Multispectral Satellite Imagery Research Articles

Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets

Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters.

GEOMATICS TECHNOLOGIES IN THE FRAMEWORK OF MULTIDISCIPLINARY PROJECT FOR INTEGRATED MANAGEMENT OF CULTURAL HERITAGE SITES

Abstract. Cultural heritage represents the link between past and present civilizations, contributing to the right appreciation of the present-day by understanding the historical evolution of different human communities that are shaping our world. Preservation of the cultural heritage and mitigating potential risks on invaluable artifacts or objectives is key to conserving the identity of present and future generations. Our complex project is dedicated to monitoring, conservation, protection and promotion of the Romanian cultural heritage. It addresses the challenge of better safeguarding the cultural heritage by developing a multidisciplinary approach.The combined use of Synthetic Aperture Radar and multispectral satellite imagery represent a non-invasive investigation instrument that enable multitemporal analysis, contributing with valuable insights regarding cultural heritage sites. Remote sensing techniques were used to generate geospatial products such as displacement maps, urban sprawl evolution maps, urban heat island maps and many more.For protecting important artifacts, innovative recipes based on nanotechnologies were developed. In order to promote these artifacts, 3D photorealistic models were created using 3D reconstruction technology. For selected cultural heritage objectives, an integrated LiDAR aerial mapping system has enabled the creation of a high accuracy 3D model.This paper illustrates how different technologies, ranging from remote sensing to nanotechnology, could be used for safeguarding and promoting the Romanian cultural heritage and represents a first step in establishing and consolidating a multidisciplinary team involved in this domain.

SPATIAL RESOLUTION ENHANCEMENT OF LAND COVER MAPPING USING DEEP CONVOLUTIONAL NETS

Abstract. Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover at the global scale. However, the accuracy of land cover classification is often constrained by the spatial and temporal resolutions of the acquired satellite images. This paper proposes a novel spatiotemporal fusion method based on deep convolutional neural networks under the application background of massive remote sensing data, as well as the large spatial resolution gaps between MODIS and Sentinel images. The training was taken on the public SEN12MS dataset, while the validation and testing were conducted using ground truth data from the 2020 IEEE GRSS data fusion contest. As a result of data fusion, the synthesized land cover map was more accurate than the corresponding MODIS-derived land cover map, with an enhanced spatial resolution of 10 meters. The ensemble approach can be implemented for improving data quality when generating a global land cover product from coarse satellite imageries.

ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA

Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.

The age of surface-exposed ice along the northern margin of the Greenland Ice Sheet

AbstractEach summer, surface melting of the margin of the Greenland Ice Sheet exposes a distinctive visible stratigraphy that is related to past variability in subaerial dust deposition across the accumulation zone and subsequent ice flow toward the margin. Here we map this surface stratigraphy along the northern margin of the ice sheet using mosaicked Sentinel-2 multispectral satellite imagery from the end of the 2019 melt season and finer-resolution WorldView-2/3 imagery for smaller regions of interest. We trace three distinct transitions in apparent dust concentration and the top of a darker basal layer. The three dust transitions have been identified previously as representing late-Pleistocene climatic transitions, allowing us to develop a coarse margin chronostratigraphy for northern Greenland. Substantial folding of late-Pleistocene stratigraphy is observed but uncommon. The oldest conformal surface-exposed ice in northern Greenland is likely located adjacent to Warming Land and may be up to ~55 thousand years old. Basal ice is commonly exposed hundreds of metres from the ice margin and may indicate a widespread frozen basal thermal state. We conclude that the ice margin across northern Greenland offers multiple opportunities to recover paleoclimatically distinct ice relative to previously studied regions in southwestern Greenland.

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

Investigating mass balance of Parvati glacier in Himalaya using satellite imagery based model

Accurate assessment of glacier mass loss is essential for understanding the glacier sensitivity to climate change and the ramifications of glacier retreat or surge. The glacier melt affects the runoff and water availability, on which the drinking and irrigation water supplies and generation of hydroelectric energy depend upon. The excessive glacial retreat may cause flood, glacial lake outburst flood, avalanches and sea level rise which are likely to affect the lives and livelihood of the people and damage the infrastructure. Here, we present a remote sensing based modeling framework to improve the understanding of accumulation and ablation processes and to quantify the glacier mass balance using multispectral satellite imageries, as several glacierized regions of the world are still poorly monitored because the field measurements for continuous monitoring on a large scale or in a complex harsh terrain are costly, time consuming and difficult. The developed modeling framework has been applied to the Parvati glacier in the western Himalaya to investigate glaciological processes and estimate the surface mass loss using 19 years of satellite images from 1998 to 2016. It spreads over 425.318 km2 and more than 50% of the area is accumulation area. The study shows that the Parvati glacier is not in equilibrium and its behavioural response changes year to year characterized with high rate of mass loss. The value of accumulation area ratio varies between 0.33 and 0.70 with an average value of 0.55, indicating a negative mass loss. The mean specific mass loss is − 0.49 ± 0.11 m w.e. and the total mass loss is 3.95 Gt., indicating strong influence of climate change and effect on river flows and water availability.

A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining

Artisanal Small-scale Mining (ASM) is a critical source of livelihoods for large areas of the Global South but it can bring with it many problems, including deforestation, water pollution and low worker safety. Timely and comprehensive management of ASM is crucial to ensure that it can take place safely and cleanly, supporting sustainable development. The informal nature of the sector presents challenges related to documenting the locations of ASM. Remote sensing methods have been used to detect ASM, although difficulties with accuracy, resolution and persistent cloud cover have been encountered. This paper proposes a method of ASM detection using a deep convolutional neural network model applied to open source Sentinel-2 multispectral satellite imagery. Firstly, the model is evaluated against both existing ASM detection methods and visual inspection of randomly sampled points. Secondly, the model is used to map mining and urban land use changes over a dataset spanning four years and 6 million hectares of southern Ghana, demonstrating the ability of this method to process very large areas.The omission and commission errors of less than 8% from the sampled points indicate that this model has achieved unprecedented levels of accuracy for the task of detecting ASM from satellite imagery. When applied to the case study area, the data on ASM trends over time demonstrate a correlation between the Ghanaian government's 2017 clampdown and ASM activities. The ASM land use category decreased by 6000 ha in 2017, despite a net increase of 15000 ha over the period 2015–2019. Additionally, the model was applied to quantify the extent of illegal mining related deforestation within Ghana's protected forests, measured at over 3500 ha, with 2400 of these lost since 2015. The results demonstrate that this methodology can detect ASM in Ghana with a high degree of accuracy at a minimal cost in terms of financial and human resources. The model shows strong generalisation abilities, offering exciting potential for using this methodology to further monitor and analyse ASM related land use changes worldwide.

Combining Satellite Multispectral Imagery and Topographic Data for the Detection and Mapping of Fluvial Avulsion Processes in Lowland Areas

Fluvial avulsion is an important process in the dynamics of the riverscapes and plays a key role in the drainage network evolution in lowland areas, also influencing past and present social processes and economic activities. Crevasse splays represent significant geomorphological features for understanding the fluvial morphodynamics in lowland areas dominated by avulsion processes. Within wide floodplains characterized by very low elevation ranges, the detection and accurate mapping of crevasse splay morphology and features, such as crevasse channels, levees, and deposit, can be very challenging considering floodplain extension, anthropic impact on the natural channels network, logistic difficulties, and in some cases, climate conditions that prevent field work. This research aims at improving the detection and mapping of crevasse splays in lowland areas through the combination of different remote sensing techniques based on optical multispectral imagery and topographic data derived from satellite earth observation missions. The Lower Mesopotamia Plain (LMP) offers a unique opportunity to study the avulsion processes because it presents numerous examples of crevasse splays, characterized by different sizes and states of activity. Furthermore, in this area, a strong correlation exists between the formation and development of crevasse splays and the expansion of agriculture and early societies since the Early Holocene. Different supervised classification (SC) methods of Landsat 8 satellite images have been tested together with topographic analysis of the microrelief, carried out based on two different 1-arcsec DEMs (AW3D30 and GDEM2). The results of this study demonstrate that the combination of multispectral imagery analysis and topographic analysis of the microrelief is useful for discerning different crevasse elements, distinguishing between active and relict landforms. The methodological approach proved helpful for improving the mapping of erosional and depositional landforms generated by the avulsion process and, in the study area, provided the best results for the active landforms.

Automatic building footprint extraction from very high-resolution imagery using deep learning techniques

Building footprint maps are useful for urban planning, infrastructural development, population estimation and disaster management. With the availability of very-high resolution satellite imagery, remote sensing community is pursuing automatic techniques for extracting building footprints for cities with varried building types. Recently, CNNs (Convolutional Neural Network) have been successfully applied for extraction of building footprint from satellite imagery. In this paper, we propose a novel CNN architecture termed UNet-AP inspired by UNet and the concept of Atrous Spatial Pyramid Pooling, for automatic extraction of building footprint from very-high resolution satellite imagery. We demonstrate extraction of building footprints from Cartosat-2 series 4-band (Blue, Green, Red and Near-Infrared) multispectral satellite imagery, pan-sharpened using the panchromatic image with less than 1-meter resolution. We also compare the performance of our proposed architecture with baseline implementation of recently proposed UNet and SegNet architectures. We present a comparative assessment of the architecture performance across different types of urban settlement classes such as dense built-up areas, slums and isolated buildings. We demonstrate that our proposed architecture outperforms SegNet and UNet in terms of overall mean intersection over union (0.75 vs 0.70 and 0.68 for UNet and SegNet respectively) and delivers consistent improvement across all three settlement classes.

Estimating Mo, Cu, Ni, Cd Contents in the Crop Leaves Growing on Small Land Plots Using Satellite Data

ABSTRACT The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R2 cal. = 0.79, R2 CV = 0.70, RMSEcal. = 11.27, RMSECV = 13.47) and ANN (R2 Train ≈ 0.80, R2 Test ≈ 0.72, RMSETrain ≈ 11, RMSETest ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.

Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas.

Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.

Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme

Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including Q A A V 5 , Q A A V 6 , Q A A L 09 and Q A A M 14 , for the AHSI images with concurrent in situ measurements in four inland water bodies with a Z s d range of 0.3–4.5 m. The semi-analytical method with Q A A V 5 can yield the most accurate Z s d predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated Z s d had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated Z s d were 0.22, 0.46, and 0.24 m for Z s d of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring.

Development of a tool for automatic bare soil detection from multitemporal satellite optical imagery for digital soil mapping applications

Understanding the variability of soil attributes allows to improve the farm production efficiency, accompanied by a reduction in environmental impacts and effective usage of resources. Several studies confirmed the potential of optical remote sensing data for quantifying soil attributes, such as clay content, soil organic carbon and texture classes A challenging issue in spatial-temporal soil surveying by remote sensing data is the limited availability of cloud-free images or affected by cloud/shadow. Further, imagery with high temporal resolution is extremely important for observing terrestrial surfaces. This study investigates the use of multispectral (Sentinel-2 MSI) satellite imagery at the regional/local scale, for the automated detection of agricultural bare soil occurrence, exploiting bands covering the spectral range from visible to shortwave infrared. The study objective is to provide bare soil time series that could be subsequently exploited in digital soil mapping (DSM) approaches based on multispectral or, also in view of the next future missions, hyperspectral remote sensing data.

Evapotranspiration and crop coefficients from lysimeter measurements for sprinkler-irrigated canola

Canola is a water-stress tolerant crop, which could be an alternative in areas with limited water resources. However, in arid and semi-arid environments where rainfall events are scarce and increasingly erratic, the use of irrigation is necessary for canola production to reach its maximum yield. The goal of this study was to determine the crop evapotranspiration (ETc) and crop coefficients of sprinkler irrigated canola (Brassica napus L.) under non-limiting soil water content conditions. A 2-year field experiment was conducted in the lysimeter facility located in Albacete (SE Spain). A large weighing lysimeter (2.7 × 2.3 × 1.7 m), with an accuracy of 0.04 mm equivalent water depth, was used to measure the daily crop evapotranspiration (ETc) rate throughout two growing seasons. ETc values were determined using daily mass change in the lysimeter. Cumulative ETc was replaced in the lysimeter through sprinkler irrigation applications, thus crop water stress was avoided. Seasonal lysimeter based (measured) canola ETc was 472 and 602 mm in 2008 and 2012, respectively. The 28 % higher ETc value in 2012 was mainly due to a much higher evaporative demand during the crop growth mid-season period of 2012. The Kc values were determined using grass reference evapotranspiration (ETo) calculated with the FAO56 Penman-Monteith equation and the ETc calculations from the lysimeter data. The dual crop coefficient approach was used to separate crop transpiration (Kcb) from soil evaporation (Ke). For the two canola seasons, mid-season Kc and Kcb values, after FAO56 climate adjustment, were Kc mid (std) = 1.15 and Kcb mid (std) = 1.11. Those values were reached coinciding with maximum fraction of ground cover (fc) values of up 0.95 and 0.97 for 2008 and 2012, respectively. The seasonal evaporation component for sprinkler-irrigated canola was estimated to be about 24 % and 19 % of ETc in 2008 and 2012, respectively. The good linear relationship found between canola Kcb values and fc and the excellent agreement found between remotely sensed vegetation indices (VIs) and different biophysical parameters, such as Kcb and fc, will allow monitoring and estimating the spatially distributed water requirements of canola at field and regional scales using multispectral satellite imagery.

Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country.

Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes

Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.

Nearshore Bathymetry From Fusion of Sentinel-2 and ICESat-2 Observations

Nearshore estimates of bathymetry are crucial for understanding coastal processes. However, current passive remote sensing methods for estimating bathymetry require in situ depth measurements to train inversion models, which can be difficult or impossible to obtain in many areas. To address this issue, we investigated the fusion of range measurements from the advanced topographic laser altimeter system (ATLAS) aboard the NASA ICESat-2 satellite, and multispectral satellite imagery from European Space Agency (ESA) Sentinel-2 using two common bathymetric inversion algorithms. The active ranging capability of the ATLAS green (532-nm) laser has been shown to generate returns of up to 38-m depth in optically clear waters, providing depth measurements to constrain passive bathymetric inversion results. Data acquired in November 2018 over the nearshore in Destin, FL, USA, offer a proof of concept for this approach. The results of the bathymetric inversion were quantitatively assessed by comparison with airborne bathymetric LiDAR collected using the U.S. Army Corps Coastal Zone Mapping and Imaging LiDAR (CZMIL) system in October-November 2018. Overall, the results of the bathymetric inversion compared with the CZMIL data have a root mean square error (RMSE) of 0.35 m in waters with similar turbidity and bottom reflectivity, and demonstrate that a combination of ICESat-2 depth observations with Sentinel-2 multispectral imagery can estimate seamless nearshore bathymetry for optically clear coastal waters.

Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

The first national product of Surface Water Dynamics in France (SWDF) is generated on a monthly temporal scale and 10-m spatial scale using an automatic rule-based superpixel (RBSP) approach. The current surface water dynamic products from high resolution (HR) multispectral satellite imagery are typically analyzed to determine the annual trend and related seasonal variability. Annual and seasonal time series analyses may fail to detect the intra-annual variations of water bodies. Sentinel-2 allows us to investigate water resources based on both spatial and temporal high-resolution analyses. We propose a new automatic RBSP approach on the Google Earth Engine platform. The RBSP method employs combined spectral indices and superpixel techniques to delineate the surface water extent; this approach avoids the need for training data and benefits large-scale, dynamic and automatic monitoring. We used the proposed RBSP method to process Sentinel-2 monthly composite images covering a two-year period and generate the monthly surface water extent at the national scale, i.e., over France. Annual occurrence maps were further obtained based on the pixel frequency in monthly water maps. The monthly dynamics provided in SWDF products are evaluated by HR satellite-derived water masks at the national scale (JRC GSW monthly water history) and at local scales (over two lakes, i.e., Lake Der-Chantecoq and Lake Orient, and 200 random sampling points). The monthly trends between SWDF and GSW were similar, with a coefficient of 0.94. The confusion matrix-based metrics based on the sample points were 0.885 (producer's accuracy), 0.963 (user's accuracy), 0.932 (overall accuracy) and 0.865 (Matthews correlation coefficient). The annual surface water extents (i.e., permanent and maximum) are validated by two HR satellite image-based water maps and an official database at the national scale and small water bodies (ponds) at the local scale at Loir-et-Cher. The results show that the SWDF results are closely correlated to the previous annual water extents, with a coefficient >0.950. The SWDF results are further validated for large rivers and lakes, with extraction rates of 0.929 and 0.802, respectively. Also, SWDF exhibits superiority to GSW in small water body extraction (taking 2498 ponds in Loir-et-Cher as example), with an extraction rate improved by approximately 20%. Thus, the SWDF method can be used to study interannual, seasonal and monthly variations in surface water systems. The monthly dynamic maps of SWDF improved the degree of land surface coverage by 25% of France on average compared with GSW, which is the only product that provides monthly dynamics. Further harmonization of Sentinel-2 and Landsat 8 and the introduction of enhanced cloud detection algorithm can fill some gaps of no-data regions.

Urban overspill and its upshot to the Bafoussam Emergent Metropolis in Cameroon

Unbridled urban sprawl in Sub-Saharan African cities reflects an infirmity that warrants both vaccination and treatment to curb urban disorder and its costs. This article divulges that rapid population growth, spatial expansion and economic activities are responsible for the urban overspill and its upshot in Bafoussam. Unplanned settlement from the rapid growing population has led to the development of slumps and reduction of arable land. A multi-spectral Landsat satellite imagery of 1988, 2001 and 2016 was used to determine the spatial expansion of the town over a period of 30 years. Field observation and interview sessions were done to have information on the proliferation of economic activities to the expansion of the town. An in-depth secondary data collection was done to gather information on the rate of population growth of Bafoussam. The data was processed to generate maps through the ArcGIS 10.4 and Adobe Illustrator CS soft wares and tables through SPSS 17 for results and analyses. Results from findings propound a significant increase in the built-up area of Bafoussam of 19.34% in 1988, 50.30% in 2001 and 79.41% in 2016. This increase was accompanied by a drop in the vegetation of 78.64% in 1988, 48.50% in 2001 and 20.07% in 2016. The built-up increase was provoked by a persistent rise in the population of the city from 62, 239 inhabitants in 1976 to 112,681 in 1987 and 282,800 in 2010. Industrial and commercial activities developed in the course of the twin increase and unfortunately with no control triggered urban disorder. This article advocates for a mandatory implementation of the urban norms in Cameroon to liberate Bafoussam from the drowning overspill syndrome plaguing Sub-Saharan cities.