Spatial Patterns Of Deforestation Research Articles

Remote Sensing for Deforestation in Rural Areas of Ghana

Purpose: The main objective of this study was to investigate remote sensing technology for deforestation in rural areas of Ghana.
 Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The findings revealed that there exists a contextual and methodological gap relating to remote sensing for deforestation in rural areas of Ghana. Preliminary empirical review illuminated the multifaceted nature of deforestation in this region. It harnessed remote sensing technology and drew upon theories such as environmental determinism, Land-Use and Land-Cover Change (LUCC), and spatial diffusion to unveil the complex drivers and spatial patterns of deforestation. These findings underscore the crucial role of the natural environment, human activities, and diffusion processes in shaping deforestation dynamics. The study's relevance extends to policymakers, NGOs, and local communities, offering a foundation for evidence-based decision-making and sustainable land management practices that balance economic development with environmental conservation. Ultimately, this interdisciplinary approach enhances our understanding of deforestation and informs strategies to address this pressing environmental challenge.
 Unique Contribution to Theory, Practice and Policy: Environmental Determinism Model, Land-Use and Land-Cover Change (LUCC) Theory and the Spatial Diffusion Theory may be used to anchor future studies on deforestation. Based on the study's findings on "remote sensing for deforestation in rural areas of Ghana," key recommendations include strengthening forest conservation policies, enhancing remote sensing capacity for monitoring, promoting community engagement and education on forest conservation, and emphasizing continued research and monitoring efforts to adapt and refine conservation strategies in response to evolving deforestation patterns and drivers. These recommendations aim to mitigate the impacts of deforestation, protect vital ecosystems, and support the livelihoods of local communities in rural Ghana.

The spatial patterns of deforestation in the Ko'mara forest area

Land-use changes result from changes in the human population, their activities, and social and environmental activities, which occur in a complex and dynamic manner. The change of forested areas to non-forested areas, also known as deforestation, is a land-use change. Deforestation influences land cover by creating a specific distribution pattern (fragmentation). Almost the entire forest has been fragmented into smaller areas, but the pattern of each of these forest areas is uncertain. As a consequence, it would be more challenging to control potential deforestation. Using the spatial metrics of Clumpiness index, Contiguity index, and Patch density, deforestation in one of the forest areas, namely the Ko'mara forest area, was carried out. This research was analyzed using GIS and Fragstat software in raster data format. The analysis results show that the spatial pattern of deforestation in this area has a clumped distribution, with a high level of patch contiguity and a low to medium level of fragmentation. This pattern represents the form of deforestation in the Ko'mara forest area in 2005-2010 and 2010-2015 was classified as Low Deforestation with a deforestation rate of <1%, but was classified as Moderate Deforestation in 2015-2019 because the value obtained was 1.1%.

Assessing Elevation-Based Forest Dynamics over Space and Time toward REDD+ MRV in Upland Myanmar

Implementation of a measuring, reporting, and verifying (MRV) framework is essential for reducing emissions from deforestation and forest degradation (REDD+). According to the United Nations Framework Convention on Climate Change, MRV can be regarded as an important mechanism to mitigate global warming. Upland Myanmar, with an elevation of ~80–2600 m, is experiencing tropical deforestation, which is commonly explained by the expansion of shifting cultivation. The vegetation change tracker algorithm, with its high-automation and wild-adaptation features, and the enhanced integrated forest z-score were applied in this elevation-based study of time series deforestation monitoring in upland Myanmar using data from 2003 to 2015. Four spatial patterns of deforestation, namely stripes, adjacent, filled, and staggered, were found in the research area. Moreover, our work showed that the center of elevation of deforestation was ~1000 m. Further analysis revealed that this center tended to shift to a higher elevation over time; a “golden cross”/changeover could be deciphered at ~1000 m, indicating that the scale and intensity of shifting cultivation continue to expand vertically. The results suggest the need to track the elevation-based signature of vegetation clearings to help achieve the goals of REDD+ at the regional level in tropical rainforest countries.

Deforestation in Continental Ecuador with a Focus on Protected Areas

Forest conservation is of particular concern in tropical regions where a large refuge of biodiversity is still existing. These areas are threatened by deforestation, forest degradation and fragmentation. Especially, pressures of anthropogenic activities adjacent to these areas significantly influence conservation effectiveness. Ecuador was chosen as study area since it is a globally relevant center of forest ecosystems and biodiversity. We identified hotspots of deforestation on the national level of continental Ecuador between 1990 and 2018, analyzed the most significant drivers of deforestation on national and biome level (the Coast, the Andes, The Amazon) as well as inside protected areas in Ecuador by using multiple regression analysis. We separated the national system of protected areas (SNAP) into higher and lower protection levels. Besides SNAP, we also considered Biosphere Reserves (BRs) and Ramsar sites. In addition, we investigated the rates and spatial patterns of deforestation in protected areas and buffer zones (5 km and 10 km outwards the protected area boundaries) using landscape metrics. Between 1990 and 2018, approximately 4% of the accumulated deforestation occurred within the boundaries of SNAP, and up to 25.5% in buffer zones. The highest rates of deforestation have been found in the 5 km buffer zone around the protected areas with the highest protection level. Protected areas and their buffer zones with higher protection status were identified as the most deforested areas among SNAP. BRs had the highest deforestation rates among all protected areas but most of these areas just became BRs after the year 2000. The most important driver of deforestation is agriculture. Other relevant drivers differ between the biomes. The results suggest that the SNAP is generally effective to prevent deforestation within their protection boundaries. However, deforestation around protected areas can undermine conservation strategies to sustain biodiversity. Actions to address such dynamics and patterns of deforestation and forest fragmentation, and developing conservation strategies of their landscape context are urgently needed especially in the buffer zones of areas with the highest protection status.

Models and spatial patterns of deforestation in the Jeneberang watershed

Deforestation is an event of permanent land cover change from forest cover to non-forest cover. Deforestation events are very influential on the condition of a watershed area. One of the watersheds on the island of Sulawesi that has become a concern is the Jeneberang watershed because of its influence on the city of Makassar and is a priority watershed in Indonesia. This study aims to analyze the model and spatial pattern of deforestation in the Jeneberang watershed. The deforestation analysis model uses the binary logistic regression method by including factors such as a river, population density, road, count, and slope. Analysis of the spatial pattern of deforestation using Fragstat software based on three indices to describe the spatial pattern, namely the Clumpiness Index, Contiguity Mean Index, and Patch Density. The model of deforestation in the Jeneberang watershed shows the road network factor that has the most influence on the occurrence of deforestation. The road network is quite high in all areas in the Jeneberang watershed including the upstream part as a protection zone. The road network serves as community access between villages and sub-districts in Gowa Regency and connects other regencies such as Sinjai, Takalar, and Jeneponto. The spatial pattern of deforestation in the Jeneberang watershed is grouping, the level of connectivity is high, and it is not fragmented. This pattern shows that deforestation occurs in groups, is interconnected with previously deforested areas, and has a fairly large area. This pattern occurs at a relatively low rate and remains the same when the deforestation rate increases or decreases.

Emerging evidence that armed conflict and coca cultivation influence deforestation patterns

The effect of armed conflict on deforestation in biodiverse regions across Earth remains poorly understood. Its association with factors like illegal crop cultivation can obscure its effect on deforestation patterns. We used Colombia, a global biodiversity hotspot with a complex political history, to explore the association of both armed conflict and coca cultivation with deforestation patterns. We generated spatial predictions of deforestation pressure based on the period 2000–2015 to understand how armed conflict and coca cultivation are associated with spatial patterns of deforestation and assess the spatial distribution of deforestation pressure induced by armed conflict and coca cultivation. Deforestation was positively associated with armed conflict intensity and proximity to illegal coca plantations. A deforestation model including 14 variables was 78% accurate in predicting deforestation at a 10 km2 resolution. On their own armed conflict and coca cultivation had a notable effect, particularly in the Amazon, but in combination with other variables it was small. Deforestation pressure induced by armed conflict and coca cultivation was highest in Tumaco and Catatumbo regions and in la Macarena, Sierra Nevada and San Lucas mountains—all areas of high biodiversity and conservation importance. In some regions, lack of governance after the peace accords is increasing armed conflict, and our results suggest that those increases in conflict may increase deforestation in those areas. The methods used here can be replicated to help understand the complex ways in which armed conflict affects deforestation patterns in other regions.

Profile, Level of Vulnerability and Spatial Pattern of Deforestation in Sulawesi Period of 1990 to 2018

Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used in this study are (1) land cover in 1990, 2000, 2010; (2) Landsat 8 imagery in 2018; (3) administrative map of BIG in 2018. The methods used are (1) image classification with on-screen digitation techniques following the PPIK land cover classification guidelines, Forestry Planning Agency (2008) using ArcGIS Desktop 10.6 from ESRI; (2) overlapping maps; (3) analysis of deforestation; (4) analysis of deforestation profiles, (5) vulnerability analysis; and (6) analysis of distribution patterns of deforestation. The results showed that the profile of deforestation occurring on Sulawesi Island in the 1990–2018 observation period was dominated by profile 3-1-1 (the proportion of large forest area, the highest incidence of deforestation early stage at the beginning, at a low rate) in 13 districts. The level of vulnerability to deforestation is a non-vulnerable category (37 districts) which is directed to become a priority in handling deforestation in Sulawesi. Spatial patterns of the deforestation that occurred randomly and were scattered are dominated by shrubs, dryland agricultural activities, and small-scale plantations.

Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon

The objective of this study was to improve the understanding of the spatial dynamics of evapotranspiration (ET) with a focus on the influence of the current level of Amazonian deforestation through comparative statistics of different land cover. The study area comprised the state of Rondônia, Brazil, which was subdivided into homogeneous regions regarding ET using cluster analysis. In addition, we analyzed the use of a logistic regression model to create deforestation maps in the Amazon based on ET fields. We used orbital data on ET and land cover type from the MOD16 product and the Amazon Forest Satellite Monitoring Project (PRODES), respectively, considering the period from 2000 to 2014. The cluster analysis results showed that for the study area, three homogeneous sub-regions were sufficient to represent the ET variability, mainly considering the intensity and seasonal pattern of this process. Regarding the impacts after changing from forest to deforested area, the analyses indicated that the ET of deforested areas decreased by an average of 28% in the dry period and increased by 4% in the rainy season. The effects observed in the rainy season were not significant at 5% significance according to the Student t-test, unlike the dry period, which presented statistical significance (p-value <0.05). In general, the results indicated that MOD16 data can provide a satisfactory representation of the change in ET in large areas of the Brazilian Amazon. Logistic regression analysis showed that the spatial pattern of deforestation can be identified by biophysical factors such as ET with 87% accuracy, despite the spatial/temporal variability present in the region. These results support this approach as an effective tool for spatial identification of Amazonian deforestation.

Environmental predictors of forest change: An analysis of natural predisposition to deforestation in the tropical Andes region, Peru

The spatial patterns of deforestation are usually non-randomly distributed across the landscape. While anthropogenically driven processes are often addressed in land-use regulation policies and deforestation research, less attention is given to the environmental factors that influence tropical deforestation. This study investigates to what extent climate conditions (temperature and precipitation) and biophysical landscape characteristics (elevation, slope, soil type, forest type, and distance to rivers) facilitate or mitigate deforestation processes in Peru's tropical Andes. A Random Forest regression model was constructed for the entire Peruvian tropical Andes, and separate models were developed for some of the known direct deforestation drivers in the region (coca production, gold mining, and land-use by indigenous and non-indigenous communities). Soil type and precipitation were identified as the most important deforestation predictors when the entire Peruvian tropical Andes was considered, whereas distance to rivers was associated with deforestation by mining activities, and elevation and temperature with coca cultivation areas. Using the regression results, a Random Forest classification model was constructed to locate areas where the composition of environmental factors could either facilitate or mitigate deforestation processes. It was found that almost 85% of the forests classified as having high to very high probability to deforestation were located outside current protected areas. In order to increase conservation impacts, the results suggest that greater consideration should be given to the distribution of environmental factors when designing land-use regulation policies and establishing protected areas.

Identifying Drivers and Spatial Patterns of Deforestation in the Rio Grande Basin, Colombia

Identifying Drivers and Spatial Patterns of Deforestation in the Rio Grande Basin, Colombia

Habitat monitoring and conservation prioritisation of protected areas in Western Ghats, Kerala, India.

Spatially explicit approach is essential to prioritise the ecosystems for biodiversity conservation. In the present study, the conservation status of 20 protected areas of the Western Ghats of Kerala, India, was analysed based on long-term changes in forests (1975-1985-1995-2005-2013), landscape level changes in fragmentation and forest fires (2005-2015). This study has shown that a significant forest loss occurred in protected areas before declaration. Idukki is one of the major protected areas which showed a drastic reduction (18.83%) in its forest cover. During 1985-1995, Periyar tiger reserve had lost 24.19km2 core 3 forest area followed by Peppara (18.54km2), Parambikulam (17.93km2), Chimmony (17.71km2), Peechi-Vazhani (12.31km2) and Neyyar (11.67km2). An area of 71.33km2 of the protected area was affected by fires in 2014. Overall protected area-wise decadal analysis indicates Periyar has the highest number of fire incidences followed by Wayanad, Kurinjimala, Silent Valley and Eravikulam. Disturbances in the form of fires and fragmentation still exist and may have significant conservation threat to flora and fauna. Among protected areas, many are having a probability to go under threat or dynamic stage. Chinnar, Thattekkad and Kurinjimala sanctuaries are representing high levels of vulnerability, or they are near to decline stage. Habitat level monitoring of the anthropogenic disturbances can be efficiently useful for the strategic conservation planning. The present study has provided geospatial database on spatial patterns of deforestation, fragmentation and forest fires in protected areas of Kerala. Conservation prioritization approach based on these parameters will be useful for the strategic planning in the state of Kerala.

Deforestation and cattle expansion in the Paraguayan Chaco 1987–2012

The dry forests of Latin America are among the most dynamic deforestation frontiers in the world and are important carbon and biodiversity reservoirs. Our knowledge on the spatial patterns of deforestation and its proximate drivers remains partial though. We used the full Landsat image archive to reconstruct deforestation and post-deforestation dynamics between 1987 and 2012 for the entire Paraguayan Chaco, where deforestation has been rampant recently. Our classification resulted in reliable land-use change maps (86.16%), highlighting drastic forest losses of almost 44,000 km2 between 1987 and 2012, equaling a deforestation rate of 27% and about 1% yearly, predominantly for grasslands. These likely represented new pastures, making pasture expansion the dominant proximate cause of deforestation. Cropland expansion, in contrast, only played a minor role as a proximate deforestation cause in the Paraguayan Chaco. Deforestation more than doubled between 2001 and 2012 (~29,000 km2) compared to 1987–2000 (~14,000 km2), due to leakage effects from the deforestation ban in the Paraguayan Atlantic Forests in 2004. Interestingly, while grasslands expanded in the Paraguayan Chaco between 1987 and 2000, cattle numbers decreased during the same time period, though strongly increased since. This apparent decoupling of area change and land-use intensity may indicate that the Paraguayan Chaco experienced an amplification period during the 1990s followed by an intensification period since 2001. Thus, our results highlight the need for both, a more detailed monitoring of post-deforestation dynamics and a land systems perspective in order to understand deforestation frontiers and thus ultimately to identify strategies to better balance production and conservation goals.

Analyzing the Spatial Dynamics of Deforestation in Brazilian Amazon

Historically, development in Brazilian Amazon was promoted by permits of deforestation under soft territory control or supervision. However, due to the importance of this biome for biodiversity and ecosystem balance in a global perspective, Brazilian's government has tightened deforestation control. This work investigates the spatial pattern of deforestation in a cross-section and time perspective using global and local spatial data analysis. Global results indicate the existence of high spatial correlation and that deforestation holds concentrated across space, despite the efficacy of policy mechanisms adopted for controlling and reducing the level of deforestation in Legal Amazon. Furthermore, local results support the hypothesis of high spillover effects. Considering the spatial analysis results, we highlight some implications for policy design aiming deforestation control.

Spatial Metrics of Deforestation in Kampar and Indragiri Hulu, Riau Province

The Riau Province has been suffering from the highest deforestation rate in Sumatra, Indonesia. Many and various factors haved been discussed as causes of different deforestation types. This research is focused on evaluating the spatial pattern of deforestation in a specific location respresenting a typical deforestation in Riau. The main objective of this study was to identify spatial metrics to describe deforestation that occurred in Kampar and Indragiri Hulu regencies.The study divided the deforestation process into 3 periods of observation, e.g., 1990–2000, 2000–2010, and 2010–2014. The study based on Landsat satellite imagery aquired in 1990, 2000, 2010, and 2014 as the main data sources. The deforestation was detected using post-classification comparison (PCC) on the basis of 11 land cover classes developed prior to any further change detection. The deforestation was initially derived from reclassifying the original classes into only forest and non-forest classes, and then followed by spatial pattern analysis using Fragstat software. The study shows that 2 spatial pattern of deforestation in Kampar distinctly differs from those occurred in Indragiri Hulu Regency, particularly for the period of 1990–2014. The spatial pattern of deforestation in Kampar Regency were clumped, low contiguous between patch, and high fragmentated. Meanwhile, the spatial pattern in Indragiri Hulu Regency were clumped, high contiguous between patch, and low fragmentated. Profile of deforestation in Kampar Regency was cathegorized into early deforestation and Indragiri Hulu Regency as lately deforestation.

Analysis of deforestation and protected area effectiveness in Indonesia: A comparison of Bayesian spatial models

Tropical deforestation in Southeast Asia is one of the leading causes of carbon emissions and reductions of biodiversity. Spatially explicit analyses of the dynamics of deforestation in Indonesia are needed to support sustainable land use planning but the value of such analyses has so far been limited by data availability and geographical scope. We use remote sensing maps of land use change from 2000 to 2010 to compare Bayesian computational models: autologistic and von Thünen spatial-autoregressive models. We use the models to analyze deforestation patterns in Indonesia and the effectiveness of protected areas. Cross-validation indicated that models had an accuracy of 70–85%. We find that the spatial pattern of deforestation is explained by transport cost, agricultural rent and history of nearby illegal logging. The effectiveness of protected areas presented mixed results. After controlling for multiple confounders, protected areas of category Ia, exclusively managed for biodiversity conservation, were shown to be ineffective at slowing down deforestation. Our results suggest that monitoring and prevention of road construction within protected areas, using logging concessions as buffers of protected areas and geographical prioritization of control measures in illegal logging hotspots would be more effective for conservation than reliance on protected areas alone, especially under food price increasing scenarios.

Fractally deforested landscape: Pattern and process in a tri-national Amazon frontier

Forest clearings in the Amazon are expanding along roads and are enhanced by the associated expansion of human settlements. The purpose of this research is to analyze the spatial patterns associated with this development process using fractal geometry and to partition this development process into different levels by a model-based classification scheme that can be applied to regions globally. A critical region of tropical forest cover in the tri-national frontier in the center of the southwestern Amazon was used as the study area. We utilized box-counting fractal dimensions to describe the spatial patterns of deforestation at a pixel level from 1986 to 2010 in the study region. The evolving pattern of development, as indicated by density-sliced fractal dimension, provides a unique and informative view of a deforesting landscape. The cleared areas have become increasingly compact from 1986 to 2010, where the low fractal dimensions typically represent little to no forest clearings and higher fractal dimensions are associated with more highly developed areas. Such differences are summarized by a classification scheme derived from a mathematical model that partitions the continuous range of fractal dimensions into five possible classes ranging from no or minimal development to highly developed. Such graphical representations of these stages of deforestation in the study region with such spatially explicit pixel-level information enables us to provide multi-level, local, adaptive, and flexible information to forest conservation groups, land managers and related programs.

Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Changes in Size of Deforested Patches in the Brazilian Amazon

Different deforestation agents, such as small farmers and large agricultural businesses, create different spatial patterns of deforestation. We analyzed the proportion of deforestation associated with different-sized clearings in the Brazilian Amazon from 2002 through 2009. We used annual deforestation maps to determine total area deforested and the size distribution of deforested patches per year. The size distribution of deforested areas changed over time in a consistent, directional manner. Large clearings (>1000 ha) comprised progressively smaller amounts of total annual deforestation. The number of smaller clearings (6.25-50.00 ha) remained unchanged over time. Small clearings accounted for 73% of all deforestation in 2009, up from 30% in 2002, whereas the proportion of deforestation attributable to large clearings decreased from 13% to 3% between 2002 and 2009. Large clearings were concentrated in Mato Grosso, but also occurred in eastern Pará and in Rondônia. In 2002 large clearings accounted for 17%, 15%, and 10% of all deforestation in Mato Grosso, Pará, and Rondônia, respectively. Even in these states, where there is a highly developed agricultural business dominated by soybean production and cattle ranching, the proportional contribution of large clearings to total deforestation declined. By 2009 large clearings accounted for 2.5%, 3.5%, and 1% of all deforestation in Mato Grosso, Pará, and Rondônia, respectively. These changes in deforestation patch size are coincident with the implementation of new conservation policies by the Brazilian government, which suggests that these policies are not effectively reducing the number of small clearings in primary forest, whether these are caused by large landholders or smallholders, but have been more effective at reducing the frequency of larger clearings.

The spatial dynamics of deforestation and agent use in the Amazon

The objective of this study was to better understand the linkages between spatial patterns of forest use, land use, and rates of deforestation. We focused on a region of the Brazilian Amazon characterized by a variety of spatial patterns of deforestation and that experienced rapid deforestation in the last decade: the colonization project “Pedro Peixoto” in the extreme East of Acre State. By combining time series of remote sensing data and household survey data, we tested the hypothesis that each spatial pattern of deforestation is associated with specific socio-economic characteristics of land managers. The statistical results support our hypothesis. However, spatial patterns of deforestation are not a consequence of the main economic activities. Rather, spatial patterns of forest use are linked to the pre-conceived design of the settlement project. The main economic activities are influenced by factors such as the property size, their location and disposition, and the availability of infrastructure.

Projecting land use changes in the Neotropics: The geography of pasture expansion into forest

In tropical Latin America, pasture land for extensive grazing continues to expand, mostly at the expense of forest cover. Until now, scientists and policy makers tackling this issue had no geographically exhaustive information at the continental level about the spatial dynamics of this process. On the basis of a land use change-modeling framework we made a projection of potential land use changes for the year 2010. The chosen modeling framework incorporates a number of essential aspects of the complexity of land use change, such as the interrelation of spatial and temporal dynamics, land use history and scale dependence. The model was provided with up-to-date, continent wide, detailed information on present land use and its location factors, selected on the basis of literature. Model inputs were established in collaboration with experts from the region. Significant statistical relations were obtained that describe land use patterns in sub-regions, giving insights into the deforestation process and its location factors. Combined with decision rules and quantitative estimates of land use change, “hot spots” of forest to pasture and crop land conversion were projected. The results envisage a predominant replacement of forest by pasture. Substantially different trends among countries are predicted, both concerning the spatial patterns of deforestation and the substitution trends between land uses. The hot spot maps also show sensitive biological areas that may be at risk. The resulting continent wide map of projected change shall help to target policy attention and measures. It also provides a context to the numerous undergoing deforestation case studies. Finally, it is proposed that the study's results be considered in the priority setting of future research on the causes of deforestation.