Forest ecosystems have been persistently affected by wildfires, leading to significant damage worldwide. The severity and frequency of wildfires have escalated in recent years, necessitating more effective prediction models. This study presents an application of convolutional neural networks (CNNs) for wildfire spread prediction, focusing on the use of atrous spatial pyramid pooling (ASPP) mechanisms in these networks. However, the black-box nature of these algorithms has not been fully explored. To bridge this gap, we proposed an explainable CNN model with an ASPP mechanism (CNN-ASPP) in this study. More specifically, we utilize the Next Day Wildfire Spread dataset, which includes environmental variables, to evaluate the performance of our model. The proposed model is compared with state-of-the-art machine learning (ML) methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and another CNN model. Our results showed that CNN-ASPP achieved an F1-score of 97%, outperforming the ML methods with an F1-score of 90% for a neighborhood size of$7\times 7$. We also opened the black box and tried to explanation different convolutional layers based on the gradient-weighted class activation mapping (Grad-CAM) algorithm. Our findings indicate that larger dilation rates (DRs) can extract more meaningful features from the input data. This study contributes to the development of more transparent and accurate models for wildfire spread prediction, which could have significant implications for forest management and wildfire prevention strategies.

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase, with the current performance evaluation. In this study, we provide results update on RCM performance for selected SAR applications. The RCM was designed to address three core applications-disaster management, maritime surveillance, and ecosystem monitoring. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba. Visual analysis of coincident RCM compact polarimetric and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87.

Wetlands provide different environmental services and contribute to global biodiversity and climate mitigation. However, these priceless environments have been damaged by direct and indirect interference of human activities. Monitoring wetland's hydroperiod over large study areas became feasible only by recent advances in cloud computing platforms and free archive of Landsat time-series. We used the Dynamic Surface Water Extent algorithm for the unsupervised classification of time-series images. This unsupervised algorithm showed 86.7% overall accuracy for 2020 results. Then hydroperiod maps were produced based on the water occurrence of each pixel, and a differencing change detection method was used to produce change maps.

Convolutional Neural Networks (CNNs) have shown promising results in classifying complex remote sensing scenery, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. In this paper, we illustrate the effectiveness of the cutting-edge Swin Transformer for the classification of complex wetlands in New Brunswick, Canada. The precision of the proposed transformer is 0.66, 0.71, 0.75, 0.78, 0.82, 0.83, 0.84, 0.90, 0.90, 0.95, and 0.98 for the recognition of shrub, fen, forested wetland, crop, bog, freshwater marsh, coastal marsh, aquatic bed, grass, urban, and water, respectively. Based on the results, with a relatively high level of overall accuracy of slightly less than 80%, the proposed Swin Transformer is highly capable of complex wetland classification.

Arctic environments are remote and inaccessible, making conventional field-based data collection challenging. Thus, this study describes an efficient desktop-based methodology for deriving reference data to support large-scale remote sensing classification focusing on wetland ecosystems. Our study area was Canada's Southern Arctic Ecozone. Various Earth observation (EO) datasets, including optical, multi-spectral, and topographic, were used as a base to support a photointerpretation process for collecting reference data. Ten 30-by-30-kilometer sampling plots were established across the ecozone for this activity based on a suite of minimum criteria. Reference polygons were assigned to one of the five major wetland classes of the Canadian Wetland Classification System (CWCS), along with a detailed wetland type definition. It is anticipated this methodology will be applied later to other northern ecozones to support large-scale wetland classification updates and status and trends reporting.

Shallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN models utilized in PolSAR image classification. In this paper, we propose using Haar wavelet transform in deep CNNs for effective feature extraction to improve the classification accuracy of PolSAR imagery. Based on the results, the proposed deep CNN model obtained better average accuracy in the San Francisco region with an accuracy of 93.3% and produced more homogeneous classification maps with less noise compared to the two much shallower CNN models of AlexNet (87.8%) and a 2D CNN network (91%). The proposed algorithm is efficient and may be applied over large areas to support regional wetland mapping and monitoring activities using PolSAR imagery. The codes are available at (https://github.com/aj1365/DeepCNN_Polsar).

While deep learning models have been extensively applied to land-use land-cover (LULC) problems, it is still a relatively new and emerging topic for separating and classifying wetland types. On the other hand, ensemble learning has demonstrated promising results in improving and boosting classification accuracy. Accordingly, this study aims to develop a classification system for mapping complex wetland areas by incorporating deep ensemble learning and satellite datasets. To this end, time series of Sentinel-1 dual-polarized Synthetic Aperture Radar (SAR) dataset, alongside Sentinel-2 multispectral imagery (MSI), are used as input data to the model. In order to increase the diversity of the extracted features, the proposed model, herein called WetNet, consists of three different submodels, comprising several recurrent and convolutional layers. Furthermore, multiple ensembling sections are added to different stages of the model to increase the transferability of the model (to other areas) and the reliability of the final results. WetNet is evaluated in a complex wetland area located in Newfoundland, Canada. Experimental results indicate that WetNet outperforms the state-of-the-art deep models (e.g., InceptionResnetV2, InceptionV3, and DenseNet121) in terms of both the classification accuracy and processing time. This makes WetNet an efficient model for large-scale wetland mapping application. The python code of the proposed WetNet model is available at the following link for the sake of reproducibility:https://colab.research.google.com/drive/1pvMOd3_tFYaMYGyHNfxqDxOiwF78lKgN?usp=sharing

Wetlands are significant global contributors to the environment and climate, and there are increasing efforts for wetland conservation globally. Recent advances in cloud computing platforms and accessibility of free medium resolution data lead to affordable solutions for large scale wetland mapping with remote sensing tools. Three Northern provinces of Iran include several complex wetland regions. A classification scheme consists of four wetland classes and five upland classes were chosen to describe wetland types for this region. A combination of Sentinel-2 surface reflectance summer composite and Sentinel-l synthetic aperture radar (SAR) datasets were used to train the machine learning model. Simple non-iterative clustering (SNIC) and Random Forest classification were implemented in Google Earth Engine (GEE) to produce an object-based wetland inventory map with an overall accuracy of 94.10%.

Wetlands are highly sensitive ecosystems that have experienced largely undocumented loss across Canada. Accurate statistics of historic loss of wetlands across many provinces is vague at best or non-existent at worst, as exemplified in Newfoundland and Labrador (NL). Thus, NL represents a perfect candidate for implementing historical remote sensing data sets and change detection methods. Given recent advancements in earth observation technology, it is now feasible to implement remote sensing-based change detection methods at scales never previously possible. As such, the goal of this work is to develop a methodology to assess wetland class change across the island of Newfoundland between 1985 and 2015 using historic and current Landsat imagery, Random Forest classification, and the Google Earth Engine (GEE) platform. The resulting accuracies ranged from 84.37% to 88.96%. The analysis reveals that wetland classes over the last 30 years have been unstable, and the biggest loss of wetlands to anthropogenic land cover occurred between the 1980's and the 1990's. Index Terms - Wetlands, Change Detection, Landsat, Geo big data

In this study, a new hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a study area located in the north eastern portion of the Avalon Peninsula, Newfoundland and Labrador province, Canada. Specifically, multi-polarization and multi-frequency SAR data, including single polarized TerraSAR-X (HH), dual polarized L-band ALOS-2 (HH/HV), and fully polarized C-band RADARSAT-2 images, were applied in three different classification levels. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. Importantly, an overall accuracy of94.82% was obtained for the final classified map in this study.

An adaptive speckle reduction algorithm for Polarimetric SAR (PolSAR) data, based on the combination of Gaussian Markov Random Field (GMRF) and Wavelet Edge Detection (WED) is proposed in this paper. The algorithm has three major steps: (a) first-time speckle reduction based on the GMRF model, (b) detail preservation using a WED approach, and (c) second-time speckle reduction using a least square approach based on pseudo span image. Both the GMRF and WED use the coherency matrix as the input, which has sensitive diagonal elements, namely T11, T22and T33corresponding to surface, double-bounce, and volume scattering, respectively. A key point in the proposed algorithm is that strong point targets are not affected by speckle phenomena and thus, they should be excluded from the de-speckling process. The proposed algorithm is applied to a full polarimetric C-band RADARSAT-2 data in the Avalon Peninsula, Newfoundland and Labrador, Canada.

In this study, we evaluate the capability of Interferometric Synthetic Aperture Radar (InSAR) technique for the monitoring wetland water level changes in the Avalon Peninsula, Newfoundland and Labrador, Canada. This province is one of the richest Canadian provinces in terms of wetland expanse, yet these productive habitats remain poorly understood in this area. The InSAR technique is proven to be efficient in monitoring solid earth changes (e.g., earthquake and landslide). However, the use of such a technique for monitoring water level changes is underdeveloped and limited to particular pilot sites. In this paper, we use 5 SLC TerraSAR-X descending track and analyze them using repeat-pass SAR interferometry technique to monitor water level fluctuations of flooded vegetation.