Operational Land Imager Data Research Articles

Degradation of Urban Green Spaces in Lagos, Nigeria: Evidence from Satellite and Demographic Data

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.

Improved cloud detection for Landsat 8 images using a combined neural network model

ABSTRACTHigh-precision cloud detection is a key step in the processing of remote sensing imagery. However, the existing cloud detection methods struggle to extract high-accuracy cloud pixels, especially for images of thin and fragmented clouds or those over high-brightness surfaces. In this study, we developed a new model by combining the existing models of Fully Convolutional Network-8 sample (FCN-8s) and U-network (U-net) (based on the three visible bands) to take full advantage of spectral and spatial information. In the proposed Fully Convolutional Network Ensembling Learning (FCNEL) model, U-net and FCN-8s initially conduct separate classifications based on their relative strengths, and their outputs are fused by the voting strategy to integrate multi-scale features from both the models. Different surface and cloud types in Landsat 8 Operational Land Imager (OLI) data were used to test the model, which showed an average overall accuracy of 91.68% and an average producer accuracy of 98.52%. Thus, the proposed FCNEL model was superior to FCN-8s or U-net as well as the widely used function of mask algorithm. The proposed method has good adaptability to various cloud types and diverse underlying surface environments.

A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires

Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the southwestern United States are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzky–Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the TIMESAT analysis package. Results showed that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession pathways exhibit distinct phenometric characteristics as early as year 5 (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time-series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape compositions to inform management strategies.

Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

Evaluation of Incident Light Sensors on Unmanned Aircraft for Calculation of Spectral Reflectance

Including incident light sensors (ILS) with multispectral sensors is an important development for agricultural remote sensing because spectral reflectances are necessary for accurate determination of plant biophysical variables such as leaf area index and leaf chlorophyll content. Effects of different aircraft flight conditions on accuracy of surface reflectances retrieved using an ILS are not known. The objectives of this study were to assess the effects of ILS orientation with respect to sun and aircraft altitude. A Tetracam Miniature Multiple Camera Array (Mini-MCA) was mounted on a fixed-wing unmanned aircraft system (UAS) with the ILS mounted on top of the aircraft’s fuselage. On two dates the aircraft flew over six 50-ha agricultural fields with center-pivot irrigation at three different altitudes (450, 650 and 1800 m above ground level (AGL)). Ground reflectances were estimated using atmospherically corrected Landsat 8 Operational Land Imager data acquired at or near the time of the aircraft overflights. Because the aircraft had a positive pitch during flight, the ILS pointed opposite to the flight direction. The first date had flight lines closely oriented towards and away from the sun. The second date had flight lines oriented perpendicularly to the solar azimuth. On the first date, red and near-infrared (NIR) reflectances were significantly higher when the ILS was oriented away from the sun, whereas ILS orientation had little effect on the second date. For both dates, red and near-infrared reflectances were significantly greater at 450 m compared to 1800 m. Both the effects of ILS orientation and flight altitude are correctable during image processing because the physical basis is well known.

Determination of optically inactive water quality variables using Landsat 8 data: A case study in Geshlagh reservoir affected by agricultural land use

Water chemical variables such as total nitrogen (TN) and total phosphorus (TP) are soluble and are optically inactive. Remote sensing (RS) technique is able to monitor the optical activity of water but it has limited capability to monitor the TN and TP concentrations. In order to determine the TN and TP concentrations of Geshlagh Reservoir (GR), RS was used. The other water quality variables that were utilized included chlorophyll-a, Secchi disk depth (SDD) and total suspended solids (TSS). These variables are optically active and have a high correlation coefficient with TN and TP concentrations. The five images of Landsat 8 Operational Land Imager (OLI) taken at different times were used. Artificial neural network (ANN) model and linear regression (LR) model were utilized to determine the relationship of OLI data and TP and TN concentrations in GR. Results showed that the TN and TP concentrations have a high correlation with chlorophyll-a concentration. The spectral reflectance of bands ratio (band3/band2) and the single band of bands 3 and 4 were obtained as the most suitable bands to determine the chlorophyll-a concentration in GR. Compared with LR model, the accuracy of the ANN model was higher in the testing phase of TP prediction with R = 0.81 and TN prediction with R = 0.93. It can be concluded that the Landsat 8 OLI data has a high potential to provide the data of solubility variables of the water quality for the management of reservoirs. The contribution of this paper is that the developed ANN model will improve the prediction accuracy of inactive water quality variables based on the RS data.

Assessment of atmospheric correction results by iCOR for MSI and OLI data on TSS concentration

Atmospheric correction is very important process to determine of land and ocean surface properties measured from satellite data, especially optical remote sensing satellite system, because passive satellite instruments will always be contaminated by the influence of the atmosphere. The result of this processing is the surface reflectance (sr) product, and it is a necessary process when quantitatively monitoring environmental quality parameters from space. The goal of this study is to assessing of the spectral remote sensing reflectance satellite (Rrs(λ) by the image correction for atmospheric effects (iCOR) tools on total suspended solid (TSS) concentration from the MultiSpectral Instrument (MSI) sensor on-board Sentinel-2 and the Operational Land Imager (OLI) sensor on-board Landsat-8. Involvement of 25 in-situ TSS stations in Kendari bay waters is to assess the results of iCOR-S2 and iCOR-L8. An assessment of the sr results reduced to Rrs(λ) on the MSI and OLI data respectively, affected the value of R2 where the highest value R2 = 0.665 is shown on red band OLI data. Meanwhile, the assessment of three TSS algorithms models is built on Rrs(λ), all of them showed mean relative error (MRE) < 30% and were considered capable of defining TSS concentrations in the study area.

Remote Monitoring of PSD Slope Under the Influence of Sand Dredging Activities in Lake Hongze Based on Landsat-8/OLI Data and VIIRS/DNB Night-Time Light Composite Data

Particle size distribution (PSD) is an important characterization parameter of the particle size structure of suspended particles, which is vulnerable to human activities such as sand dredging. In this study, sand dredging numbers were evaluated by the accumulated radiance algorithm using the night-time light composite data of the visible infrared imaging radiometer suite, and the PSD slope was derived by band ratio algorithm of Landsat-8/operational land imager (OLI) imagery. Then, the influence of sand mining on PSD slope was analyzed. We draw several crucial findings. First, the ratio of ${\boldsymbol{R}_{{{\bf rs}}({{{\bf Red}}})}}$ to ${\boldsymbol{R}_{{{\bf rs}}({{{\bf Green}}})}}$ based on OLI imagery was capable of retrieving PSD slope with good robustness ( R 2 = 0.71, p n = 41) and verified acceptance (mean absolute percentage error = 4.29%, root mean square error = 0.19, n = 20), and the PSD slope in HZL was lower in summer than in the other seasons from 2014 to 2017. Second, The night active vessels extracted from the night-time images show that sand dredging activities began at the outlet of Chengzi Lake (CZL) in April 2012, spread to the whole lake between 2014 and 2016, and then stopped in the northeast region in March 2017 due to the strict control of the local government. Third, the PSD slope is strongly influenced by sand mining, which is positively correlated with the number of sand dredgers ( R 2 = 0.92), i.e., the PSD slope increases 0.21 per 100 working sand dredgers in CZL.

On-Orbit Measurement of the Effective Focal Length and Band-to-Band Registration of Satellite-Borne Whiskbroom Imaging Sensors.

We have developed an approach for the measurement of the Effective Focal Length (EFL) and Band-to-Band Registration (BBR) of selected spectral bands of satellite-borne whiskbroom imaging sensors from on-orbit data. Our approach is based on simulating the coarser spatial resolution whiskbroom sensor data with finer spatial resolution Landsat 7 ETM+ or Landsat 8 OLI data using the geolocation (Earth location) information from each sensor, and computing the correlation between the simulated and original data. For each scan of a selected spectral band of the whiskbroom data set, various subsets of the data are examined to find the subset with the highest spatial correlation between the original and simulated data using the nominal geolocation information. Then, for this best subset, the focal length value and the spatial shift are varied to find the values that produce the highest spatial correlation between the original and simulated data. This best focal length value is taken to be the measured instrument EFL and the best spatial shift is taken to be the registration of the whiskbroom data relative to the Landsat data, from which the BBR is inferred. Best results are obtained with cloud-free subsets with contrasting land features. This measurement is repeated over other scans with cloud-free subsets. We demonstrate our approach with on-orbit data from the Aqua and Terra MODIS instruments and SNPP and J1 VIIRS instruments.

Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product

Landsat and Sentinel-2 sensors together provide the most widely accessible medium-to-high spatial resolution multispectral data for a wide range of applications, such as vegetation phenology identification, crop yield estimation, and forest disturbance detection. Improved timely and accurate observations of the Earth's surface and dynamics are expected from the synergistic use of Landsat and Sentinel-2 data, which entails coordinating the spatial resolution gap between Landsat (30 m) and Sentinel-2 (10 m or 20 m) images. However, widely used data fusion techniques may not fulfil community's needs for generating a temporally dense reflectance product at 10 m spatial resolution from combined Landsat and Sentinel-2 images because of their inherent algorithmic weaknesses. Inspired by the recent advances in deep learning, this study developed an extended super-resolution convolutional neural network (ESRCNN) to a data fusion framework, specifically for blending Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) data. Results demonstrated the effectiveness of the deep learning-based fusion algorithm in yielding a consistent and comparable dataset at 10 m from Landsat-8 and Sentinel-2. Further accuracy assessments revealed that the performance of the fusion network was influenced by both the number of input auxiliary Sentinel-2 images and temporal interval (i.e., difference in image acquisition dates) between auxiliary Sentinel-2 images and the target Landsat-8 image. Compared to the benchmark algorithm, area-to-point regression kriging (ATKPK), the deep learning-based fusion framework proved better in the quantitative assessment in terms of RMSE (root mean square error), correlation coefficient (CC), universal image quality index (UIQI), relative global-dimensional synthesis error (ERGAS), and spectral angle mapper (SAM). ESRCNN better preserved the reflectance distribution as the original image compared to ATPRK, resulting in an improved image quality. Overall, the developed data fusion network that blends Landsat-8 and Sentinel-2 images has the potential to help generate continuous reflectance observations of higher temporal frequency than that can be obtained from a single Landsat-like sensor.

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks.

Conjugate utilization of Landsat-8 OLI, ground gravity and magnetic data for targeting mafic cumulates within anorthositic-layered complex of Sittampundi, India

In this article, we have processed Landsat-8 spectral bands, ground gravity, and magnetic data for delineating the mafic cumulates within the anorthositic-layered complex in Sittampundi, Tamilnadu, India. In this regard, surface anomaly map of mafic cumulates was derived by processing spectral bands of Operational Land Imager (OLI) sensor using matched filtering method. Residual gravity, magnetic anomaly maps are also derived. Spatial position of geophysical anomalies corroborate well with the spectral anomalies. A promising zone of chromitite occurrences is identified at the eastern fringe of the study area based on the concurrence of spectral and geophysical anomalies. Spatial profiles of geophysical and spectral anomalies around this promising zone are similar to the respective spatial profiles observed around the known chromite bearing mafic cumulates. Depth continuity of the mafic cumulates is confirmed by deriving of gravity and magnetic anomaly map at different depth using upward continuation filters to confirm concealed deposit.

Method for predicting soil salinity concentrations in croplands based on machine learning and remote sensing techniques

To assess the potential of machine learning methods for predicting and mapping soil salinity in highly vegetated and slightly salt-affected croplands, a random forest (RF) regression model was constructed based on the measured soil salinity and spectral indices of remote sensing data, Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS). The model was evaluated under different soil salinity conditions and with different resolutions of imagery data (Landsat 8 OLI and MODIS) to compare it with the multivariate adaptive regression splines (MARS) model. The results indicated that a higher accuracy estimation can be achieved from the RF model under conditions of poor correlations between the soil salinity and the spectral indices in the highly vegetated croplands, with a cross-validation determination coefficient ( R 2 ) = 0.86, root-mean-square error ( RMSE ) = 1.83, and ratio of performance to deviation ( RPD ) = 2.7; these were better than the MARS model ( R 2 = 0.81, RMSE = 4.8, and RPD = 1.96). The performance of the MARS model shows sensitivity to the variety of salinity levels. Estimated total salinity maps of Jiashi County were produced using the RF model based on OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data for September 2015 and April 2016, respectively. Acceptable mapping accuracy was achieved from the cross validation of the salinity mapping results for September 2015 ( R 2 = 0.84 and 0.81 for OLI and MODIS data, respectively) and April 2016 ( R 2 = 0.85 and 0.82 for OLI and MODIS data, respectively). The results indicated that the method developed is reliable and accurate for digital soil salinity mapping of slightly and moderately salt-affected croplands.

Retrieval of Inorganic Suspended Particle Size with Landsat-8 OLI Data in the Yellow River Estuary

Qing, S.; Hao, Y.L., and Bao, Y.H., 2019. Retrieval of inorganic suspended particle size with Landsat-8 OLI data in the Yellow River estuary. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 19-26. Coconut Creek (Florida), ISSN 0749-0208.Particle size distribution (PSD) is relevant to many physical and biogeochemical processes in marine ecosystems. Field investigation or satellite data with coarse resolution have limitations in description of detailed PSD in a small scale region, e.g. river estuary. In this study, Landsat-8 Operational Land Imager (OLI) data was used for estimating inorganic suspended particle size in the Yellow River estuary. A simple empirical model using ratio of green to red bands was developed based on in situ measured remote sensing reflectance (Rrs) and median particle size (Dv50) for inorganic materials. Validation against in situ measurements showed that retrieved Dv50 was in good accordance with in situ measurements with the mean absolute percentage error (MAPE) of 24.0 % and root mean square error (RMSE) of 4.310µm. Application of the model to OLI data retrieved a clear and reasonable spatial distribution of Dv50 in the Yellow River estuary. OLI retrieved Dv50 agreed well with that from MODIS data (R2=0.89, MAPE=6.4 %, RMSE=0.6 µm). Dv50 maps characterized a river mouth to offshore changing trend, demonstrating the model potential for understanding of the detailed information of a local scale region. The empirical algorithm developed here can be easily applied to other satellite data with similar bands of Landsat-8 OLI data and fine spatial resolution.

Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone

Fire is used worldwide to clear natural vegetation areas for economic activities and to manage the regeneration of already opened sites. In Brazil, fire has been traditionally used to convert natural vegetation areas to agricultural lands (slash and burn) and to manage pastures for livestock. We developed the Burned Area Spectral Mixture Analysis (BASMA) algorithm in Google Earth Engine, which is designed to process Landsat data to produce a multi-temporal fire scar database representing annual burned area for an extent of 362,000 km2 in the transition zone between the Amazon forest and the Cerrado biome. This region is considered a conservation hotspot, given its high deforestation rates over the last four decades. We digitally processed a 32-year time-series (1985 to 2017) of Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper+, and Landsat 8 Operational Land Imager data to map fire scars based on sub-pixel char fraction, aiming to generate a consistent burned area product at a finer scale and covering a longer period than those currently available for the region. Manually interpreted reference burned area polygons for each annual mosaic was used to guide the definition of the best fire scar endmember and its fraction threshold. To assess our BASMA-delineated fire scar, they were compared to independent datasets of manually delineated burned area produced by visual analysis of finer spatial resolution imagery, returning an average Dice Coefficient value of 0.86. Accuracy was also measured against the 30-meter Burned Area product available at the ‘Queimadas’ data portal. A total of 11,106,258 ha was mapped as having been affected by fire during the annual dry season over the 32 years, which represents 30.7% of the study region. Results showed a decreasing trend in the annual amount of burned area over the time-series. It reflects a similar pattern shown in the deforestation rate for the Legal Amazon, measured by the Brazilian National Institute for Space Research - INPE. Moreover, the Cerrado biome subset of the study region consistently showed higher burned area when compared to the Amazon forest subset. Our findings provide robust evidence that our approach is a consistent method to identify and delineate fire scars for large areas over a long time-series in a very efficient fashion, given the digital processing power of Google Earth Engine, which reduces the time necessary to analyze such big amount of data.

Fusing MODIS and OLI images: evaluating applications to winterwheat mapping

Moderate resolution imaging spectroradiometer (MODIS) series products are widely used to observe Earth’s surface, but the coarse resolution of MODIS images may negatively affect the monitoring accuracy. In this paper, MODIS vegetation index data (NDVI, normalized difference vegetation index; EVI, enhanced vegetation index) were fused with operational land imager (OLI) data (Landsat 8 OLI: pan band) using two image transform methods (WT, wavelet transform; GST, Gram-Schmidt transform) to improve the accuracy of winter wheat mapping. The findings showed that image fusion resulted in increased information entropy for image interpretation; however, the mapping accuracy was improved based on the fused OLI and MODIS images for only the OLI pan image acquired during the winter wheat growing season. Winter wheat was more often misclassified as urban green spaces in the summer than in the spring. For the entire study area, the overestimation of winter wheat based on the fused EVI and OLI data and the WT algorithm reached 5.98%. The time-series curves of the fused images were similar to those of the MODIS images, suggesting that time-series MODIS images might have expanded application in phenology-based wheat mapping.

Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery

Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentation-merge-fitting method, the algorithm determines the subpixel location accurately. In experiments, we select five artificial shorelines with various geometric morphologies from Landsat 8 Operational Land Imager (OLI) data. The five subpixel artificial shoreline RMSE results lie in the range of 3.02 m to 4.77 m, with line matching results varying from 2.51 m to 3.72 m. Thus, it can be concluded that the proposed subpixel localization algorithm is effective and applicable to artificial shoreline in various geometric morphologies and is robust to complex offshore environments, to some extent.

Height Above Nearest Drainage (HAND) model coupled with lineament mapping for delineating groundwater potential areas (GPA)

In the 21st century, global warming, erratic rainfall and the pressure on groundwater resources are becoming severe and leading to water scarcity, which contributes to the poverty of around one-third of the world's people. Hence, it became necessary to use new geospatial technologies for mapping and monitoring of groundwater resources. Spatial analysis of hydro-geological lineament network and hydrological modeling may allow accurate identification of areas that favor infiltration and recharge of overexploited groundwater. In Morocco, the Middle Atlas region suffers from a lack of reliable hydro-geological maps for local water resource managers. Consequently, this research aims to assess the relative contribution of the topo-hydrological factor, known as Height Above Nearest Drainage (HAND) for efficient exploration and management of groundwater resources in central Middle Atlas. The methodology was based on the Landsat-8 Operational Land Imager (OLI) data for retrieving the lineaments and the elevation data from Shuttle Radar Topography Mission (SRTM) for the calculation of the HAND model. Four landscape classes namely, waterlogged land, wetlands sloped, hillslope and plateaus, corresponding to the main hydrological systems, were delineated using the HAND model. In addition, fissured carbonate plateaus, faults, karsts, water sources, lakes and groundwater levels have been used to develop a decision support system based on geospatial criteria analysis. The fracture analysis indicated that the study area has many long and short hydro-geological lineaments, with the highest densities oriented NE–SW. Their synergy with the HAND model and the other factors has allowed the detection of potential areas for the recharge of the groundwater table. The results have been confirmed and validated using lakes, water sources and piezometric levels. The application of the HAND factor as an independent variable in the delineation of groundwater potential areas (GPAs) is demonstrated to be useful for improving the accuracy of the existing multi-criteria GIS models.