Climate change represents a major challenge for the scientific community, civil society organizations, natural ecosystems and human societies, as well as for the health of the population
[1]
[2]
. In order to combat these changes, different strategies have been put in place, including the definition and implementation of mitigation policies, the development of adaptation actions at the local level and the establishment of strong economic measures to finance the ecological transition
[3]
[4]
.
Deforestation and tropical forest degradation contribute about 17% - 25% of human-caused carbon emissions and result in the destruction of globally significant carbon sinks
[5]
-
[7]
. However, the impacts of forest degradation are understudied and poorly understood, mainly due to the focus of international emission reduction programs on deforestation, which is easier to detect and monitor.
A consistent estimate of emissions from forest degradation between 2005 and 2010 in 74 developing countries, covering 2.2 billion hectares of forest, puts annual emissions at 2.1 billion tons of carbon dioxide
[8]
.
Several studies have addressed the direct and indirect causes of the loss of tropical forests in recent decades worldwide, as well as in specific countries
[9]
[10]
. These studies, usually conducted in tropical countries, have identified the causes of degradation and deforestation, as well as determining the contribution of degradation to global greenhouse gas emissions
[8]
. The conversion of forests for agricultural purposes is the most widespread human activity on planet Earth
[11]
. In addition to this, there is logging, firewood harvesting, forest fires and urban expansion in forest areas
[9]
.
However, many questions remain about the future evolution of the causes of deforestation and forest degradation in the world, as well as in specific regions. These questions are linked to global and regional demographic growth, as well as the evolution of global economic parameters. The drivers of these forest losses have both international and national origins.
Modeling deforestation and degradation helps to understand the spatio-temporal dynamics of this phenomenon and promotes a better understanding of the factors that contribute to deforestation
[12]
. According to this author, the model’s primary function is to schematize reality, to propose a theoretical scheme that provides information on the processes and relationships that exist between the elements of a system. In the current context of sustainable development and climate change adaptation policies, it is essential to identify and assess the parameters at the origin of deforestation and forest degradation in order to understand and control the future changes, particularly for regional issues (heritage and economic value) or planetary issues (carbon stock and emissions, biodiversity)
[13]
.
Modeling the factors explaining deforestation and forest degradation is of great importance, as it combines verbal formalization and graphic formalization
[14]
. It makes it possible to measure the effects on deforestation, to better understand the complex mechanisms underlying deforestation and forest degradation
[15]
-
[17]
and to develop scenarios for the evolution of social ecosystems
[18]
. The literature offers several models in different fields, such as the environment or land use.
The main objective of these models is to contribute to a better understanding of the factors (environmental or cultural) explaining deforestation and forest degradation
[19]
[20]
, to propose a theoretical scheme allowing to understand the socio-economic and ecological processes as well as the existing relationships between different parameters, whether environmental or cultural
[12]
[19]
, and to predict the rate of expansion of deforestation
[19]
.
To characterize the process of evolution of surface conditions, these models answer three questions: what environmental and cultural variables best explain deforestation as well as the ecological and socioeconomic processes that support them? How fast is this deforestation progressing? Which areas are most affected by deforestation?
[19]
Several modeling studies have been carried out in Congo, Africa and the rest of the world
[17]
[21]
-
[25]
.
These studies have shown the close link between population growth, urban sprawl and pressure on natural resources, especially forest resources. For example, the city of Brazzaville, which was mainly forest-based in 1946
[26]
, has suffered the effects of urbanization and population growth on the sustainability of forests
[27]
[28]
. Other studies have helped to understand the causes of forest loss not only globally, but also in the Congo Basin
[9]
[20]
[29]
[30]
. Urbanization, which is a direct cause of deforestation, is incompatible with maintaining the sustainability of forest ecosystems
[31]
.
According to
[32]
, cited by
[33]
, the high rate of population growth is associated with a high rate of deforestation in developing countries. Population increase poses a direct threat to the local environment and reduces the natural assimilative capacity of the environment
[33]
.
This study presents the originality of simultaneously addressing the future modeling of deforestation and forest degradation based on cities with different development histories. Indeed, among the towns studied, some emerged from logging camps, while others began as simple logging fishing camps with no logging activity.
The objective of this study is to identify the past causes of deforestation and forest land degradation through a diachronic cartographic study, to propose a complex spatial and economic model that accounts for the complex interactions explaining deforestation and forest degradation in the localities studied, and to define the rate of forest loss by predicting the future.
2. Materials and Methods2.1. Location of the Study Area
This study was carried out in five localities of the Republic of Congo, which confirmed the initial hypothesis according to which these localities were initially covered with forest. The localities concerned are Impfondo and Dongou, located in the department of Likouala, as well as Ouesso, Pokola and Ngombé, located in the department of Sangha (see
Figure 1
).
Figure 1. Map of the study area.
2.2. Methodology
The causes of deforestation and forest degradation in the urban and local areas of this study were examined in three stages: 1) analysis of past land cover maps, 2) collection of data on the field through surveys, and 3) building a model to project future deforestation.
For this study, Landsat Operational Land Imager (OLI) images covering the different study areas and different years were downloaded from the USGS website (Earth Explorer,
https://earthexplorer.usgs.gov/
). These images had a spatial resolution of 30 meters. Prior to classification, a series of pre-processing operations were performed on the selected images of the study area. Then, a post-classification was performed on these images to produce the final maps. All these steps were carried out using the free software QGIS version 2.18.
It was possible to highlight the different types of land use and produce the corresponding map. The first land cover map was produced based solely on training areas determined visually from Landsat pixels. Five land cover classes were selected: urban area, dense forest, degraded area (agriculture, fallow land), savannah and water. The second land cover map for the same area was created by combining both data from “Google Earth” and ecological data collected during field visits. This made it possible to map the agriculture class and the secondary forest class.
A questionnaire was developed with the aim of collecting relevant information on the history of land use in the different localities for the years 1986, 2003 and 2019. This survey was conducted randomly with five hundred people from different localities in the study area, as well as with forest services and local administrations.
The questionnaire also aimed to identify the direct or indirect causes of deforestation and forest degradation, as well as to collect socio-economic data. These data should make it possible to understand all the factors of anthropogenic pressure on local forests, in order to propose a spatial and economic model that can explain the complex future interactions explaining deforestation and forest degradation in connection with population growth.
The demographic data of the various localities studied were collected from the National Institute of Statistics and from the archives of the reports of the administrations responsible for human demography in the Republic of Congo. Missing demographic data between two periods were obtained by simulation from existing data using Equations (1) and (2) (growth rate r and basic population data).
Other data, such as income-generating activities (IGA), monthly income of populations, statistics on civil war refugees from neighboring countries, and the impact of the policy of building modern infrastructure in localities forests, were collected in the field from the local authorities of the different localities studied.
To estimate the population size for the years when we were unable to obtain population size data, we used the following equations:
(1)
(2)
where:
The growth rate applied to estimate the size of the population between two years varied from one locality to another according to certain criteria defined by the National Institute of Statistics. Population growth results from birth rate, death rate and migration rate. For example, for the city of Impfondo, the growth rate was 3.1 from 1984 to 2007, and 3.68 between 2008 and 2019. The value of r = 5.1 was applied for the city of Dongou in Likouala. For the localities of Ouesso and Pokola, an increase rate of 2.8 was used. The population explosion in the town of Impfondo is associated with the arrival of refugees from the Democratic Republic of Congo, the Central African Republic and Rwanda.
Based on the data collected in the field and the analysis of the cartographic products, interactions between the direct and indirect causes as well as between the direct causes have been identified in order to develop an explanatory framework of the anthropogenic pressure on the forests of the different study areas. In order to establish future projections, an annual growth rate has been applied to follow the evolution of the various parameters until 2030 and 2050.
The future population numbers in the different cities were obtained through modeling using the Excel 2019 forecasting tool. For this modeling or projection, two parameters were taken into account: the reference year of the study, the final year of the study and the year for which we wish to estimate the population, as well as the known population counts of the reference year and the last year available.
These forecasts were obtained using two main parameters: the year for which we wish to estimate the area, the reference year and the final year of the study, as well as the existing area at these different periods. For this, we entered all these data in the Excel spreadsheet using the forecast formula available in this tool.
3. Results3.1. Study of the Causes of Deforestation and Forest Degradation
The analysis of land cover made on the basis of land cover maps developed revealed a conversion of forest land to other types of land cover such as urban area class, degraded area class (agricultural zone and fallow land) in the five localities of the study area (Impfondo, Dongou, Ouesso, Pokola and Ngombé). These land cover maps made it possible to obtain the results of the areas indicated in
Figure 2
. This figure reveals an increase in the area of the different types of land cover regardless of the type of land conversion. Agricultural area (SA), urban area (SA) and fallow area have been steadily increasing from 1986 to 2019. The cumulative sum of these three types of land use: from the forest class to the urban fabric, agricultural zone, fallow classes from 1986 to 2019 for the five localities increased from 2690 ha to 3484.51 ha in 2003 to 6576.48 ha in 2019.
However, one certainty emanates from the modeling results: the population of the various localities will increase significantly until 2050, assuming that the current population growth rate is maintained. The urban growth observed in 1986 is still present in 2019, in greater proportions, which attests to the importance of this factor in tropical deforestation. This increase is also correlated with the increase in areas of fallow land and agricultural land in almost all the localities studied.
Figures 2(A)-(E)
highlights the land cover classes directly associated with human activities, and therefore with the gradual replacement of forests by non-forest areas, which will then become urbanized areas with the growth of the human population in the different localities.
(A)--(B)--(C)--(D)--(E)--Figure 2. Evolution of the areas of the different types of land use between 1986 and 2019 in the different localities.
(A)--(B)--(C)--(D)--(E)--Figure 2. Evolution of the areas of the different types of land use between 1986 and 2019 in the different localities.
(A)--(B)--(C)--(D)--(E)--Figure 2. Evolution of the areas of the different types of land use between 1986 and 2019 in the different localities.
(A)--(B)--(C)--(D)--(E)--Figure 2. Evolution of the areas of the different types of land use between 1986 and 2019 in the different localities.
(A)--(B)--(C)--(D)--(E)--Figure 2. Evolution of the areas of the different types of land use between 1986 and 2019 in the different localities.
Figures 2(A)-(E)
illustrates an increase in the areas of the different land cover classes at the expense of the forest class in all localities of the study area, between 1986 and 2019.
The growth of the urban fabric, which corresponds to the zone where constructions develop (
Figure 3
), and agricultural activities are in fact the main causes of deforestation in these study localities.
Figure 3. Urban growth in the different cities studied.
Demographic data collected at the National Institute of Statistics in Brazzaville and in the various local administrations, as well as estimates based on the growth rate, made it possible to construct the population growth curve (
Figure 4
).
Figure 4. Populations in the different cities studied.
Data on population counts collected in the field in the different localities for the different dates were used to estimate the population counts for the intermediate years where data were missing between 1986 and 2019.
Different growth rates were applied to obtain the curves (
Figure 4
), thus making it possible to make future projections on the evolution of the urban area by modeling. This curve shows an increase in population in all localities during the study period. The city of Impfondo experienced the strongest population acceleration between 2007 and 2019. On the other hand, the city of Dongou, although having a regular increase in its population, remains the locality with the lowest number among the five studied.
This difference can be explained by the high concentration of refugees from neighboring countries. These refugee populations, deprived of financial resources, had as their only source of subsistence the dense forests located around the towns of Impfondo and Dongou.
The counting and analysis of collection sheets made it possible to obtain results on the main income-generating activities in the study localities, the causes of the rural exodus towards the different study localities, the causes of urban growth, as well as the causes of deforestation and forest degradation. Analysis of
Figure 4
reveals that among the respondents, 38% mentioned traditional agriculture as the most common activity, followed by fishing at 27%, trading at 15%, animal husbandry at 10% and hunting at 6%. Activities such as crafts and masonry are the least mentioned, representing only 2% of the sample of the population questioned. Among these activities mentioned, only agriculture has an impact on forests.
A study of the causes of forest loss, city by city, indicates that the causes differ from one locality to another. For example, in the localities of Ngombe and Pokola, forest degradation is mainly due to logging and the construction of living bases for forest industry workers and their families. In the town of Dongou, which was originally a fishing camp, development took place slowly in the 1970s and 1980s. Agriculture around the town is the main income-generating activity, associated with the fishing in the Oubangui River. The survey of local populations on the causes of urban growth revealed that population growth was the most frequently cited cause (76% of the sample), followed by land use planning (15%). Economic activities and proximity to the river are the least cited causes with 3% (
Figure 5
).
The results of the survey revealed that several reasons were mentioned by the subjects questioned to explain the displacement of local populations from villages to large urban centers, which are our different study localities (
Figure 5
): the reasons given include more favorable living conditions in these urban centers compared to rural areas, proximity to major rivers (Sangha and Oubangui) and access to education for children. Each of these reasons was cited by 16% of respondents, out of a total of 100%. Reasons related to health and the need to live in these urban centers were mentioned by 13% of the subjects, while the status of a shopping center and professional reasons were mentioned by 6% of the individuals. Religious reasons and ease of disposal of products account for 3% of responses, and 9% of individuals did not answer this question.
Regarding the direct causes of deforestation and forest degradation, if we consider the number of respondents who answered this question, shifting cultivation is identified as the main cause, cited by 41% of the total sample. Then, the collection of wood fuel is mentioned by 22%, the collection of construction wood by 19%, the illegal exploitation of wood by 6% and the extension of cities by 5%.
(A)--(B)--(C)--(D)--Figure 5. Survey results.
(A)--(B)--(C)--(D)--Figure 5. Survey results.
(A)--(B)--(C)--(D)--Figure 5. Survey results.
(A)--(B)--(C)--(D)--Figure 5. Survey results.
By analyzing the survey data on the indirect causes of deforestation and degradation, population growth turns out to be the main cause with 70% of the total surveyed, followed by bush fires with 19%. About 11% of participants did not answer this question.
3.2. Projected Population and Area Deforested by 2050
By projecting these population counts to 2050, we found that all curves follow an exponential trend for each locality (
Figure 6
), indicating a gradual increase in population in all cities. Forecasts show that the population of Dongou town, which was the lowest among the five localities between 1986 and 2019, will eventually surpass the populations of Pokola and Ngombe in 2050. Ouesso, which initially had the largest population, is surpassed by Impfondo, which becomes the city with the highest population until the end of the study period.
Figure 6. Simulation of the population growth of the main cities.
If the population continues to grow according to the observed and simulated trend, the locality of Impfondo will reach a population of 151,681 inhabitants, Dongou will have 48,804 inhabitants, Ouesso will have a population of 93,339 inhabitants, Pokola will have 35,634 inhabitants and Ngombe will reach 28,679 inhabitants by 2050 (
Figure 6
). It is important to note that the populations of Impfondo and Dongou will double their current numbers by 2050.
The deforestation forecasts in the vicinity of different cities are presented in
Figure 7
. It reveals that by the year 2050, the areas of deforested land are increasing. In 2019, for example, the deforested area was 391.4 hectares; it will increase to an estimated area of 1829.6 hectares in 2050 for the city of Dongou, representing a loss of 1438.1 hectares during this period. This increase in the deforested area is also significant for the cities of Impfondo, Ouesso, Pokola, and Ngombe, with respective values of 3298.1 hectares, 3268.5 hectares, 1368.0 hectares, and 847.0 hectares. This corresponds to estimated forest losses of 2222.5 hectares, 1829.6 hectares, 786.8 hectares, and 487.2 hectares between 2019 and 2050. These projections highlight the need to pay particular attention to the cities of Ouesso, Dongou and Impfondo, whose total projected areas will be significant if no sustainable land management measures are put in place.
(A)--(B)--Figure 7. Simulation of increased deforestation around major cities.
(A)--(B)--Figure 7. Simulation of increased deforestation around major cities.
4. Discussion4.1. Model of Deforestation and Forest Degradation
The model of deforestation and forest degradation proposed in this study (
Figure 8
) was constructed from the direct and indirect factors of deforestation and forest degradation (
Table 1
) identified through the analysis of survey data collected on the field during the various missions. Among these direct and indirect causes are: Urban growth, timber extraction, shifting agriculture, population growth, proximity to waterways, etc. Indeed, the main direct cause of land conversion is urban growth. According to
[34]
, the processes of degradation and deforestation evolve from specific areas corresponding to towns or villages. This observation is confirmed by our studies in the localities concerned. Data collected from local governments, indigenous populations and colonial document archives indicate that the five localities studied were initially fishing camps, which have now become developed towns. This urban growth is induced by the increase in population, which in turn positively influences land use planning, land occupation and the issue of poverty of this population and economic activities. These factors interact with traditional agriculture, illegal logging and the use of wood as a source of energy. Generally, at the level of territorial governance, the central government develops projects aimed at improving the habitat. However, this development can exceed the limits and lead to deforestation. For example, in the city of Ouesso, one of the main cities studied, urban growth at the expense of forests is explained by the occupation of land for cadastral purposes. The increase in population is manifested by anarchic occupation of land and a growing demand for housing, which pushes the State to develop the territory to promote the development of different localities. The development model of cities, characterized by rapid population growth and anthropogenic pressure, leads to logging and therefore to deforestation.
Figure 8. Model of deforestation and forest degradation of deforestation.
<xref ref-type="bibr" rid="scirp.138794-"></xref>Table 1. Causes according to the different study areas.
IMPFONDO
DONGOU
OUESSO
POKOLA
NGOMBE
Agricultural extension
Subsistence farming
X
X
X
X
X
Cash crop farming
X
X
NWFP exploitation/collection
X
X
X
X
X
Wood extraction
wood fuel
X
X
X
X
X
Lumber
X
X
Other factors
Accessibility (river, infrastructure)
X
X
X
X
X
4.2. Influence of Population on Forests and Urban Growth
The results of the study showed a rapid increase in the human population in all the forest localities studied. Indeed, the Republic of Congo is one of the most urbanized countries in the world
[35]
[36]
. The large forest towns of the Republic of Congo have experienced a high concentration of population in recent decades
[35]
[37]
-
[39]
. According to the latest estimates, 61.8% of the population lives in urban areas, with a higher concentration in the two main metropolises
[36]
. The reasons vary from North to South of the Republic of Congo depending on the centers of economic interest such as the establishment of forestry companies, administrations, health and education structures (middle and high schools), etc. This observation was also confirmed by
[40]
and
[27]
, who both claim that the city offers opportunities for education, access to health and social services, as well as paid employment to its inhabitants.
[34]
stated that cities are centers of diverse activity. Also, this population increase is not only due to an increase in births within local populations, but rather also, to the arrival of external populations (refugees from the civil war) due to armed conflicts in the Democratic Republic of the Congo, Rwanda and the Central African Republic. Their installation has contributed significantly to the expansion of cities by building sites on the outskirts for their accommodation
[41]
. For example, the town of Impfondo received a total of 30,600 refugees from the DRC in November 2000, according to an NGO, African doctors
[35]
. The GAF project has shown that, from 2000 to 2010, significant areas of forest were lost around the localities of Bétou, Dongou and Impfondo, leading to the expansion of these towns.
4.3. Impact of Population Increase
Assuming the population continues to grow at the same rate, this indicates a considerable increase in the local population. This increase in population will lead to an expansion of residential, agricultural and fallow areas by 2050 in each locality studied.
Thus, population growth puts pressure on the land by using large areas for housing construction and for agriculture (cultivation of cassava, corn, etc.) around the study areas. After a maximum of three harvests in cultivated areas, these populations seek new land due to soil depletion. They then turn to forests, which are rich in organic matter and therefore very fertile in nutrients to improve the agricultural yield of their crops.
Moreover, as the population increases, the percentage of poor population becomes more important, which translates into a scarcity of income-generating activities in all forest localities. Existing activities are not able to provide income that can be redistributed to the entire local population, which would contribute to limiting the exploitation of forest resources.
Another impact of population growth in a city, in addition to the need for energy, is the search for food and access to non-timber forest products (NTFPs). Population growth leads to growing demand for goods, services and livelihoods, which degrades the environment and puts pressure on natural resources
[42]
. The pressure increases with the population density per hectare. For example, the town of Impfondo, which is the administrative capital of the department of Likouala and is home to the only major high school in the department, has a large young population. Unfortunately, this youth, deprived of financial means, turns to forest resources, such as the collection of NTFPs such as honey, edible fruits, caterpillars, asparagus, etc., to meet their needs. These practices are generally accompanied by the killing of certain species. If socioeconomic parameters do not change, it is expected that the extraction of natural resources to meet nutritional needs will increase in the context of this study.
4.4. Impact of Economic Growth on Deforestation
According to
[24]
, when population increase is not accompanied by economic growth, it has adverse consequences on the population situation and threatens the sustainability of natural resources (aggravates the consequences of deforestation). In dense forest areas, local people are very poor and highly dependent on forest resources for their livelihoods
[43]
[44]
. In the Republic of Congo, the percentage of the population living below the national poverty line fell from 50.7% in 2005 to 40.9% in 2011
[36]
. This rate is 64.4% in the department of Sangha and 66.7% in the department of Likouala
[45]
, the two departments involved in this study. These statistics underline the total dependence of local populations on forest resources.
Field investigations revealed traces of human activities in the forests up to about 15 km around the towns of Impfondo and Dongou. For example, Dongou had only 8 state officials at the time of the survey, which also reflects the low circulation of money from the central government. In these two study localities (Impfondo and Dongou), the prevailing model is the impoverishment model developed by
[15]
. In this model, the poor population exerts pressure on the forest (clearing for agriculture, felling of trees for firewood, etc.) to meet their primary needs. According to the model called “environmental Kuznets curve”, economic growth worsens the consequences of deforestation when per capita income is low
[46]
.
In these localities, slash-and-burn agriculture is the main livelihood activity and source of income in these localities
[47]
-
[49]
.
[46]
points out that a significant annual loss of forest area and a significant annual gain of agricultural area have been observed in low-income countries. Although some studies have noted that an increase in population does not necessarily lead to an expansion of agricultural land
[27]
, other studies have found that as the population increases, the demand for agricultural products is also increasing, leading to an increase in urban areas
[48]
.
[50]
asserts that the increase in urban population translates into a growing need for food, thus encouraging commercial farmers to increase their production.
[47]
points out in his study that African cities, with their characteristics such as demographic explosion, uncontrolled urbanization and the importance of the informal sector, promote the development of urban and peri-urban agriculture. In this context, the poor population, which does not have sufficient means, illegally exploits the wood both for the construction of houses, the manufacture of canoes, furniture and also for food (wood fuel).
5. Conclusions
The study of land conversion is a complex process involving many actors and complex interactions between direct and indirect drivers of deforestation and forest degradation in peri-urban areas. These activities, which initially lead to forest degradation, eventually lead to deforestation due to the lack of forest recovery due to urban growth. Population growth, land use planning, land occupation, population poverty and economic activities are the main factors that contribute to deforestation and forest degradation in the localities studied. In other words, it should be said that there are three main things that would explain the destruction of forests around large cities: the satisfaction of food needs, energy needs, and housing. As the population grows, so does the demand for space, building materials, wood fuel and food. Consequently, this leads to an expansion of urban areas and an increase in pressure on the surrounding forests.
If this increase in population continues at the same rate, it will lead to a considerable increase in the local population. It is therefore necessary to put in place measures for the conservation and sustainable management of forest resources to prevent the continued loss of forests and preserve the environment. We have outlined several suggestions to promote the conservation and sustainable management of forest resources, including:
References
Mooney, H., Larigauderie, A., Cesario, M., Elmquist, T., Hoegh-Guldberg, O., Lavorel, S., et al. (2009) Biodiversity, Climate Change, and Ecosystem Services. Current Opinion in Environmental Sustainability, 1, 46-54. >https://doi.org/10.1016/j.cosust.2009.07.006
Vardoulakis, S., Dimitroulopoulou, C., Thornes, J., Lai, K., Taylor, J., Myers, I., et al. (2015) Impact of Climate Change on the Domestic Indoor Environment and Associated Health Risks in the UK. Environment International, 85, 299-313. >https://doi.org/10.1016/j.envint.2015.09.010
Javadinejad, S., Dara, R. and Jafary, F. (2019) Taking Urgent Actions to Combat Climate Change Impacts. Annals of Geographical Studies, 2, 1-13. >https://doi.org/10.22259/2642-9136.0204001
Glasnovic, Z., Margeta, K. and Logar, N.Z. (2020) Humanity Can Still Stop Climate Change by Implementing a New International Climate Agreement and Applying Radical New Technology. Energies, 13, Article 6703. >https://doi.org/10.3390/en13246703
IPCC, GIEC (2007) Changements climatiques 2007: Rapport de synthèse. Quatrième rapport d’évaluation du GIEC.
Bellassen, V., Crassous, R., Dietzsch, L. and Schwartzman, S. (2008) Réductions des émissions dues à la déforestation et à la dégradation des forêts: Quelle contribution de la part des marches du carbone? Etude Climat, No. 14, 1-44.
UNEP (2012) The Emissions Gap Report 2012. United Nations Environment Pro-gramme (UNEP) Nairobi.
Pearson, T.R.H., Brown, S., Murray, L. and Sidman, G. (2017) Greenhouse Gas Emissions from Tropical Forest Degradation: An Underestimated Source. Carbon Balance and Management, 12, Article No. 3. >https://doi.org/10.1186/s13021-017-0072-2
Geist, H.J. and Lambin, E.F. (2001) III Land-Use and Land-Cover Change (LUCC) Project IV. International Human Dimensions Program on Global Environmental Change (IHDP) V. International Geosphere-Biosphere Program (IGBP), LUCC Report Series.
Austin, K.G., Schwantes, A., Gu, Y. and Kasibhatla, P.S. (2019) What Causes Deforestation in Indonesia? Environmental Research Letters, 14, Article 024007. >https://doi.org/10.1088/1748-9326/aaf6db
López-Carr, D. (2021) A Review of Small Farmer Land Use and Deforestation in Tropical Forest Frontiers: Implications for Conservation and Sustainable Livelihoods. Land, 10, Article 1113. >https://doi.org/10.3390/land10111113
Oszwald, J. (2005) Dynamique des formations agroforestières en Côte d’Ivoire (des années 1980 aux années 2000). Suivi par télédétection et développement d’une approche cartographique. Doctorat Thèse, Laboratoire de Géographie des Milieux Anthropisés.
Konan, E., Mafou, K.C., Sylla, D., Diomande, G. and Lida, D.S. (2019) Modélisation prospective de la déforestation dans le Parc National du Mont Sangbé (Côte d’Ivoire). Conférence OSFACO: Des images satellites pour la gestion durable des territoires en Afrique, 13-15 March 2019, Cotonou, 1-23.
Some, Y.S.C. (2010) Modélisation de la distribution spatiale des formes moléculaires M et S D’Anopheles gambiae sensu stricto au Burkina Faso avec les SIG et l’analyse spatiale. Docteur Thèse, l’université d’Orléans.
Rudel, T. and Roper, J. (1997) The Paths to Rain Forest Destruction: Crossnational Patterns of Tropical Deforestation, 1975-1990. World Development, 25, 53-65. >https://doi.org/10.1016/s0305-750x(96)00086-1
Vieilledent, G., Grinand, C. and Vaudry, R. (2013) Prévision de la déforestation et des émissions de carbone dans les pays en développement tropicaux confrontés à l’expansion démographique: Une étude de cas à Madagascar. Ecology and Evolution, 3, 1702-1716. >https://doi.org/10.1002/ece3.550
Mosnier, A., Havlík, P., Obersteiner, M., Aoki, K., Schmid, E., Fritz, S., et al. (2012) Modeling Impact of Development Trajectories and a Global Agreement on Reducing Emissions from Deforestation on Congo Basin Forests by 2030. Environmental and Resource Economics, 57, 505-525. >https://doi.org/10.1007/s10640-012-9618-7
Gillet, P., Vermeulen, C., Feintrenie, L., Dessard, H. and Garcia, C. (2016) Quelles sont les causes de la déforestation dans le bassin du Congo? Synthèse bibliographique et études de cas. BASE, 20, 183-194. >https://doi.org/10.25518/1780-4507.13022
Lambin, E.F. and Strahlers, A.H. (1994) Change-Vector Analysis in Multitemporal Space: A Tool to Detect and Categorize Land-Cover Change Processes Using High Temporal-Resolution Satellite Data. Remote Sensing of Environment, 48, 231-244. >https://doi.org/10.1016/0034-4257(94)90144-9
Tchatchou, B., Sonwa, D.J., Ifo, S. and Tiani, A.M. (2015) Déforestation et dégradation des forêts dans le bassin du Congo: Etat des lieux, causes actuelles et perspectives. Papier occasionnel, CIFOR, Bogor.
Kouassi, J.L. (2014) Suivi de la dynamique de l’occupation du sol à l'aide de l’imagerie satellitaire et des systèmes d’informations géographiques: Cas de la direction régionale des eaux et forêt de Yamoussoukro (Cote d’Ivoire). Mémoire de fin d’étude pour l’obtention du diplôme approfondie (DAA) en Eaux et Forêts, Ecole supérieure d’Agronomie (ESA).
Ngamba, M., Kabuang’a, M., Sungu, A., Kahindo, J.-M. and Nshimba, H. (2016) Analyse prospective de la déforestation estimée par télédétection dans la réserve de biosphère de Yangambi. International Journal of Innovation and Scientific Research, 24, 236-254.
Gómez-Ossa, L.F. and Botero-Fernández, V. (2017) Application of Artificial Neural Networks in Modeling Deforestation Associated with New Road Infrastructure Projects. DYNA, 84, 68-73. >https://doi.org/10.15446/dyna.v84n201.54310
Rakotondrasoa, L.O., Malaisse, F. and Bogaert, J. (2017) Modelling of the Forest Landscape Dynamics of Réserve Spéciale D’ambatovaky (Northeast of Madagascar). Tropicultura, 35, 312-324. >https://doi.org/10.25518/2295-8010.1102
Dezécache, C. (2017) Les fondements de REDD+ à l’épreuve des modèles de déforestation: Cas d’étude sur le Plateau des Guyanes. Ecologie, Environnement. Université de Guyane.
Ifo, S.A., Binsangou, S., Nzingoula, S. and Louvouandou, L. (2017) Cartographie des sites Forestiers de la ville de Brazzaville dans la période de 1946 à 2016 et étude des causes de la déforestation. Revue Ivoirienne de Géographie des Savanes, No. 4, 55-72.
Eloy, L. and Le Tourneau, F. (2009) L'urbanisation provoque-t-elle la déforestation en Amazonie? Innovations territoriales et agricoles dans le nord-ouest Amazonien (Brésil). Annales de géographie, 667, 204-227. >https://doi.org/10.3917/ag.667.0204
Oura, R.K. (2012) Extension urbaine et protection naturelle: La difficile expérience d’Abidjan. VertigO, 12, 1-24. >https://doi.org/10.4000/vertigo.12966
Zhang, Y. and Guindon, B. (2005) Landscape Analysis of Human Impacts on Forest Fragmentation in the Great Lakes Region. Canadian Journal of Remote Sensing, 31, 153-166. >https://doi.org/10.5589/m05-002
Duveiller, G., Defourny, P., Desclée, B. and Mayaux, P. (2008) Deforestation in Central Africa: Estimates at Regional, National and Landscape Levels by Advanced Processing of Systematically-Distributed Landsat Extracts. Remote Sensing of Environment, 112, 1969-1981. >https://doi.org/10.1016/j.rse.2007.07.026
Lambin, E.F., Turner, B.L., Geist, H.J., Agbola, S.B., Angelsen, A., Bruce, J.W., et al. (2001) The Causes of Land-Use and Land-Cover Change: Moving Beyond the Myths. Global Environmental Change, 11, 261-269. >https://doi.org/10.1016/s0959-3780(01)00007-3
Allen, J.C. and Barnes, D.F. (1985) The Causes of Deforestation in Developing Countries. Annals of the Association of American Geographers, 75, 163-184. >https://doi.org/10.1111/j.1467-8306.1985.tb00079.x
Nguyen Van, P. and Azomahou, T.T. (2003) Déforestation, croissance économique et population: Une étude sur données de panel. Revue économique, 54, 835-855. >https://doi.org/10.3917/reco.544.0835
Bamba, I., Yedmel, M.S. and Bogaert, J. (2010) Effets des routes et des villes sur les forêts denses dans la province orientale de la République Démocratique du Congo. European Journal of Scientific Research, 43, 417-429.
Gazel, H., Harre, D. and Moriconi-Ebrard, F. (2010) Rapport général de l’étude «Africapolis II; l’Urbanisation en Afrique Centrale et Orientale». Fiche Pays République du Congo.
World Bank Group (Groupe Banque mondiale) (2017) Rapport sur l’urbanisation en Afrique: Pour soutenir la croissance et améliorer la vie des habitants et des entreprises dans les villes. Communiqué de presse.
Tati, G. (1987) La croissance urbaine au Congo (1960-1984). Institut de Formation et de Recherche Démographiques (EFORD), 177-187.
Ziavoula, R.E. (1996) Villes secondaires et pouvoirs locaux en Afrique sub-saharienne. Document de Recherche, Nordiska Afrikainstitutet.
RPP (Proposition pour la Préparation à la REDD+) (2011) Ministère du développement durable, de l’économie forestière et de l’environnement (MDDEFE), République du Congo.
Ferland, A. (2015) La conservation de la biodiversité en milieu urbain: Comment aménager les villes du monde? Essaie du double diplôme en vue de l’obtention des grades de maître en environnement et de maître en écologie international, 91P. >https://savoirs.usherbrooke.ca/bitstream/handle/11143/8020/Ferland_Andreanne_MEnv_2015.pdf
Binsangou, S., Ifo, S.A., Ibocko, L., Louvouandou L., Tchindjang, M. and Koubouana, F. (2018) Urban Growth and Deforestation by Remote Sensing in the Humid Tropical Forest of Congo Bassin: Case of Impfondo in Republic of Congo. American Journal of Environment and Sustainable Development, 3, 46-54.
World Bank (1992) Environnement, Macroéconomie et croissance économique, Finances et développement du secteur financier, Gouvernance (Rapport annuel de la Banque mondiale).
Rautner, M., Leggett, M. and Davis, F. (2013) Le Petit Livre des grands moteurs de déforestation, Global Canopy Programme, Oxford.
Sunderlin, W.D. et al. (2007) Poverty and Forests: Multi-Country Analysis of Spatial Association and Proposed Policy Solutions. CIFOR, Bogor.
JORC (2022) Journal Officiel de la république du Congo. Edition spéciale N˚5.
FAO (2016) Situation des forêts du monde 2016. Forêts et agriculture: Défis et possibilités concernant l’utilisation des terres.
Dauvergne, S. (2011) Les espaces urbains et péri-urbains à usage agricole dans les villes d’Afrique subsaharienne (Yaoundé et Accra): Une approche de l’intermédiarité en géographie. Ecole normale supérieure de Lyon.
Binsangou, S., Ifo, S.A., Koubouana, F., Louvouandou, L.F., Nzingoula, S. and Mansisse, L. (2017) Suivi par télédétection de la déforestation due à la croissance urbaine de la ville de Ouesso dans la période de 2000-2016, République du Congo. International Journal of Innovation and Scientific Research, 30, 186-197.
Okanga-Guay, M., Mpie Simba, C., Ndonghan Iyangui, N., Moussavou, G., Obiang Ebanega, M., Biboutou, A.S., Nkoumakali, B. and Mintsa Nguema, R. (2018) Transformations des paysages périurbains de Libreville (Gabon): Dynamiques de l’occupation du sol par télédétection de la zone de Bambouchine-Bikélé, 1990-2014. Revue Canadienne de Géographie Tropicale, 5, 25-33.
EDF (2015) Forêts du Bassin du Congo—Forêts et changements climatiques.