In this paper, we address the important task of Individual Tree Detection (ITD) in forest environments for enabling tree parameter estimation including tree count, height, volume, and crown dimensions. Recent advances in high-resolution multispectral and LiDAR data collected by Unmanned Aerial Vehicles (UAVs) show a promising solution for ITD. We introduce a novel ITD method using the YOLO V7-tiny deep learning framework on UAV LiDAR data. First, we rasterize point clouds into Vertical Density (VD) and Canopy Height Models (CHM), and then we utilize the modified YOLO V7tiny algorithm to detect the boundary of the trees. The accuracy, precision, recall, and F1-score results of YOLO V7 compared to YOLO3 showed a significant improvement. The proposed method demonstrates promising results for urban and forest tree inventory updates and contributes to largescale satellite-based forest structure and biomass estimation.

Machine learning (ML) methods such as Random Forest (RF) have shown promises to estimate Secchi Disk Depth (Zsd). However, lack of a comprehensive dataset has been a long-lasting issue for training ML models in remote sensing of water quality. To aid the training process, the GLORIA dataset has recently provided access to hyperspectral in-situ measurements of remote sensing reflectance (Rrs) along with associated water quality parameters for globally representative inland and coastal waters. We use simulated Sentinel-2 Rrs to train a global model using GLORIA and then validate it on independent data from Finger Lakes, USA. When compared to RF model trained on Finger Lakes data, the validation results indicate better performance (Mean Absolute Error (MAE) 37%) as compared to the global model trained on GLORIA (MAE 94%). However, when the global model was validated on independent dataset from GLORIA (i.e. Lake Erie), the results were promising (MAE 34%). Therefore, the models can be used to estimate Zsd globally, provided the uncertainties in deriving satellite based Rrs are accounted for.

Wetlands serve critical functions for protecting ecosystems but are declining globally due to anthropogenic activities and environmental change. There are challenges in delineating wetlands that partially cannot be addressed using the integration of optical and SAR data. NASA’s global ecosystem dynamics investigation (GEDI) data, forest height products can differentiate wetland types with different heights. However, GEDI data is only available for limited footprints. In this study, we used a random forest regression model to create a canopy height model (CHM) by merging discrete GEDI footprints with Sentinel-2 data. We calculated the R-squared and RMSE using test data (0.81 and 3.48 meters, respectively). Such an investigation might provide the basis for better wetland management.

The growing effects of climate change and urbanization necessitates continuous monitoring of the freshwater resources in terms of water quality. Although remote sensing techniques have been successful in estimating water quality, its applicability over small oligotrophic lakes still remains a challenge due to the lower contribution of constituents to the water-leaving radiance. As such, this study leverages the availability of citizen science data and the synergistic use of Sentinel-2 imagery with Random Forest (RF) regression to estimate Secchi Disk Depth (SDD) over Canandaigua Lake. The results indicate an R2of 0.74, RMSE of about 0.72 m, MAE and Bias of 1.11 and 0.98, respectively. The feature importance for RF was also calculated, and the results indicate high value for visible bands. The model can be replicated for similar study areas and the findings can be used for efficient freshwater monitoring.

Investigating the quantity of forest above-ground biomass (AGB) is vital for understanding the role of forests in the global carbon cycle. Canopy height model (CHM) plays an important role in estimating AGB. Accurate large-scale forest CHM estimation using traditional methods requires a lot of labor, time, and cost. Remote sensing is a cost-effective approach, which provides valuable information over large areas in a timely manner. Recent advances in spaceborne light detection and ranging (LiDAR) data paved the road for measuring elevation. The global ecosystem dynamics investigation (GEDI) onboard the International Space Station is particularly designed to collect information on vertical vegetation structure. Thus, the main objective of this paper is to use the GEDI level 2A elevation and height metrics product to create a high resolution, state-wide CHM. To create a continuous CHM, GEDI point-based height measurements were extrapolated using Sentinel-2 imagery to produce a 10 m CHM of the New York State for the year 2019. The generated 10 m CHM was evaluated using GEDI height measurements (RMSE=4.85 m, R2=0.65). A comparison of 10 m CHM and a 30 m global CHM (RMSE=6.6 m, R2=0.62) demonstrated the potential of finer spatial resolution and red-edge bands in creating a more accurate CHM which is important to support forest monitoring at large-scale.

Monitoring freshwater quality is a global concern because of increasing harmful algal blooms (HABs). Therefore, it is important to detect HABs especially in small lakes as they hold great socioeconomic value. This study estimates the potential of using Sentinel-2 for estimating chlorophyll-a value in small inland lakes. In particular, this study uses support vector regression (SVR), random forest (RF) and adaptive boosting (AB) for Seneca lake. The processing power of Google Earth Engine (GEE) was used to extract the input features. The results indicate the superior performance of machine learning models in comparison to linear regression. Furthermore, AB provided the best results (R2=0.85) as compared to RF and SVR. In terms of ensemble, the combination of all three models performed best in terms of R2 (0.76), RMSE (0.633 µg/L) and MAE (0.728 µg/L).

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

Identifying wetland's spatial distribution is essential for their restoration and management due their significant role in the global water and carbon cycles. This study aims to assess the ability of upcoming NISAR data for delineating similar wetland classes using machine learning techniques. In particular, we investigated the synergistic use of several polarimetric features for efficient classification of wetland types. To this end, 84 polarimetric features from 11 polarimetric decompositions were extracted from full-polarimetry simulated NISAR data. The mean-shift algorithm was employed to segment the imagery for importing to the object-based machine learning classifiers. Post-classification feature importance analysis using the Gini index suggests that H/A/ALPHA, Freeman-Durden, and Aghababaee decomposition parameters have the highest contribution to the overall accuracy. Further, overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrated a great capability of NISAR data for wetland mapping and monitoring using limited available training data. Overall, the proposed approach for manipulating the upcoming NISAR data will provide some insight on the efficiency of an upcoming trend in using multi-frequency and full-polarimetry NISAR data with Sweep-SAR architect for producing high-resolution land cover maps on a global scale.

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

Shrub willow is a valuable source of hardwood biomass feedstock which is used for the production of bioenergy, biofuels, and renewable bio-based products. The biomass produced from this short-rotation woody plant can be used for heat and electricity generation. Thus, an accurate estimation of shrub willow above-ground biomass (AGB) is of paramount importance. This paper aimed to estimate shrub willow AGB using multi-spectral unmanned aerial system (UAS) imagery and machine learning techniques. To accomplish this goal, a machine learning model (i.e., random forest (RF)) and a deep learning method (i.e., convolutional neural network (CNN)) were applied to the spectral bands and some vegetation indices over a site in Camillus, NY, US in July 2019. The results demonstrated the superiority of the RF model (RMSE of 1.73 Mg/ha and R2 of 0.95) compared to the CNN (RMSE of 2.69 Mg/ha and R2 of 0.89) technique. Adding vegetation indices to spectral bands and using a convolutional approach for training purposes could significantly improve the modeling efficiency.

The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland. In particular, a CNN model was used for classification of remote sensing imagery with limited number of training data by fine-tuning of a preexisting CNN (AlexNet). The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely Random Forest (RF), to evaluate the efficiency of CNN Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., 3 features) for CNN compared to RF. The proposed classification scheme serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing 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.

Wetlands are important natural resources which provide many benefits to the environment. Consequently, mapping and monitoring wetlands has gained a considerable attention in recent years among remote sensing experts. Wetlands undergo a considerable change within a year. Thus, it is important to study how much various wetland types are distinguishable at different dates. This will help in choosing an appropriate image for wetland classification. On the other hands, combining various satellite images acquired on different dates is a promising approach to obtain a more accurate classified map compared to the map obtained by single-date satellite imagery. In this study, wetlands within a pilot sites, located in Newfoundland were first classified using each of the several available Landsat 8 data, captured in the three seasons of Spring, Summer, and Fall. By doing this, the separability of the wetland classes in each season was analyzed. Then, these multi-temporal data were integrated to obtain a more accurate map of wetlands. The overall classification accuracy of the final map was 88%, proving that using multi-temporal remote sensing data was necessary to obtain a more reliable and accurate map of the dynamic wetlands in the province.

Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based classification are not done in one step, but instead several feature selections and classifications are applied, and in each level a target class is classified and masked out. While classifying the target class, other spectrally resembling classes are merged so that feature selection is mainly concentrated on separating two classes only. Object-based features were extracted from several SAR and optical images, including RADARSAT-2, ALOS-1, ALOS-2, RapidEye and Landsat-8 images. 15 and 10 percent improvement was obtained in wetlands' average producer and user accuracies compared to the typical feature selection by using the proposed method.

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.

Mapping linear disturbances from oil and gas extraction and mining in Western Canada traditionally relies on manual digitizing from very high resolution remote sensing data, which usually limits results to small operational area. In this paper, we present an approach of mapping linear disturbances based on time series data analysis of regularly acquired, and low cost satellite data of moderate resolution. This approach involves three steps: line detection based on a multi-scale directional template, line updating based on reappearance frequency and line connection using Hough Transform. This automatic method has been tested over three sites in Alberta, Canada by detecting linear disturbances occurring over the period of 1984-2013 using Landsat imagery. The results show that the method can extract very narrow linear features, including seismic lines, roads and pipelines with a good performance.

This research presents a novel strategy for hyperspectral and LiDAR data fusion to generate accurate LC/LU maps with object based classification method. Unlike conventional object-based strategies (hierarchical and multilevel models), in the proposed method, classification has been performed in an iterative Segmentation-Classification-Merging (SCM) process. In each iteration, image objects are extracted by using their spectral, height, geometric and class-related characteristics based on data availability, class importance and higher extraction capability. Results indicate great overall accuracy of 97.33% and a kappa coefficient of 0.9710.

Development associated with oil and gas exploration has expanded rapidly in Alberta and Northwest Territories, Canada. Such explorations result in landscape disturbances including forest cuts, seismic lines, well and waste sites. This paper describes a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines pixel-based and object-based image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. For accuracy assessment, a small part of the image was used and the results were compared against visual counting of well sites visible in the pan-sharpened image of Landsat-8 of the same area. Results show correctness, completeness and quality factors of 87.3%, 96.2%, and 83.7%, respectively.