for crop classification, showing the highest overall accuracy (OA) (95.4%) and a kappa index of 0.94, followed by BPN, SVM, and CART, respectively. The performance of these algorithms based on major crop types such as wheat or potato showed that CART is the highest with OA (98%) followed by RF, SVM, and BPN, respectively. Nevertheless, CART fails to classify other minor crop types. We concluded that RF is the best algorithm for classifying different crop types in the study area, using multiple remote sensing data sources. Graphical abstract
  PubDate: 2022-06-17
  DOI: 10.1007/s41976-022-00072-7
Assessment of Different Spectral Unmixing Techniques on Space Borne
Hyperspectral Imagery
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  Abstract: Abstract Spectral unmixing decomposes the mixed pixels into constituent land cover features present in that pixel. This can be understood through the concepts of affine, convex and projective geometries. Spectral unmixing is difficult to implement in coarser spatial resolution space-borne hyperspectral data, due to the natural heterogeneity of the different land cover features. Linear spectral unmixing (LSU) follows linear equations for generating fractional coefficients; however, it contains limitations like its inability to handle noisy pixels, least-square error calculation, etc. Mixture tuned matched filtering (MTMF) is a partial unmixing technique in which user-defined targets are mapped. This approach uses a matched filter (MF) and linear mixture theory in combination. Whereas simplex projection unmixing (SPU) technique is nonlinear and is utilized for resolving problems such as fully constrained least square and projecting a point onto a simplex. In this study, Hyperion data was used for performing spectral unmixing using LSU, MTMF, and SPU techniques. The unmixing results obtained were compared and validated using available images from geo-portals. The abundance images of SPU were observed better than MTMF and LSU in terms of the material identification. The variation in the percentage aerial coverage of the land cover features in the mixed pixel is found closer in the abundance results of SPU, i.e., 0.1–3.4% whereas MTMF and LSU have a variation of 0.6–5.2% and 1.9–8.7%, respectively. Rule-based classification was performed on the “abundance images” and SPU classification outperformed the other two techniques, as it enabled differentiation of most of the land cover features.
  PubDate: 2022-06-10
  DOI: 10.1007/s41976-022-00071-8
Evaluation of Monthly Precipitation Data from Three Gridded Climate Data
Products over Nigeria
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  Abstract: Abstract The use of satellite and reanalysis weather product is gaining traction in the scientific community especially in developing worlds where in situ data are sparse. Tropical locations have dynamic and widely variable climate which needs to be continuously monitored. The efficiency of these products in capturing the climatic dynamics of these regions is important. The aim of this research is to investigate the performance of three gridded precipitation products (the University of Delaware (UDEL), NOAA’s precipitation reconstruction over land (NOAA), and Global Precipitation Climatology Centre (GPCC)) across 21 locations within tropical Nigeria. The performance of the gridded data was assessed with gauge data from the Nigerian Meteorological Services (NIMET) over a period of 51 years (1960–2010). Correlation values in the range 0.68–0.92, 0.69–0.92 and 0.30–0.93 were obtained for GPCC, NOAA, and UDEL respectively in all stations. The three products have poor performance in the northern stations of the country during the dry season but good performance in all stations during the wet season. The GPCC gridded product was found to have the best performance over the region.
  PubDate: 2022-06-07
  DOI: 10.1007/s41976-022-00069-2
Development of a Raspberry Pi–Based Remote Station Prototype for Coastal
Environment Monitoring
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  Abstract: Abstract Monitoring of the marine and coastal environment using standard measuring equipment is not without incurring a significant amount of cost. This study was geared at prospecting relatively inexpensive environmental monitoring instrument using the Raspberry Pi computer in combination with commonly available sensors. Atmospheric temperature, humidity, and sea surface temperature (SST) were monitored using locally assembled low-cost measuring equipment with a subsequent comparison with data from a standard weather station. The developed instrument was consequently evaluated for its efficacy and various functionalities in coastal environmental monitoring. DHT11 and DHT22 sensors are relatively cheap and both measure atmospheric temperature and humidity, while a DS19B20 waterproof digital thermometer measures water temperature. These sensors were incorporated in a locally built in situ measuring equipment interfaced by a Python-programmed Raspberry Pi for acquiring data. A successful assemblage and deployment of the device in a near-shore coastal marine environment yielded efficient and accurate data recorded by the DHT22 and DS19B20 sensors. A comparison of the DS18B20-measured SST to SST from Sentinel-3 satellite revealed no significant difference for a simple T-test and with R2 and root mean square error (RMSE) values of 0.172 and 2.15 °C respectively. Similarly, a comparison of atmospheric temperature and humidity between the developed equipment using DHT22 sensor, and the standard weather station yielded strong positive correlations (0.92 and 0.93) and with R2 of 0.71 and 0.58, and RMSE of 0.92 °C and 3.1% respectively. A transformation of the data from the developed equipment with respective regression equations yielded further significant improvements in the results with R2 values of 0.93, 0.84 and 0.87, and RMSE values of 0.63 °C, 0.68 °C and 1.74% respectively for SST (DS19B20), atmospheric temperature (DHT22) and humidity (DHT22). Although the DHT11 sensor recorded higher errors in atmospheric temperature and humidity data due to its low operating tolerance ranges, an application of respective regression equations also yielded improved results. This study has successfully demonstrated the potential of developing and using locally assembled relatively low-cost equipment for environmental monitoring where funding is a constraint for small-scale research and operational in situ observations.
  PubDate: 2022-06-01
  DOI: 10.1007/s41976-021-00053-2
A Global-Scale Assessment of Water Resources and Vegetation Cover Dynamics
in Relation with the Earth Climate Gradient
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  Abstract: Abstract Changes in the terrestrial climate and the rapid growth of the world population cause pressures on water resources and natural vegetation covers. Given the importance of these resources for the survival of both human communities and the terrestrial ecosystems, it is critical to envision research-based strategies for their preservation. However, studies that assessed changes in vegetation and freshwater resources have preferentially focused on the marginal role of human, precipitation, and temperature, while neglecting the connection with global climate gradient. Yet, a full understanding of the ongoing changes in the terrestrial vegetation and water resources is needed to develop effective strategies for preserving these resources. In an effort of contributing to the understanding of these changes, this study investigates the actual patterns in the terrestrial land water masses and vegetation covers in relation with the earth climate gradient. Especially, climate aridity indices are estimated and used to highlight climate classes. Trends analyses of monthly leaf area index and land water storage anomalies show different signals depending on the earth latitude bands. Results show 36.5% of the continental lands have experienced a decrease of water resources, but these areas do not necessarily encompass regions with decreasing trends of vegetation cover. Chi-square statistics indicated significant connections between climate classes and vegetation cover trends as well as climate classes and land water storage trends. This study concludes the global climate gradient marginally regulates the dynamics of water resources and vegetation covers. Yet, examples show human-induced changes can supersede this overall connection in certain regions of the globe.
  PubDate: 2022-01-07
  DOI: 10.1007/s41976-021-00063-0
Seasonal Variability of Sea Surface Salinity in the NW Gulf of Guinea from
SMAP Satellite
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  Abstract: Abstract The advent of satellite-derived sea surface salinity (SSS) measurements has boosted scientific study in less-sampled ocean regions such as the northwestern Gulf of Guinea (NWGoG). In this study, we examine the seasonal variability of SSS in the NWGoG from the Soil Moisture Active Passive (SMAP) satellite and show that it is well-suited for such regional studies as it is able to reproduce the observed SSS features in the study region. SMAP SSS bias, relative to in-situ data comparisons, reflects the differences between skin layer measurements and bulk surface measurements that have been reported by previous studies. The study results reveal three broad anomalous SSS features: a basin-wide salinification during boreal summer, a basin-wide freshening during winter, and a meridionally oriented frontal system during other seasons. A salt budget estimation suggests that the seasonal SSS variability is dominated by changes in freshwater flux, zonal circulation, and upwelling. Freshwater flux, primarily driven by the seasonally varying Intertropical Convergence Zone, is a dominant contributor to salt budget in all seasons except during fall. Regionally, SSS is most variable off southwestern Nigeria and controlled primarily by westward extensions of the Niger River. Anomalous salty SSS off the coasts of Cote d’Ivoire and Ghana especially during summer are driven mainly by coastal upwelling and horizontal advection.
  PubDate: 2021-11-10
  DOI: 10.1007/s41976-021-00061-2
Extreme Rainfall Events over Accra, Ghana, in Recent Years
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  Abstract: Abstract This study examines the recent changes in extreme rainfall events over Accra, Ghana. For this study, an extreme rainfall event is defined as a day with rainfall equal to or exceeding the 1980–2019 95th percentile. Knowing extreme rainfall events help to identify the years with the likelihood of rainfall-related disasters in Accra. In addition, it helps to identify the years with the likelihood of drought or severe dryness which are critical for the livelihoods and economic activities of the people. The study used rainfall data from rain gauge for Accra and satellite-derived winds at the 850 hPa level over southern Ghana from 1980 to 2019. It compares these climatic parameters for both pre-2000 and post-2000 to find out the changes that have occurred throughout the study period. Results show that the frequency and magnitude of extreme rainfall have generally increased during the post-2000 period than during the pre-2000 period, causing increases in mortalities and damages to properties. Seasonally, extreme rainfall events were most intense in July during the pre-2000 period but have changed to June during the post-2000 period. Notably, more intense rainfall events have also occurred during post-2000 winter than pre-2000 winter, consistent with increased warming in the study area. Monthly mean meridional winds at the 850 hPa level were stronger (weaker) in the northerly (southerly) direction during the pre-2000 period but have changed to be stronger (weaker) in the southerly (northerly) direction during the post-2000 period.
  PubDate: 2021-11-09
  DOI: 10.1007/s41976-021-00062-1
Earth Observation Services in Support of West Africa’s Blue Economy:
Coastal Resilience and Climate Impacts
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  Abstract: Abstract The marine and coastal resources of the West Africa region contribute immensely to the global economy as well as to the economies of countries in the region. The region boasts of two vibrant Large Marine Ecosystems (LMEs), namely the Canary Current Large Marine Ecosystem (CCLME) and the Guinea Current Large Marine Ecosystem (GCLME), which provide vast fisheries and other marine resources. The region and its marine and coastal resources are however faced with diverse threats such as climate change, destruction of mangroves, overfishing, habitat destruction and coastal erosion, among many others. Several initiatives have been developed to address these challenges. This paper reviews some of the past as well as ongoing initiatives that address the challenges in the marine and coastal environment of West Africa. Among these initiatives is the Global Monitoring for Environment and Security (GMES) and Africa programme which uses Earth Observation (EO) data and derived information to manage marine and coastal resources, with focus on the “Marine and Coastal Areas Management in western Africa” theme implemented by the University of Ghana (UG). The Regional Marine Centre (RMC) at the University of Ghana, which serves as the Regional Implementation Centre for the GMES and Africa marine programme for West Africa, uses sentinel-1 satellite data to monitor shoreline change. This information is combined with other datasets to generate coastal vulnerability indices (CVI) map for erosion hotspots in the region, which directly feeds into policy initiatives towards addressing the problem of coastal erosion in the region. This, as a result, contributes to building coastal resilience and alleviating the severe impacts of climate change on the West Africa coast, and contributing to the Blue Economy agenda.
  PubDate: 2021-10-19
  DOI: 10.1007/s41976-021-00058-x
Capacity Strengthening Towards Application of Earth Observation Tools and
Services to Enhancing Marine and Coastal Areas Management in West Africa
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  Abstract: Abstract Capacity development forms the core and contributes to the success and sustainability of every emerging field and technology. The use of Earth Observation (EO) space technology, though not new, requires a substantial amount of capacity for its sustainable application, especially in the West Africa sub-region, where knowledge of such technology and its applications is minimal. As part of the drive to adopt EO applications and space technology in Africa, the European Commission (EC) and the African Union Commission (AUC) instituted several EO initiatives to encourage African countries to utilise EO tools and services to aid their decision-making processes. Among these initiatives is the Monitoring for Environment and Security in Africa (MESA) project (2013 to 2017), which was succeeded by the ongoing Global Monitoring for Environment and Security and Africa (GMES & Africa) programme. The implementation of the MESA and GMES & Africa programmes has, at their core, capacity development strategies to help use EO data and services for mitigating the numerous challenges facing the marine and coastal areas of the sub-region. The University of Ghana, being the lead implementing institution of these initiatives for the marine domain, for the West Africa region, embarked on several capacity strengthening activities to support the use of EO tools and services in addressing the challenges of marine and coastal areas. These activities span from regional online and onsite meetings, national face-to-face training, internships, fellowships, innovation challenges and formation of open-source clubs. More than 1,150 participants, from 14 beneficiary coastal countries and more than 130 academic and research institutions in marine and coastal areas, national institutions, start-ups companies, private sector and NGOs, were trained within the West Africa region. At the regional level, 36.72% constituted female trainees. The training covered areas such as EO data access, monitoring biological indicators for fisheries management, monitoring ocean conditions for ensuring safety at sea, and monitoring and mapping coastal habitats and land use.
  PubDate: 2021-10-02
  DOI: 10.1007/s41976-021-00057-y
Evaluation of ECMWF and NCEP Reanalysis Wind Fields for Long-Term
Historical Analysis and Ocean Wave Modelling in West Africa
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  Abstract: Abstract Ocean wind fields form a significant input to ocean wave models. This study evaluates the accuracy of two major reanalysis wind fields: NCEP-NCAR reanalysis-II (NNR-II) and ECMWF ERA5 wind datasets in the marine domain of West Africa. The objective is to identify the reanalysis data that best represents the wind regimes of the sub-region for use in climate studies and ocean wave modelling. The reanalysis datasets were validated against in situ measurements from PIRATA meteorological buoys in the region. Both reanalysis datasets indicate good agreement with in situ measurements and capture the variability in the wind fields. However, ERA5 wind fields outperform the NNR-II wind fields and better represents the variability in wind fields in the region. They display higher correlation coefficients and R-squared values as well as lower bias and RMSE values for all wind components at all PIRATA buoy locations. Correlation coefficients of 0.92, 0.87, 0.94, and 0.98; R-squared values of 0.83, 0.76, 0.89, and 0.96; mean bias of −0.34±0.75 ms−1, 0.25±33.75°, 0.07 ± 0.86 ms−1, and −0.21±0.96 ms−1; and RMSE values of 0.82 ms−1, 33.75°, 0.87 ms−1, and 0.98 ms−1 were observed for ERA5 resolved wind speeds, wind directions, and zonal and meridional winds respectively. NNR-II also recorded correlation coefficients of 0.64, 0.7, 0.73, and 0.9; R-squared values of 0.19, 0.39, 0.32, and 0.79; mean bias of 0.12±1.77 ms−1, 8.91±53.43°, 0.55±2.09 ms−1, and −0.31±2.15 ms−1; and RMSE values of 1.77 ms−1, 54.17°, 2.16 ms−1, and 2.17 ms−1 for resolved wind speeds, wind directions, and zonal and meridional winds, respectively. NNR-II winds tend to highly overestimate zonal wind speeds and underestimate meridional wind speeds. Meridional winds are better predicted compared to zonal winds for both NNR-II and ERA5 winds. There was a general overestimation of lower wind speeds and underestimation of higher wind speeds on the part of both reanalysis datasets although this assertion varied with geographical location. To enhance the accuracy of resolved wind velocities and directions in the region, there is the need to improve the estimation of zonal winds in general by both NNR-II and ERA5 winds but with much efforts needed for NNR-II. In effect, ERA5 reanalysis winds better describe the wind regime of West Africa for climate studies and ocean wave modelling.
  PubDate: 2021-08-18
  DOI: 10.1007/s41976-021-00052-3