ALBERT

Description: Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler’s First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10–PM2.5 relationship constraint. In the site-based independent test in mainland China (2015–2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R2: 8–78%, decrease in RMSE: 14–48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.

Description: The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.

Description: In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94≤R2≤0.98, 5.37≤RMSE≤7.9 and 20≤MAE≤33.

Description: Australia’s Great Barrier Reef (GBR) is a globally unique and precious national resource; however, the geomorphic and benthic composition and the extent of coral habitat per reef are greatly understudied. However, this is critical to understand the spatial extent of disturbance impacts and recovery potential. This study characterizes and quantifies coral habitat based on depth, geomorphic and benthic composition maps of more than 2164 shallow offshore GBR reefs. The mapping approach combined a Sentinel-2 satellite surface reflectance image mosaic and derived depth, wave climate, reef slope and field data in a random-forest machine learning and object-based protocol. Area calculations, for the first time, incorporated the 3D characteristic of the reef surface above 20 m. Geomorphic zonation maps (0–20 m) provided a reef extent estimate of 28,261 km2 (a 31% increase to current estimates), while benthic composition maps (0–10 m) estimated that ~10,600 km2 of reef area (~57% of shallow offshore reef area) was covered by hard substrate suitable for coral growth, the first estimate of potential coral habitat based on substrate availability. Our high-resolution maps provide valuable information for future monitoring and ecological modeling studies and constitute key tools for supporting the management, conservation and restoration efforts of the GBR.

Description: Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.

Description: In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.

Description: Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

Description: The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.

Description: Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Description: Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture.