Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing

Abstract

The mangrove areas in Senegal have fluctuated considerably over the last few decades, and it is therefore important to monitor the evolution of forest cover in order to orient and optimise forestry policies. This study presents a method for mapping plant formations to monitor and study changes in zonation within the mangroves of Senegal. Using Landsat ETM+ and Landsat 8 OLI images merged to a 15-m resolution with a pansharpening method, a processing chain that combines an OBIA approach and linear spectral unmixing was developed to detect changes in mangrove zonation through a diachronic analysis. The accuracy of the discriminations was evaluated with kappa indices, which were 0.8 for the Saloum delta and 0.83 for the Casamance estuary. Over the last 20 years, the mangroves of Senegal have increased in surface area. However, the dynamics of zonation differ between the two main mangrove hydrosystems of Senegal. In Casamance, a colonisation process is underway. In the Saloum, Rhizophora mangle is undergoing a process of densification in mangroves and appears to reproduce well in both regions. Furthermore, this study confirms, on a regional scale, observations in the literature noting the lack of Avicennia germinans reproduction on a local scale. In the long term, these regeneration gaps may prevent the mangrove from colonising the upper tidal zones in the Saloum. Therefore, it would be appropriate to redirect conservation policies towards reforestation efforts in the Saloum rather than in Casamance and to focus these actions on the perpetuation of Avicennia germinans rather than Rhizophora mangle, which has no difficulty in reproducing. From this perspective, it is necessary to gain a more in-depth understanding of the specific factors that promote the success of Avicennia germinans seeding.

Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images

“…For land cover classification, satellite images can be analyzed by grouping pixels or objects with similar characteristics based on statistical or mathematical relationships [1]. The unsupervised machine learning algorithm utilized for mangrove mapping is ISODATA [18], [19].…”

“…This approach analyzes a group of pixels based on comparable spectral features [1]. In mangrove mapping studies, these two approaches are combined with supervised [31], [33] or unsupervised machine learning methods [18], [19]. Based on the literature review, various machine learning algorithms have been employed and demonstrated their performance in mangrove mapping tasks.…”

“…Some commonly used open-source satellite images with moderate resolution include Landsat MSS/TM/ETM+/OLI and Sentinel-2 imagery. These images have been employed in conjunction with supervised machine learning algorithms [13]- [17] unsupervised machine learning algorithms [18], [19], and even deep learning techniques [20], [21] for various tasks related to mangrove ecosystems. Moreover, the utilization of cloud-based computer platforms, such as Google Colaboratory and Google Earth Engine, available for free, has also begun to be used for large-scale mangrove forest monitoring [21].…”

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

“…The related features of different land objects, including spectral, context, and shape features, are extracted for the different land objects [ 18 ]. OBIA has been widely used in remote sensing image analysis [ 19 , 20 , 21 , 22 , 23 , 24 ]. The basic processing unit of the OBIA method is the object, which differs from methods based on the object’s pixels [ 25 ], and thus, OBIA has been determined to have a great uncertainty in terms of image processing.…”

An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening

“…Indeed, pansharpening represents a crucial step in the production of images aimed at visual interpretation in widely exploited software such as Google Earth and Bing Maps. Likewise, many other applications take advantage of this kind of fused data, for instance, agriculture (e.g., for crop type [6] and tree species [7] classification and for precision farming [8]), land cover change detection (e.g., for snow [9], forest [10] and urban [11] monitoring), archaeology [12] and even space mission data analysis [13].…”