**1. Introduction**

Forests contribute greatly to soil conservation and climate change mitigation and represent one of the simplest and most effective means of establishing or maintaining carbon sinks [1]. As one of the most important global carbon reservoirs, tropical forests are home to between half and two-thirds of the Earth's species [2]. Unfortunately, these forest carbon stocks are not stable, given that conversion to other land cover is occurring at an alarming rate despite the increased awareness of climate change [3,4]. Between 2000 and 2005, land-use and land-cover (LULC) changes resulted in forest cover reductions of 0.6% per annum worldwide [5]. Between 2015 and 2020, annual deforestation rates were estimated at 10 million hectares globally [6]. Such land-cover changes occur mainly as a result of anthropogenic disturbances, including deforestation, together with the expansion of croplands and urban areas [7]. LULC changes, mostly caused by agriculture and deforestation, contribute to about one-third of global greenhouse gas (GHG) and worsen the

**Citation:** Kombate, A.; Folega, F.; Atakpama, W.; Dourma, M.; Wala, K.; Goïta, K. Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine. *Land* **2022**, *11*, 1889. https://doi.org/ 10.3390/land11111889

Academic Editors: Carmine Serio, Guido Masiello and Sara Venafra

Received: 2 September 2022 Accepted: 21 October 2022 Published: 25 October 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

adverse effects of climate change [8,9]. Faced with these increasingly significant effects of climate change, ongoing demands for action are becoming more urgen<sup>t</sup> to curb the extent of deforestation and forest degradation, while enhancing carbon storage through better accounting of carbon sources and sinks. To this end, the United Nations Framework Convention on Climate Change (UNFCCC) has established the REDD+ (Reducing Emissions from Deforestation and forest Degradation Plus) mechanism, which is seen as a global system of centralized forest governance. Aimed primarily at developing nations, REDD+ provides financial compensation for these countries to preserve their forests to reduce carbon emissions and, thus, mitigate the risks of climate change [10,11].

In order to qualify for financial offsets by implementing REDD+, these countries are required to establish National Measurement, Reporting, and Verification (MRV) systems within a national forest monitoring system (NFMS) that must provide national estimates of changes in forest carbon stocks and emissions every two years. The Intergovernmental Panel on Climate Change (IPCC) recommends a combination of Earth observation data and field inventories to estimate forest area, carbon stocks, and changes that follow disturbance [12]. Regular analysis of forest dynamics and LULC changes using satellite data could effectively establish the baseline for the MRV reporting requirement in this context. However, many concerned developing countries are generally faced with a lack of quantitative data on forest degradation-induced changes and limited technical capabilities and material capacity to produce such data for GHG emissions monitoring [12].

The aforementioned challenges beset the West African nation of Togo (République Togolaise), which is the subject of our study, in its quest to meet reporting requirement needs within the framework of the REDD+ strategy, and to guide strategies for monitoring the evolution of forest ecosystems and land cover. A few studies based on observational data have made it possible to monitor changes in land cover in certain parts of the country, but they generally have a starting and an ending year for a period that occasionally spans several decades. The coarse temporal frequency of sampling does not make it possible to detect changes that have been incurred within these periods or to discern which main factors drive their behavior. Furthermore, the spatial extent of these studies is often very limited (i.e., river basins, protected zones, and administrative jurisdictions, among others), whereby changes are not perceived across an entire ecological region or on a national scale. Land and vegetation cover have been studied, but these changes are mainly in protected areas [13–16]. Other studies have focused on watersheds [17,18], while some have been carried out at regional or prefectural scales [19,20]. To a much lesser extent, few comprehensive studies have spanned several ecological zones [21]. These studies have generally covered about 1 to 10% of the national territory, and there are regrettably very few studies quantifying the LULC changes observed over time or analyzing the drivers of these changes.

The spatial and temporal limitations of these previous studies in detecting land-cover changes are related to the difficulties in finding sufficient cloud-free satellite images over large areas. This problem could be overcome by using Synthetic Aperture Radar (SAR) images which, even when acquired in all atmospheric and solar conditions, allow change detection analyses [22], but SAR long historical data does not exist in our study area. These limitations are also related to computational resource problems (large storage capacity and access to high computing power), together with the labor-intensive nature of processing these mega-data [23,24]. Furthermore, global-scale mapping projects often use satellite data with a variable spatial resolution (1 km to 30 m), and generally do not involve local experts; therefore, these approaches do not meet the standards of accuracy that are sought at the national level [25]. With the availability of the new geospatial technology of the Google Earth Engine (GEE), it is now possible to apply very advanced machine-learning algorithms in an efficient manner [26]. The GEE is a cloud-computing platform with a JavaScript code editor that integrates a long-time series of satellite imagery, thereby allowing the classification of large volumes of data and the production of multi-date land-cover changes. It should be further noted that relatively few studies in the scientific literature

have focused on the use of these methods to advance operational forest monitoring in MRV systems [27].

The major challenges to implementing Togo's national REDD+ strategy are reversing the process of forest degradation and savannization, while spatially containing agricultural pressure and constraining urban expansion. These measures should eventually increase carbon stocks and reduce greenhouse gas emissions [28]. Unfortunately, most studies that have been conducted in Togo on progressive LULC changes are incomplete, and forest inventories over the last three decades are very limited. The availability of historical LULC data at a national scale is necessary to meet the challenge of better understanding the LULC dynamics and forest developmental trends over time. This study aims to answer the question of whether the use of multi-temporal images in the GEE would provide a picture of land-cover changes, particularly forest cover, at the national scale. Its main objective is to characterize vegetation dynamics over the entire national territory using a long-time series of Landsat images from 1985 to 2020. More specifically, the study aims to quantify the evolution of spatiotemporal changes and to analyze their effects on forest cover during this period.

### **2. Study Area and Data Used**
