Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula.

Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finistère with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH/VV, and finally, heading in VV and VH polarizations.

Land use and land cover (LULC) information is a fundamental component of environmental research relating to urban planning, agricultural sustainability, and natural hazards assessment. In particular, remote sensing technology has demonstrated a powerful capacity for LULC modeling with a corresponding increase in sensor number and type. Here, an advanced convolutional neural network (CNN) deep learning model was developed in combination with object-based image analysis (OBIA) to map LULC in Ain Témouchent coastal area, western Algeria, using sentinel-2 and Pléiades imagery data. First, the CNN model was constructed based on convolution, hidden, and max pooling layers. The parameters of CNN architecture were optimized to improve the model for further processing. Then, based on high levels of CNN feature extraction, the OBIA was applied to classify the segmented objects, and detect the LULC features. Furthermore, machine learning methods, including random forest and support vector machines were tested for comparison. The proposed method achieved a high overall accuracy (93.5%) using Pléiades imagery, revealing significant improvements compared to other machine learning techniques. Accordingly, it was concluded that the method proposed here is useful for LULC detection, and can be applied at larger scales in coastal areas. The derived maps can also inform regional and national-level decision making.

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

<!-- *** Custom HTML *** --> Coastal monitoring is an essential feature for the sustainable management of naturally vulnerable areas; however, data acquisition is a tedious task. We aim to identify an efficient method of automatic shoreline monitoring based on high water level detection using very high-resolution Pleiades images and taking as the pilot zone the Mostaganem coastline (Algeria). Through a comparative study between classification methods based on pixel- and object-based image analyses (PBIA and OBIA, respectively), algorithmic development and optimizing was conducted on two machine learning (ML) classifiers: random forest (RF), and support vector machine (SVM), and two segmentation algorithms: multiresolution (MRS) and meanshift (MSS). These classification methods yielded six different shorelines that were validated using an in-situ GPS survey shoreline acquired on the same day as the Pleiades image. The results showed that the OBIA generated a shoreline with a 5% to 25% better accuracy than that of PBIA using the same ML algorithm. Within the OBIA approach, MRS generated a shoreline with 20% higher accuracy compared to MSS, suggesting the importance of segmentation possessing. The RF based on MRS was the method that produced the shoreline at the best accuracy, where 55.5% of the extracted shoreline was within 1 pixel of the in situ shoreline. This method was successfully shown to be a good alternative for shoreline monitoring of sandy microtidal coasts, offering to coastal managers a reliable tool to complete the data and efficiently manage the coastal erosion.