By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: ...pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.
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•PCA is reliable for identifying and predicting air pollution variation.•WPCA and RPCA models well fitted observed NO2.•Both models accurately estimated near-surface NO2 concentrations.•Total lockdown had prolongated effect on NO2 concentrations.•Weather conditions propitiated air pollutant decrease during lockdown and increase after.
•We provide compressed data structures for raster data supporting advanced queries.•We provide extended solutions for spatio-temporal raster data.•Good compression and query performance in real-world ...applications and datasets.
The raster model is commonly used for the representation of images in many domains, and is especially useful in Geographic Information Systems (GIS) to store information about continuous variables of the space (elevation, temperature, etc.). Current representations of raster data are usually designed for external memory or, when stored in main memory, lack efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We present different representations for binary raster data, general raster data and time-evolving raster data. We experimentally compare our proposals with traditional storage mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show that our structures are up to 10 times smaller than classical linear quadtrees, and even comparable in space to non-querieable representations of raster data, while efficiently answering a number of typical queries.
To solve the problems of small area, slow calculation speed and low precision in the projection transformation algorithm of raster data, the idea of using Kriging interpolation approximate grid ...algorithm for raster data projection transformation is proposed. Through the experimental results of point-to-point transformation and Kriging interpolation approximate grid algorithm transformation under the same conditions, it can be seen that for different projection types under the same limit conditions, Kriging interpolation approximate grid algorithm can ensure that the raster data projection error is always within the given projection limit. With the same number of points, the Kriging interpolation approximate grid algorithm is faster and more efficient than the point-to-point projection algorithm. Under the same pixel condition, the Kriging interpolation approximate grid algorithm is faster, more accurate and more effective than the point-to-point projection algorithm.
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Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and ...meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ±2σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina’s extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters’ mitigation efforts.
•A novel method to construct drainage network considering rice terraces and ponds.•A hybrid vector-raster data model was used for spatial discretization.•Flow direction is determined by elevation and ...land use specific boundary rules.•The constructed drainage network are more geometrically and topologically realistic.•It is helpful for simulating residence time of water, sediments and nutrients.
A well-constructed drainage network is essential for hydrological modeling. Agricultural watersheds with heavy human alternations often contain man-made features like terraced fields and ponds. Incorporating these features in the drainage network in these watersheds is extremely important for hydrological modeling. This study proposed a novel hybrid vector-raster approach to drainage network construction in this type of regions. First, a watershed is discretized using a hybrid vector-raster data model to accurately represent the shapes and areas of irregular geographical entities. Then, the flow directions among discretized units are determined firstly within each land use type and then on the boundaries of different land use types based on elevation and land use specific boundary rules. After that, loops among units are processed to make sure each unit can flow out of the watershed. A small agricultural watershed in Jiangsu Province, China was selected to conduct a case study, and the results showed that the proposed method can obtain a more realistic drainage network than traditional methods, both in terms of geometric shapes and spatial topology.
The raster data structure stores categorical and continuous field data for spatial analysis, environmental modeling, and resource planning. With rapidly advancing sensor networks, the spatial ...resolution of data is increasing, sometimes outpacing the optimum resolution for applications. Overcoming granularity differences between raw and "analysis ready" data often requires upscaling source data to a desired target map with the goal of maintaining the structure and spatial variance of the higher resolution data. Common strategies for resampling categorical data (nearest neighbor and majority rule) force users to choose between preserving map structure and map variety. A new method is presented here that integrates global and zonal class proportions to guide the optimal allocation of classified cells. This technique provides more representative maps with respect to variety and structure, better retains minority classes, and produces higher (or equal) levels of user's and producer's accuracy than the traditional methods. An R-based implementation is provided that has serviceable run times, and the performance of the algorithm is shown to be scalable, proving the tool widely usable.
•Site-connected habitat amount accounts for movements across habitat networks.•Integrated habitat amount evaluates aggregations of connected habitat in area units.•Together these provide a consistent ...and equitable method for calculating potential occupancy.•Raster-based assessment best represents fine-grained, heterogeneous habitats.•We resolve issues of equitable trading between habitat quality and extent.
Landscape connectivity measures based on metapopulation theory were developed over 20 years ago. Initially, they applied classic metapopulation models to simple patch-based representations of landscapes using vector spatial data structures. Realism was improved by developing dynamic estimates of occupancy and metapopulation capacity, the latter providing a measure of the integrated habitat amount. Such measures are used to estimate the ability of habitat networks to support metapopulation persistence. The original methods for occupancy mapping and metapopulation capacity were adapted to work with fine-grained, continuous-value raster data. That step shifted the method outside of the classic metapopulation model which left some methodological issues unresolved; in particular, what has been termed the deceptive paradox of patch-based connectivity whereby perverse and what we describe as inequitable results are obtained through arbitrary circumscription of the analysis grid and through the trading of habitat between habitat quality, extent and connectivity. We provide a solution to this issue and apply it within the frame of Drielsma and Ferrier's (2009) raster-based Rapid Evaluation of Metapopulation Persistence (REMP).
We demonstrate our solution using simple hypothetical examples; and in order to demonstrate the practicality of our approach to real-world settings, we apply the approach to habitat suitability mapping of the White-browed Treecreeper (Climacteris affinis) in south eastern New South Wales, Australia.
This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth ...using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region.
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•A Forward Feature Selection - Random Forest (FFS-RF) model describes urban growth.•FFS-RF found temporal non-stationarity of drivers in Iran's Tehran-Karaj region.•The Total Operating Characteristic evaluated the transition index map (TIM).•FFS-RF produced a TIM that has predictive power no better than a baseline TIM.•FFS-RF and Cellular Automata extrapolated urban growth to the year 2060.
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