Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km²). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

Wetlands are important natural resources due to their numerous ecological services. Consequently, identifying their locations and extents is imperative. The stability, repeatability, cost-effectiveness, multi-scale coverage, and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications, such as provincial wetland mapping. To do so, it is required to (1) process and classify big geo data (i.e. a large amount of satellite datasets) in a time- and computationally-efficient approach and (2) collect a large amount of field samples. In this study, Google Earth Engine (GEE) and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba, Quebec, and Newfoundland and Labrador (NL). Additionally, using GEE, a generalized supervised classification method is proposed to produce a regional wetland map from a large area (e.g., a province) when lacking field samples. In fact, using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics, the wetland maps were generated for the other two provinces. The overall classification accuracies for Manitoba, Quebec, and NL were 84%, 78%, and 82%, respectively, indicating the high potential of the proposed method for aiding provincial wetland inventory systems.

Groundwater extraction rates exceeding recharge are occurring throughout Iran to sustain industrial and agricultural activities, resulting in land deformation in many areas, particularly in the Yazd‐Ardakan Plain (YAP) in central Iran's dry and desert regions. In this study, Interferometric Synthetic Aperture Radar (InSAR) time series analysis and statistical models are applied to characterize the controls on land subsidence in the YAP from 2003 to 2020. Our results reveal the existence of a northwest‐southeast elongated area of 234.45 km² experiencing subsidence at rates up to 15 cm/yr. In the YAP, the international Airport, railway, transit road, and several industrial and historical sites are threatened by the differential subsidence. Well data confirm that groundwater levels have decreased by 18 meters between 1974 and 2018, driving the compaction of sediments within the underlying aquifer system. Our statistical analysis shows that the thickness of a shallow, clay‐rich aquitard layer controls the extent of the observed subsidence and the Independent Component Analysis of the InSAR time series shows that inelastic compaction is dominated. This work reveals that current groundwater extraction practices in central Iran are not sustainable and result in permanent subsidence, ground fractures with impact on infrastructures, and a permanent decrease in water storage capacity.

Rational polynomial coefficients (RPCs) biases and over-fitting phenomenon are two major issues in terrain-independent rational function models. These problems degrade the accuracy of extracted spatial information from very high spatial resolution (VHSR) satellite images. This study particularly focused on overcoming the over-fitting problem through an optimal term selection approach. To this end, multiobjective genetic algorithm was used in order to optimize three effective objective functions: the RMSE of ground control points (GCPs), the number, and the distribution of both RPCs and GCPs. Finally, the technique for order of preference by similarity to ideal solution, as an efficient multicriteria decision-making method, was applied to select the best solution, i.e., the optimum terms of RPCs, through the ranking of solutions in the optimum set. The performance of the proposed method was evaluated by using three VHSR images acquired by GeoEye-1, Worldview-3, and Pleiades satellite sensors. Experimental results show that subpixel accuracy can be nearly achieved in all data sets, when over-fitting problem is addressed. The optimal selected terms leaded to a significant improvement compared to the original RPCs. Indeed, our method, which is independent of GCPs distribution, not only requires a small number of GCPs, but also leads to a 30% to 75% improvement when compared to the original RPCs. This improvement in VHSR images, usually makes no more need to remove the RPCs biases.

Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs.