A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. © 2016 by the authors

Using Landsat-Based Phenology Metrics, Terrain Variables, and Machine Learning for Mapping and Probabilistic Prediction of Forest Community Types in West Virginia

This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 348 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 53.36% (map-level image classification efficacy (MICE) = 0.42). Accuracy increased to a mean OA of 73.0% (MICE = 0.62) when the Oak/Hickory and Oak/Pine classes were combined to an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 61.58% (MICE = 0.52). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic prediction are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

DETECTION OF WATER-BODY BOUNDARIES FROM SENTINEL-2 IMAGERY FOR FLOODPLAIN LAKES

The impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans

Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency