*5.2. Land-Cover Changes*

The classifications indicate fairly rapid changes in land cover over the 35 years that are covered by this study and the rapid deforestation or degradation of forest cover, the area of which fell countrywide from 49.89% in 1985 to 23.81% in 2020. These changes have favored crops and fallow lands, savannahs, urban areas, and bare soil. In considering the evolution of forest areas in the different ecological zones, we found that zones II and IV, which cover 32.55% of the national area, contained 55.10% of the national forest cover in

2020. This could be explained by the fact that these two zones are mountainous with very steep relief (Figure 1B), making it very difficult to access forest resources and land in these zones. Zone IV, in particular, has retained most of its original forest area (72.77%), even though it is the smallest of the five ecological zones. Furthermore, ecological zones I and III are areas par excellence in terms of agriculture and housing, as can be seen in our mapped results. Zone V is home to more than one-third of the country's population; the relatively broad extent of forest that was found in this zone would have more to do with poor image quality than with the actual area.

In Table 7, we note that r is negative and R is positive when there is a contraction of forested areas, while the opposite occurs when there is an expansion of forested areas. From these two indicators of forest cover change, we further note that the study area experienced a substantial loss of forest area between 1985 and 2000 and, again, between 2005 and 2015. In contrast, only small increases in the area occurred until 2005 and, again, between 2015 and 2020. Current forest area declines are most likely related to agricultural expansion and rapid human population growth in Togo (2.84% y<sup>−</sup>1), which exert strong pressures on natural resources and land. The national REDD+ Togo study of 2018 on the causes and consequences of deforestation and forest degradation across the nation has confirmed that agricultural development, including associated managemen<sup>t</sup> practices (notably, the use of fire), is the main cause of forest disturbance, ahead of timber exploitation (timber and energy) and urban expansion. Furthermore, the dynamics of urbanization, which underlies the country's population growth, are driving rapid changes in LULC and are contributing to forest loss, both directly and indirectly [32,33].

Nevertheless, the increase in forest area in 2005 could be attributed simply to the aforementioned poor quality of Landsat 7 data, which would influence the classifiers during processing. The 2020 increase could be due to an overestimation by classifiers of the open forest class at the expense of savannah, but this could also be due to the results of conservation policies and programs that have been recently implemented by the governmen<sup>t</sup> (forest inventory and REDD + strategy). In order to achieve the state's objective of increasing forest cover to 30% of the territory by 2030, these factors of forest degradation would have to be reconsidered in terms of governmental actions at the social, environmental, and political levels. In addition, the rate of land-cover conservation and the speed of change that has been quantified at the level of administrative regions indicate that the Plateaux and Centrale regions are better conserved, while the Maritime region records the highest frequency of change. The Savanes region is intermediate between these two extremes; most land cover has only changed once or twice. Yet, it should be noted that most of the plant formations of the Savanes region were very early transformed into crops and remained in this class. This explains why this region has a relatively low rate of land cover change for a given location despite its higher rate of degraded area. The Maritime region has experienced the most land-cover changes over the period, i.e., three to four times. These conditions would thus need to be monitored when making land-cover planning or development decisions. Given that forest managemen<sup>t</sup> across the study area is based more on administrative subdivisions, our results should enable centralized administrative and forestry authorities to prioritize actions for a much more balanced environmental governance.

### *5.3. Advantages and Limitations of the Method Used*

For the selection, pre-processing, and classifications of satellite images during this study, we used the RF algorithm, which can take into account even disparate data to make a fairly accurate classification of heterogeneous land cover such as in forest-savannah mosaics [60]. This algorithm has been used on the GEE platform containing a vast catalog of Earth observational data. It is based upon millions of servers around the world that allow for the rapid processing and analysis of satellite data over large areas, without the need to download them [81]. The GEE has a user-friendly programming environment with

high computational efficiency, which allows less time to be spent on usual satellite data processing steps that are frequently quite time-consuming when using dedicated software.

A further advantage of this method is the possibility of making enormous savings in both time and money when conducting regional or national forest inventories. For example, when considering the results that were obtained for several land-cover classes through methods requiring very few means that were applied in this study, we note that they are more or less comparable to those that were obtained from the national forest inventory (NFI), which had mobilized many more human and financial resources. For the 2015 results (the year closest to the NFI), we obtained 22.48% for the forest class, 38.90% for the savannah class, and 38.27% for the grouping of agriculture and infrastructure classes versus 24.24%, 34.86%, and 40.90, respectively, for the 2016 NFI [71]. With this method of processing satellite data in the GEE, once the processing code is completed, it can be easily optimized and applied for the long-term monitoring of LULC changes when incorporating newly acquired images [62].

However, it must be noted that this processing power is not available on demand for all types of operations, given that a quota is allocated to each user and, thus, the GEE system sometimes limits or aborts certain code executions that are computationally demanding [26]. Furthermore, despite having millions of images, some areas have long periods when cloud-free data are absent, especially in tropical environments. This is a particularly lamentable state of affairs, given that research in this region has calculated the probability of acquiring Landsat MSS or Landsat TM images with <70% cloud cover in a year to be only 26% [79]. In these cases, the GEE permits the selection of pixels from multiple images exhibiting large temporal differences in acquisition dates to form the composite, as was the case in our study. Unfortunately, such selections do not allow for estimates of seasonal differences or phenologies, thereby introducing potential classification errors. A further limitation is that during satellite data processing, code execution errors that are encountered can be difficult to debug, given that scripts in the GEE run in the Google Cloud. As confirmed by [62], errors also can occur in the JavaScript code, either on the client side, which is manageable with some effort, or during server-side execution, a situation that can be very difficult to manage.
