Deforestation Patterns Research Articles

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas.

Assessing the Implications of Deforestation and Climate Change on Rural Livelihood in Ghana: a Multidimensional Analysis and Solution-Based Approach.

The Ashanti region in Ghana, abundant in natural resources such as forests and vegetation biomes, significantly supports the livelihoods of a significant portion of the population. The sustainable management of forest resources remains a significant challenge to achieving environmental and economic growth and poverty alleviation. The study aims to identify the drivers of deforestation and assess its impact on the livelihoods of the poor and vulnerable communities in the Ashanti region. The study utilized qualitative and space-based data to examine the patterns of vegetation cover and deforestation from 2000 to 2020. The results revealed moderate to sparse vegetation in Ashanti from 2002, 2005, 2011, 2015, 2017, and 2018, with no vegetation in the northcentral part, attributed to climate change, agricultural practices, government policies, and deforestation-related disasters. The study found a significant correlation (R² = 0.8197) between years and deforestation areas, especially in 2018 at around 16,000 Sqkm, indicating an exponential increase with severe implications for sustainable livelihoods. Much of these changes were reflected in 2020 with a high peak of deforestation towards the southeastern parts of the region. Additionally, the results show that the poor groups are not passive actors but are actively involved in identifying systems and processes through which to build their adaptive capacity and resilience to environmental and climate change-induced changes. The findings provide evidence-based and all-inclusive approaches that would encourage vulnerable and marginalized groups to participate in the co-production and co-creation of policies and strategies. This outcome is geared towards transformative and sustainable communities while ensuring efficient and effective response and recovery capacities of deforested lands.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

Will the EU deforestation-free products regulation (EUDR) reduce tropical forest loss? Insights from three producer countries

The European Union regulation on deforestation-free products (EUDR) represents a policy innovation with important implications for the governance of global commodity chains. We discuss the risks and limitations of this policy derived from an ex-ante assessment of the robustness of its theory of change. For doing so, we use insights from the literature on zero-deforestation commitments and other private standards in value chains and from trade and deforestation patterns in three relevant producer countries (Brazil, Colombia and Indonesia). Despite the potentially positive symbolic effect in mobilizing a global agenda for combating deforestation, the EUDR faces several drawbacks and risks associated with three of its constitutive features: To be demand-oriented, having the value chain as the unit of intervention and holding a high degree of unilateralism. We conclude that to be effective in curbing tropical deforestation, the Regulation must be complemented with international cooperation aiming to strengthen national policies with a territorial approach, as well as social movements addressing the underlying causes of forest loss and human rights violations in the territories where it takes place.

Forest Loss Drivers and Landscape Pressures in a Northern Moroccan Protected Areas’ Network: Introducing a Novel Approach for Conservation Effectiveness Assessment

This study assesses the conservation effectiveness of 21 protected areas (PAs) in Northern Morocco, comprising 3 parks and 18 Sites of Ecological and Biological Interest (SBEIs), against five major landscape pressures (LSPs): deforestation, infrastructure extension, agricultural expansion, fires, and population growth. We propose a novel quantitative methodology using global remote sensing data and exploratory spatial data analysis (ESDA). Data were sourced from Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), and World Population datasets. The combined impact of the five LSPs was measured using a cumulative effect index (CEI), calculated with the Shannon–Wiener formula at a 1 km2 scale. The CEI was analyzed alongside the distance to the PAs’ network using Moran’s index, identifying four spatial association types: high–high (HH), high–low (HL), low–low (LL), low–high (LH), and non-significant (NS) cells. This analysis defined four zones: inner zone (IZ), potential spillover effect zone (PSEZ), statistically non-significant zone (SNSZ), and non-potential effect zone (NPEZ). Conservation effectiveness was quantified using the conservation ratio (CR), which compared the prevalence of LL versus HL units within IZs and PSEZs. Four disturbance levels (very high, high, medium, and low) were assigned to CR values (0–25%, 25–50%, 50–75%, 75–100%), resulting in sixteen potential conservation effectiveness typologies. Initial findings indicated similar deforestation patterns between protected and unprotected zones, with wildfires causing over half of forest losses within PAs. Conservation effectiveness results categorized the 21 PAs into nine typologies, from high conservation to very high disturbance levels. A significant positive correlation (71%) between CRs in both zones underscored the uniform impact of LSPs, regardless of protection status. However, protected natural area zones in the parks category showed minimal disruption, attributed to their advanced protection status. Finally, we developed a methodological framework for potential application in other regions based on this case study.

UAV survey mapping of illegal deforestation in Madagascar

Societal Impact StatementUnmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation.Summary This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery. Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs. By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources. The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest.

Integration of Artificial Intelligence into Sustainable Development Goals in India

In the pursuit of sustainable development, the fusion of artificial intelligence (AI) with legal mechanisms emerges as a potent force for transformative change. This paper delves into the intricate interplay between AI technologies and the legal framework, elucidating their collective capacity to address multifaceted challenges and propel progress towards the Sustainable Development Goals (SDGs). At the forefront of environmental stewardship, AI-driven solutions offer unprecedented capabilities in pollution monitoring, waste management, and climate change adaptation. From predictive analytics for air quality assessment to real-time monitoring of deforestation patterns, AI empowers stakeholders with actionable insights to mitigate environmental degradation and foster resilience. Simultaneously, AI applications hold promise in addressing socio-economic inequities through innovative approaches to poverty alleviation, financial inclusion, and inclusive growth strategies. By leveraging predictive modelling and data analytics, AI facilitates targeted interventions tailored to the unique needs of marginalized communities, thereby fostering sustainable development trajectories. However, the ethical and regulatory dimensions of AI deployment necessitate careful consideration within the legal framework. Ensuring data privacy, algorithmic transparency, and equitable access emerges as imperatives in harnessing the full potential of AI for societal benefit. Through the formulation of robust regulatory frameworks and ethical guidelines, the legal system plays a pivotal role in safeguarding against potential risks and maximizing the societal dividends of AI innovation. Moreover, interdisciplinary collaboration and stakeholder engagement are paramount in navigating the evolving landscape of AI governance, fostering dialogue, and consensus-building among diverse stakeholders. Thus, this paper advocates for a holistic approach to sustainable development, synergizing the transformative potential of AI with the protective mechanisms of the legal system. By fostering innovation, inclusivity, and accountability, this symbiotic relationship serves as a catalyst for advancing the global agenda towards a more equitable, resilient, and sustainable future. Keywords: Artificial Intelligence, Climate Change Adaptation, Deforestation, Education, Healthcare, Legal System, Pollution, Poverty, Sustainable Development Goals, Water and Waste Management.

Clouds and plant ecophysiology: missing links for understanding climate change impacts

Observations and models indicate that human activity is altering cloud patterns on a global scale. Clouds impact incident visible and infrared radiation during both day and night, driving daily and seasonal variability in plant temperatures—a fundamental driver of all physiological processes. To understand the impacts of changing cloud patterns on essential plant-based processes such as carbon sequestration and food production, changes in local cloud regimes must be linked, via ecophysiology, with affected plant systems. This review provides a comprehensive treatment of cloud effects (apart from precipitation) on fundamental ecophysiological processes that serve as the basis of plant growth and reproduction. The radiative effects of major cloud types (cumulus, stratus, cirrus) are differentiated, as well as their relative impacts on plant microclimate and physiology. Cloud regimes of major climate zones (tropical, subtropical, temperate, polar) are superimposed over recent changes in cloud cover and primary productivity. The most robust trends in changing global cloud patterns include: (i) the tropical rain belt (comprised mostly of deep convective clouds) is narrowing, shifting latitudinally, and strengthening, corresponding with shorter but more intense rainy seasons, increased clouds and precipitation in some parts of the tropics, and decreases in others; (ii) tropical cyclones are increasing in intensity and migrating poleward; (iii) subtropical dry zones are expanding, resulting in fewer clouds and drier conditions at these latitudes; (iv) summer mid-latitude storm tracks are weakening and migrating poleward, and clouds in temperate regions are decreasing; and (v) clouds over the Arctic are increasing. A reduction in coastal fog and low clouds (including those associated with montane cloud forests) have also been observed, although these trends can be partially attributed to local patterns of deforestation, urbanization, and/or reductions in aerosols associated with clean air initiatives. We conclude by highlighting gaps in the cloud-ecophysiology literature in order to encourage future research in this under-studied area.

Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review

Deforestation poses a critical global threat to Earth’s ecosystem and biodiversity, necessitating effective monitoring and mitigation strategies. The integration of deep learning with remote sensing offers a promising solution for precise deforestation segmentation and detection. This paper provides a comprehensive review of deep learning methodologies applied to deforestation analysis through satellite imagery. In the face of deforestation’s ecological repercussions, the need for advanced monitoring and surveillance tools becomes evident. Remote sensing, with its capacity to capture extensive spatial data, combined with deep learning’s prowess in recognizing complex patterns to enable precise deforestation assessment. Integration of these technologies through state-of-the-art models, including U-Net, DeepLab V3, ResNet, SegNet, and FCN, has enhanced the accuracy and efficiency in detecting deforestation patterns. The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. Additionally, attention mechanisms combat overfitting, while group and shuffle convolutions further enhance accuracy by reducing dominant filters’ contribution. These strategies collectively fortify the robustness of deep learning models in deforestation analysis. The integration of deep learning techniques into remote sensing applications serves as an excellent tool for deforestation identification and monitoring. The synergy between these fields, exemplified by the reviewed models, presents hope for preserving invaluable forests. As technology advances, insights from this review will drive the development of more accurate, efficient, and accessible deforestation detection methods, contributing to the sustainable management of the planet’s vital resources.

Analysis of Forest Fragmentation and Connectivity Using Fractal Dimension and Succolarity

Forests around the world, vital for ecological, economic, and social functions, are facing increasing threats such as deforestation and habitat fragmentation. This study introduces “succolarity” to analyse forest connectivity and fragmentation directionally in the Romanian Carpathians from 2000 to 2021. Besides traditional indices such as the fractal fragmentation index (FFI), the fractal fragmentation and disorder index (FFDI), the local connected fractal dimension (LCFD), and succolarity, two novel indices are proposed: potential succolarity and delta (Δ) succolarity, which provide nuanced insights into environmental changes and human interventions in forests worldwide. The succolarity tests showed invariance at the image scale and sensitivity to the obstacles in different image scenarios. The combined analysis of succolarity and fractal indices reveals dynamic patterns of connectivity, fragmentation, and spatial disturbance. The directional insights of succolarity highlight and enhance understanding of deforestation patterns. The FFI and FFDI show different levels of fragmentation across mountain groups, while the LCFD details local connectivity. The adaptability of the method makes it globally applicable, supporting biodiversity conservation and landscape management. In conclusion, the integration of succolarity and traditional fractal indices provides a robust methodology for the comprehensive analysis of forest fragmentation. This advancement not only supports the sustainable management of the Romanian Carpathians but also represents a versatile approach applicable to ecosystems worldwide, ensuring the long-term resilience of forested regions.

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.

DEFORESTATION DETECTION IN THE AMAZON WITH SENTINEL-1 SAR IMAGE TIME SERIES

Abstract. Deforestation has a significant impact on the environment, accelerating global warming and causing irreversible damage to ecosystems. Large-scale deforestation monitoring techniques still mostly rely on statistical approaches and traditional machine learning models applied to multi-spectral, optical satellite imagery and meta-data like land cover maps. However, clouds often obstruct observations of land in optical satellite imagery, especially in the tropics, which limits their effectiveness. Moreover, statistical approaches and traditional machine learning methods may not capture the wide range of underlying distributions in deforestation data due to limited model capacity. To overcome these drawbacks, we apply an attention-based neural network architecture that learns to detect deforestation end-to-end from time series of synthetic aperture radar (SAR) images. Sentinel-1 C-Band SAR data are mostly independent of the weather conditions and our trained neural network model generalizes across a wide range of deforestation patterns of Amazon forests. We curate a new dataset, called BraDD-S1TS, comprising approximately 25,000 image sequences for deforested and unchanged land throughout the Brazilian Amazon. We experimentally evaluate our method on this dataset and compare it to state-of-the-art approaches. We find it outperforms still-in-use methods by 13.7 percentage points in intersection over union (IoU). We make BraDD-S1TS publicly available along with this publication to serve as a novel testbed for comparing different deforestation detection methods in future studies.

Forecasting Worldwide Temperature from Amazon Rainforest Deforestation Using a Long-Short Term Memory Model

Biosphere–atmosphere interactions are a critical component of the Earth’s climate system. Many of these interactions are currently contributing to temperature increases and accelerating global warming. One of the main factors responsible for this is land use and land cover changes; in particular, this work models the interaction between Amazon rainforest deforestation and global temperatures. A Long Short-Term Memory (LSTM) neural network is proposed to forecast temperature trends, including mean, average minimum, and average maximum temperatures, in 20 major cities worldwide. The Amazon rainforest, often referred to as the Earth’s “lungs”, plays a pivotal role in regulating global climate patterns. Over the past two decades, this region has experienced significant deforestation, largely due to human activities. We hypothesize that the extent of deforestation in the Amazon can serve as a valuable proxy for understanding and predicting temperature changes in distant urban centers. Using a dataset that tracks cumulative deforestation from 2001 to 2021 across 297 municipalities in the Amazon rainforest, a multivariate time series model was developed to forecast temperature trends worldwide up to 2030. The input data reveal a variety of behaviors, including complex deforestation patterns. Similarly, the forecasted temperature data showcases diverse trends. While some cities are expected to exhibit a steady temperature increase, others may experience gradual changes, while some cities may undergo drastic and rapid temperature shifts. Our findings contribute to a deeper understanding of the far-reaching impacts of deforestation on global climate patterns and underscore the importance of preserving vital ecosystems like the Amazon rainforest.

Does land tenure change accelerate deforestation? A matching-based four-country comparison

The scale and pace of land tenure change for agricultural development, often known as ‘land grabbing’ or ‘global land rush,’ are historically unprecedented. Control over millions of hectares of land has shifted over the past decade for the production of food, fiber, and biofuel. However, the rapidly growing body of research on land tenure change provides few rigorous assessments of deforestation across multiple countries with distinct biophyisical and socioeconomic characteristics. In this paper, based on spatial boundaries of land transactions (LTs) in Cambodia, Ethiopia, Liberia, and Peru, we investigate their association with deforestation using a pixel-based matching approach. Our findings suggest that the context, size, and distribution of LTs and the associated deforestation patterns vary substantially across the four countries. Land tenure change significantly accelerates deforestation in Cambodia and Liberia, but such acceleration is insignificant in Ethiopia. In Peru, we observe the opposite trend, where areas adjacent to LTs experience higher deforestation than areas within. Our analysis reveals how patterns and outcomes of LTs are linked to country characteristics and contexts, and suggests that sweeping generalizations about positive or negative consequences of LTs on deforestation and possibly other socioenvironmental outcomes need to attend more closely to specific patterns and drivers of LTs.

Spatial metrics of deforestation in Central Sulawesi, Indonesia

Climate change has become an environmental issue that has received the most attention because it affects all aspects of life. One of the factors causing climate change is deforestation which continues to occur, especially in tropical countries like Indonesia. Indonesia is a country that has a high rate of deforestation that occurs in large islands including the island of Sulawesi as the Wallacea Region. Central Sulawesi Province has had the largest area of deforestation among other provinces on Sulawesi Island in the last three decades (1990-2020). Analysis of the rate of deforestation using the Geographic Information System shows that the highest deforestation rate occurred in Palu City at 2.16% (1990-2020), even in the last decade (2010-2020) the rate of deforestation was 3.31%. After that, it was followed by Banggai Islands Regency at 0.17%, while other districts were at rates below 0.1% (1990-2020). Palu City and Banggai Islands Regency are also a concern, further information after the rate of deforestation is the pattern of deforestation which is described through three metrics, namely Clumpiness Index, Contiguity Mean Index, and Patch Density which describes fragmentation using Fragstat software. Palu City has a clustered deforestation pattern and is entirely connected to each deforested area, but it has lower fragmentation. Banggai Islands District has fairly clustered deforestation, but does not have low connectivity between deforested areas, and tends not to be fragmented. Dealing with deforestation incidents in Palu City as a whole land area, and Banggai Islands Regency as a separate land area is different.

Harvesting trees to harvest cash crops: The role of migrants in forest land conversion in Uganda

Agricultural expansion-led deforestation in Uganda is one of the highest of the world. At the same time, internal migration patterns are strongly inter-linked with agricultural dynamics in the country, as migrants are involved in crop production activities and traditionally play important roles in major crop value chains. Migration for agricultural purposes may complicate the already difficult trade-off between agricultural development and forest preservation. This article investigates how internal migration and commercial agriculture shape deforestation patterns across Uganda. Our analysis suggests that the number of cash crop producers and the number of inter-district migrants engaged in agriculture in the recipient parishes are linked to a significant increase in deforestation. We identify cash crop production as a major channel through which migrants affect deforestation. Taken together, these findings call for an improved coordination between policies on forest, agricultural development and land use. In particular, ad-hoc policies of integration are critical to support migrants in recipient areas and avoid additional pressures on natural resources.

Land cover change assessment using random forest and CA markov from remote sensing images in the protected forest of South Malang, Indonesia

Protected forest has important roles in protecting life support systems through water management, maintain micro climate stability, flood prevention, erosion control, prevention of seawater intrusion and maintenance of soil fertility. Deforestation in protected forest areas will reduce the roles, however, deforestation appeared to occur in forested areas of Bantur Regency. Infrastructure development which is growing rapidly and the compleexity of economic activities is driving deforestation in this region. The deforestation caused water supply problems, landslide and decreased endemic bird population (“rangkong”) in study areas. Therefore it is necessary to identify deforestation through land cover change assessment. This study aims at (1) quantifying the forest land cover changes in protected forest areas of Bantur and Donomulyo Regency, (2) identifying environmental condition in protected forest areas of Bantur and Donomulyo Regency. Landsat TM, ETM+ and 8 were used together with Random Forest classification for mapping the forest areas. Carbon Monoxide (CO) and temperature were used as biophysical factors in the model. The results of this study suggested the continuing deforestation with the existence of roads, economic activity, and tourism development triggered the deforestation pattern. Role of community was also found to affect the nature of deforestation.

Modeling the Impacts of Soil Management on Avoided Deforestation and REDD+ Payments in the Brazilian Amazon: A Systems Approach

An Integrated Assessment Model (IAM) was employed to develop a Narrative Policy Framework (NPF) and a quantitative model to investigate the changes in land use within the Brazilian Amazon. The process began by creating a theoretical NPF using a ‘systems thinking’ approach. Subsequently, a ‘system dynamic model’ was built based on an extensive review of the literature and on multiple quantitative datasets to simulate the impacts of the NPF, specifically focusing on the conversion of forests into open land for ranching and the implementation of soil management practices as a macro-level policy aimed at preserving soil quality and ranching yields. Various fallow scenarios were tested to simulate their effects on deforestation patterns. The results indicate that implementing fallow practices as a policy measure could reduce deforestation rates while simultaneously ensuring sustainable long-term agricultural productivity, thus diminishing the necessity to clear new forest land. Moreover, when combined with payments for avoided deforestation, such as REDD+ carbon offsets, the opportunity costs associated with ranching land can be utilized to compensate for the loss of gross income resulting from the policy. A sensitivity analysis was conducted to assess the significance of different model variables, revealing that lower cattle prices require resources for REDD+ payments, and vice-versa. The findings indicate that, at the macro level, payments between USD 2.5 and USD 5.0 per MgC ha−1 have the potential to compensate the foregone cattle production from not converting forest into ranching land. This study demonstrates that employing an IAM with a systems approach facilitates the participation of various stakeholders, including farmers and landowners, in policy discussions. It also enables the establishment of effective land use and management policies that mitigate deforestation and soil degradation, making it a robust initiative to address environmental, climate change, and economic sustainability issues.

Spatial–Temporal Dynamics of Forest Extent Change in Southwest China in the Recent 20 Years

Deforestation is thought of as a huge threat to carbon neutrality and the development of contemporary society and it has brought wide interest and attention in the science community to develop new methods to identify and quantify the occurrence and extent of forest loss. Understanding the forest-loss patterns is essential for forest management and protection. With the help of a high-spatial-resolution remote-sensing dataset on forest loss, the spatial and temporal dynamics of deforestation patterns in forests of Southwest China (SWC) have been investigated. The major findings of this study indicated that small-scale (<5 ha) deforestation raised pervasively in the region from 2001 to 2019, and the number of large patches of forest loss (>5 ha) has decreased significantly during the same period. Moreover, the mean size of forest-loss patches showed an increase from 0.34 ha to 0.61 ha over time. With the alarming trend of increasing deforestation in the southern region of our study area, the growth of emerging forest-loss hotspots was clearly observed in Chongqing and Sichuan Province. The results promoted an indepth understanding of forest-loss patterns in SWC and can help provide more coherent guidance for further forest monitoring and conservation.

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%.