Meisam Amani

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.

Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels

Abstract. Soil moisture content (SMC) plays an important role in different environmental. In this study, four different soil moisture indices, namely, SOMID, SOMID-FS, SOMID-FT, and CSOMID-FT, were introduced. In this work, the following parameters were used to estimate SMC at a depth of 5 cm: (a) the distance of pixels from the origin in the scatter-plot of near-infrared (NIR) and red bands (SNIR-R), (b) the fraction of soil cover in each pixel, and (c) the land surface temperature. It was concluded that the CSOMID-FT was the most accurate index for estimation of SMC (RMSE=0.045, R=0.92). This index divides the SNIR-R into three separate regions based on the pixels’ normalized difference vegetation index (NDVI) values and assigns a specific regression equation to each region. The results showed that as the NDVI values increase, the accuracy of the proposed indices decreases. Furthermore, the SOMID-FT and CSOMID-FT were used to estimate SMC at five different depths of 5, 10, 20, 50, and 100 cm. It was concluded that the satellite-estimated SMC was highly correlated with the field-measured data at 5-cm soil depth.

Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

ABSTRACT Newfoundland and Labrador (NL) is the only province in Atlantic Canada that does not have a wetland inventory system. As a consequence, both classifying and monitoring wetland areas are necessary for wetland conservation and human services in the province. In this study, wetlands in 5 pilot sites, distributed across NL, were classified using multi-source and multi-temporal optical remote sensing images. The procedures involved the application of an object-based method to segment and classify the images. To classify the areas, 5 different machine learning algorithms were examined. The results showed that the Random Forest (RF) algorithm in combination with an object-based approach was the most accurate method to classify wetlands. The average producer and user accuracies of wetland classes considering all pilot sites were 68% and 73%, respectively. The overall classification accuracies, which considered the accuracy of all wetland and non-wetland classes varied from 86% to 96% across all pilot sites confirming the robustness of the methodology despite the biological, ecological, and geographical differences among the study areas. Additionally, we assessed the effects of the tuning parameters on the accuracy of results, as well as the difference between pixel-based and object-based methods for wetland classification in this study.

Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data

ABSTRACT Despite the fact that vast portions of Newfoundland and Labrador (NL) are covered by wetlands, currently there is no provincial inventory of wetlands in the province. In this study, we analyzed multi-temporal synthetic aperture radar (SAR) data for wetland classification at 4 pilot sites across NL. Object-based image analysis (OBIA) using a segmentation method based on optical data (RapidEye image in this study), and well-adjusted to SAR images, was first compared to pixel-based classification. Next, multi-date object-based wetland maps using the Random Forest classifier were compared to single-date classification. Finally, ratio and textural features were evaluated for wetland classification. The OBIA method demonstrated superior results, and the multi-date classification performed better than single-date classification with accuracies ranging from 75% to 95%. The multi-date results showed that the images acquired in August are the most appropriate for classifying wetlands, while the October images are of less value. Also, covariance matrix is a valuable feature set for wetland mapping. Moreover, ratio and textural features slightly increase the overall accuracy when the initial overall accuracy is relatively low. It can be concluded that multi-date SAR classification, with the proposed segmentation method, shows great potential for mapping wetlands and can be applied throughout the province.

Remote sensing for wetland classification: a comprehensive review

Wetlands are valuable natural resources that provide many benefits to the environment. Therefore, mapping wetlands is crucially important. Several review papers on remote sensing (RS) of wetlands have been published thus far. However, there is no recent review paper that contains an inclusive description of the importance of wetlands, the urgent need for wetland classification, along with a thorough explanation of the existing methods for wetland mapping using RS methods. This paper attempts to provide readers with an exhaustive review regarding different aspects of wetland studies. First, the readers are acquainted with the characteristics, importance, and challenges of wetlands. Then, various RS approaches for wetland classification are discussed, along with their advantages and disadvantages. These approaches include wetland classification using aerial, multispectral, synthetic aperture radar (SAR), and several other data sets. Different pixel-based and object-based algorithms for wetland classification are also explored in this study. The most important conclusions drawn from the literature are that the red edge and near-infrared bands are the best optical bands for wetland delineation. In terms of SAR imagery, large incidence angles, short wavelengths, and horizontal transmission and vertical reception polarization are best for detecting of herbaceous wetlands, while small incidence angles, long wavelengths, and horizontal transmission and reception polarization are appropriate for mapping forested wetlands.

Contemporaneous estimation of Leaf Area Index and soil moisture using the red-NIR spectral space

ABSTRACT The potential of the red-NIR spectral space has been frequently investigated for the sole estimation of the Leaf Area Index (LAI) or Soil Moisture (SM) values. However, the applicability of the space for simultaneous prediction of the LAI and SM has not yet been studied. In this study, a general model was first proposed for the contemporaneous estimation of the LAI and SM . Then, the correlations between the field LAI and SM data and ten parameters, extracted from the red-NIR space, were assessed to improve the accuracy of the general model. The results indicated that five of these ten parameters were more suitable for LAI estimation, while the other five provided more information for SM estimation. Using this information, a modified model was finally developed to estimate the LAI and SM contemporaneously, for which the results were more promising than those obtained from the general model. Using the modified model, the correlation coefficient (r) and the Root Mean Square Error (RMSE) for LAI estimation were 0.76 and 0.18 , respectively, and those for SM estimation were 0.85 and 0.02 , respectively. Overall, it was concluded that the proposed method was effective and provided reliable information about vegetation and its background soil concurrently.

Evaluation of multi-temporal landsat 8 data for wetland classification in newfoundland, Canada

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.