Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin

Hufkens, Koen; Haulleville, Thalès de; Jacobsen, Kim; Beeckman, Hans; Stoffelen, Piet; Vandelook, Filip; Meeus, Sofie; Amara, Michael; Hirtum, Leen Van; Bulcke, Jan Van den; Verbeeck, Hans; Wingate, Lisa

doi:10.3390/rs12040638

Cited by 14 publications

(6 citation statements)

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“…These findings match those found by Yila and Resurreccion (2014) in Nigeria and Tesfahun and Chawla (2019) in Ethiopia. However, slash-and-burn agriculture as a cause of climate change has been cited at local (Hufkens et al , 2020), national (MECNT, 2012; Molinario et al , 2015) and international levels (Pan et al , 2011; Arneth et al , 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, despite the fact that agriculture is considered as the most vulnerable sector to climate change at both local and national levels (MECNT and UNDP, 2009; Ulimwengu and Kibonge, 2016); several studies have shown that it is a major source of greenhouse gas (GHG) emissions (Pan et al , 2011; Arneth et al , 2019), through the process of deforestation (Potapov et al , 2012; Hufkens et al , 2020), thus contributing to climate change (Kipalu and Mukungu, 2012; AMCEN, 2014). Slash-and-burn agriculture is the main direct cause of deforestation in DRC (MECNT, 2012; Molinario et al , 2015), particularly in the Yangambi landscape (Hufkens et al , 2020). The search for fertile land pushes the population to clear increasingly more forest areas to meet their ever-increasing needs and to cope with greater demographic pressure (Alongo et al , 2013; Kyale et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

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Building a framework towards climate-smart agriculture in the Yangambi landscape, Democratic Republic of Congo (DRC)

Mangaza

Sonwa²,

Batsi

et al. 2021

IJCCSM

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Purpose This paper aims to produce a framework for climate-smart agriculture (CSA) in the Yangambi landscape, Democratic Republic of the Congo (DRC). This would enable the authors to identify agricultural practices, assess vulnerability to climate change, identify options for improving agricultural systems from a climate change mitigation and adaptation perspective and finally provide climate-smart agricultural options. Design/methodology/approach The study used household survey methods of data collection. The data were collected using a structured questionnaire survey by interviewing 250 farm households, subdivided using three axes of the Yangambi landscape. Fisher’s exact test was used to determine relationships between two or more variables. Findings Results of the survey revealed that the vast majority (98%) of respondents perceived changes in temperature, rainfall and weather patterns. Reduction of crop yields and the emergence of new weed species and new crop pests are the main impacts on agricultural activities. Although 87.6% of respondents have no means of adaptation and resilience, some of them use crops rotation, fallow practice, fertilizers and bio-pesticides. A framework for CSA is proposed for the Yangambi landscape. Practical implications Policies and strategies to promote CSA in the study area should take into account local farmers' perceptions of climate change and consider first the adequacy of CSA practices for the specific conditions of the target area before its promotion. This study is thus useful for many REDD+ initiatives that are currently being promoted in DRC and particularly in the Tshopo Province. Originality/value This study is one of the first studies to focus on CSA in the Yangambi landscape, DRC. It assists the use of agriculture as a response to reducing deforestation while at the same time lowering agriculture’s carbon footprint and promoting a resilient and more productive farming system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building a framework towards climate-smart agriculture in the Yangambi landscape, Democratic Republic of Congo (DRC)

Mangaza

Sonwa²,

Batsi

et al. 2021

IJCCSM

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“…well as the trafficking of ivory and skins (van Vliet al, 2018). Annual deforestation rates on primary forests subsequently increased after about 2010 (Hufkens et al, 2020). These impacts mostly reflect a growing human population as the post-conflict period provided greater stability for people looking to invest in agriculture.…”

Section: Figurementioning

confidence: 99%

Uneven transmission of traditional knowledge and skills in a changing wildmeat system: Yangambi, Democratic Republic of Congo

Shephard,

Muhindo,

Nyumu

et al. 2023

Front. Conserv. Sci.

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IntroductionIndigenous communities typically hold diverse traditional ecological knowledge (TEK) of their social-ecological system. Much of this knowledge is embodied as skills related to subsistence practices within a specific landscape and is associated with community values and norms. Ways of knowing often reflect the different activities traditionally undertaken by men and women. The incursion of external forces, including urbanization, the cash economy and migration tends to diminish transmission of traditional embodied skills. Knowledge can be lost as culturally significant environments degrade or species become extirpated. Lack of opportunity to develop traditional knowledge and skills can diminish feelings of place and identity, and thus capacity for local environmental stewardship.MethodsThe Yangambi region, Democratic Republic of Congo is a hunting territory of the Turumbu ethnic group. We used questionnaires to explore how levels of wildmeat knowledge and skill may have changed over time among the Turumbu.ResultsThe responses showed lower levels of self-reported skill among women who started to participate in the last 10-15 years. This pattern partly reflects the period of ‘apprenticeship’ but may also suggest diminished learning opportunity in recent years. Skills in cooking, smoking, and selling wildmeat persisted at a higher level than skills in curing disease and gathering wild produce. There was a much more marked pattern for men, with diminishing levels of wildmeat skill reported for around 35-40 years, and even earlier for knowledge of traditional medicine and wildmeat taboos. Questions about mentoring suggested that women have maintained knowledge pathways between mother and daughter, while men showed a shift toward increased learning from uncles.DiscussionGender differences in sharing and learning TEK may be linked to the type of skills that remain valuable in a changing social, ecological, and economic context. Men traditionally undertake the capture elements of hunting, while women deal with wildmeat processing, marketing, and cooking. The Yangambi wildmeat system has evolved from subsistence to a strongly market-driven economy during the lifetime of our study participants. This shift may partly explain why market-based kills such as food smoking and selling have endured longer than hunter’s nature-based knowledge.

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“…At the same time, deforestation and the unbalanced functioning of forest ecosystems have wider effects and environmental consequences including land degradation, desertification, the loss of wildlife habitat and soil erosion [34]. Analysis of deforestation is possible by comparing forest areas on older maps with those detected on the remote sensing data [35][36][37] or landscape dynamics evaluated by time series of images [38]. Other approaches include continuous metrics to measure the degree of forest degradation [39] While the techniques of the remote sensing data acquisition and image processing by Geographic Information System (GIS) have demonstrated advances over recent decades [40,41], the algorithms of geospatial data modelling are not as straightforward regarding handling the data automatically.…”

Section: Introductionmentioning

confidence: 99%

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

Lemenkova

Debeir

2022

Applied Sciences

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In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

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Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin

Cited by 14 publications

References 60 publications

Building a framework towards climate-smart agriculture in the Yangambi landscape, Democratic Republic of Congo (DRC)

Building a framework towards climate-smart agriculture in the Yangambi landscape, Democratic Republic of Congo (DRC)

Uneven transmission of traditional knowledge and skills in a changing wildmeat system: Yangambi, Democratic Republic of Congo

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

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