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Journal articles on the topic 'Deforestation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2025

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1

Sayeed, Prof Rezavia. "Deep Learning Based Deforestation Prediction and Classification." International Journal of Innovative Research in Information Security 10, no. 04 (May 8, 2024): 261–67. http://dx.doi.org/10.26562/ijiris.2024.v1004.29.

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Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. Where, this type of detection or classification helps us to degrade the deforestations in future. In this project we are mainly focusing on the problem of deforestation, performing the classification of deforestation and the healthy forests with the help of deep learning model. CNN is the algorithm that which are been using here for the classification process. We are preparing a dataset which is trained using the algorithm and classification will be performed in testing.

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2

Pontius, Robert. "Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance." Land 7, no. 3 (September 10, 2018): 105. http://dx.doi.org/10.3390/land7030105.

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The Verified Carbon Standard (VCS) recommends the Figure of Merit (FOM) as a possible metric to confirm models that simulate deforestation baselines for Reducing Emissions from Deforestation and forest Degradation (REDD). The FOM ranges from 0% to 100%, where larger FOMs indicate more-accurate simulations. VCS requires that simulation models achieve a FOM greater than or equal to the percentage deforestation during the calibration period. This article analyses FOM’s mathematical properties and illustrates FOM’s empirical behavior by comparing various models that simulate deforestation and the resulting carbon disturbance in Bolivia during 2010–2014. The Total Operating Characteristic frames FOM’s mathematical properties as a function of the quantity and allocation of simulated deforestation. A leaf graph shows how deforestation’s quantity can be more influential than its allocation when simulating carbon disturbance. Results expose how current versions of the VCS methodologies could conceivably permit models that are less accurate than a random allocation of deforestation, while simultaneously prohibit models that are accurate concerning carbon disturbance. Conclusions give specific recommendations to improve the next version of the VCS methodology concerning three concepts: the simulated deforestation quantity, the required minimum FOM, and the simulated carbon disturbance.

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3

Garcia Da Silva, Bruno, and Pierre-Noé Milcamps. "The Regulation on Deforestation-Free Products: When the EU Takes on Deforestation’s Corrupted Roots." European Energy and Environmental Law Review 32, Issue 6 (December 1, 2023): 293–310. http://dx.doi.org/10.54648/eelr2023019.

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Traditional science has long examined the causes of deforestation and forest degradation. Yet, it has often overlooked one of its main roots: corruption. As more recent research shows the intrinsic link between corruption and deforestation, this relationship deserves to be considered by the (legal) community in and of itself. For this reason, the present contribution analyses how the EU tackles the issues of corruption and deforestation through its legislative framework. It looks at the new Deforestation-Free Products Regulation (EU) 2023/1115, adopted on 31 May 2023. In particular, this paper identifies whether the new EU regime on deforestation effectively addresses corruption in the field. This includes a critical review of the Deforestation-Free Products Regulation, and more particularly, its context of adoption, its main features, its improvements, and its structural limitations. In addition, this newRegulation should not be cloistered fromthe broader legislative landscape.Thenew proposals foraDirective onCombatingCorruptionandfora revision of the Environmental Crimes Directive have the potential to mitigate some of the identified limitations and to playarole intackling thecorruption rootsofdeforestation. European Union, Deforestation, Corruption, Deforestation-Free Regulation, Due Diligence, Environmental Crimes

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4

Hosen, Bappa. "EXPLORING THE ECOLOGICAL CONSEQUENCES OF DEFORESTATION IN TROPICAL RAINFORESTS." Environment & Ecosystem Science 7, no. 2 (2023): 112–21. http://dx.doi.org/10.26480/ees.02.2023.112.121.

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Tropical rainforests, characterized by their remarkable biodiversity and critical role in climate regulation, face unprecedented threats from deforestation. This research seeks to comprehensively explore the ecological consequences of deforestation in tropical rainforests by synthesizing existing literature and empirical studies. Our objectives encompass assessing the impacts on biodiversity, climate, and ecosystem services, while also examining conservation efforts and policy recommendations. The analysis of biodiversity impacts reveals that deforestation disrupts complex ecosystems, leading to species extinctions, altered ecological interactions, and genetic diversity loss. These effects resonate across taxonomic groups, affecting both well-known and lesser-known species. Deforestation’s relationship with climate change is a central concern. We find that tropical rainforests act as vital carbon sinks, and their degradation exacerbates global warming. Deforestation-induced changes in precipitation patterns and greenhouse gas emissions further highlight the interconnectedness of these ecosystems with climate dynamics. Ecosystem services, including water purification, pollination, and cultural values, are compromised by deforestation, impacting local communities and global society. Effective conservation strategies, such as protected areas and reforestation initiatives, offer hope, but face challenges of scale and implementation. Drawing on case studies from diverse tropical rainforest regions, we illustrate the variation in ecological consequences, emphasizing the need for context-specific solutions. Overall, It examines the causes and drivers of deforestation, the ecological functions of rainforests, and the impacts of deforestation on biodiversity, carbon cycling, climate, and local communities. The paper also discusses conservation efforts and policy implications for mitigating these consequences, this research underscores the urgent need for collective action to combat deforestation in tropical rainforests. The implications of this study inform policy recommendations, emphasizing the importance of international agreements and multi-stakeholder collaboration. Our findings highlight the imperative to protect these irreplaceable ecosystems to safeguard biodiversity, mitigate climate change, and preserve the ecosystem services they provide for present and future generations.

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5

Hamilton, David P. "Deforestation Slows." Science 251, no. 5000 (March 22, 1991): 1425. http://dx.doi.org/10.1126/science.251.5000.1425.b.

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6

Dias, B. "Targeting Deforestation." Science 343, no. 6168 (January 16, 2014): 248–49. http://dx.doi.org/10.1126/science.343.6168.248-c.

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7

Saxena, Ashok Kumar, and Jagdish C. Nautiyal. "Analyzing Deforestation." Journal of Sustainable Forestry 5, no. 3-4 (April 10, 1997): 51–80. http://dx.doi.org/10.1300/j091v05n03_04.

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8

Wood, William B. "Tropical deforestation." Global Environmental Change 1, no. 1 (December 1990): 23–41. http://dx.doi.org/10.1016/0959-3780(90)90005-t.

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9

Kwatu, A. A., A. T. Ogah, S. Y. Kpalo, M. Muhammed, and A. Umar. "Effect of Deforestation on Livelihood and the Adaptation Strategies in Niger South Senatorial District, Niger State, Nigeria." Journal of Agriculture and Ecology Research International 24, no. 3 (March 22, 2023): 27–38. http://dx.doi.org/10.9734/jaeri/2023/v24i3529.

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Tropical rainforests are ecosystem of genetic diversity that offers an important medicinal plants source, it’s also provides high-yield foods, and a myriad of other important products. They are an important environment for migratory animals and maintain as much as fifty percent of the species on globe, as well as several diverse and unique indigenous cultures. They also act an important role in controlling world weather in addition to sustaining normal rainfall, while preventing against floods, deforestations, and erosion. The study’s aim is to analyse the effect of deforestation on livelihood and the adaptation strategies in Niger South Senatorial District, Niger state, Nigeria. Information about the various effects of deforestation and adaptation strategies was collected using a structured questionnaire and Focus Group Discussion (FGD). The result shows that wind storms, flooding, late rainfall; low output yields, and wide spreads of pest infestation were ranked as the major effects of deforestation. Different survival strategies are been used by the respondents comprises of fishing, animal rearing, poultry production, hiring labour and use of fertilizer to maximize production. It was acceded by the more than half of the discussants at FGD that artisan work and small- scale businesses are the major other sources of livelihood used by the individuals. In their various submissions, they all attest that wind storms and flooding are the most common problem of deforestation noticeable in the study area.

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10

Delacote, Philippe, Elizabeth J. Z. Robinson, and Sébastien Roussel. "Deforestation, leakage and avoided deforestation policies: A spatial analysis." Resource and Energy Economics 45 (August 2016): 192–210. http://dx.doi.org/10.1016/j.reseneeco.2016.06.006.

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11

Lewellen, Ted C. "Tropical Deforestation: The Human Dimension:Tropical Deforestation: The Human Dimension." American Anthropologist 99, no. 3 (September 1997): 643. http://dx.doi.org/10.1525/aa.1997.99.3.643.

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12

Hultera, Hultera, Lilik Budi Prasetyo, and Yudi Setiawan. "Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District." Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) 10, no. 2 (July 3, 2020): 294–306. http://dx.doi.org/10.29244/jpsl.10.2.294-306.

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Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha.

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13

RUDEL, THOMAS K. "Have tropical deforestation's changing dynamics created conservation opportunities? A historical analysis." Environmental Conservation 42, no. 2 (August 27, 2014): 108–18. http://dx.doi.org/10.1017/s0376892914000228.

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SUMMARYDuring the past century, humans converted extensive areas of tropical forest into cultivated lands. Three distinct processes, each predominant during a different historical period, have driven the destruction of the forests. This review describes each of these deforestation dynamics: natural resource degrading poverty traps that predominated during the colonial era, new land settlement schemes that prevailed for two decades after decolonization, and finally, financialized, large enterprise dynamics that have predominated during the past quarter century. Each dynamic has, over time, given rise to different opportunities for conservation. Peasants emigrated from the sites of the poverty traps, and regrowth began to cover these degraded landscapes. Smallholders in the new land settlement areas became better acquainted with tropical tree species and allowed some trees to recolonize their fields, creating silvopastoral and agroforested landscapes. The heads of large enterprises relied on credit to clear land, so government regulators found that they could curb corporate-led deforestation by restricting access to credit when landowners failed to comply with laws against forest clearing. These links between deforestation's dynamics during past eras and conservation policies during the present era illustrate how a historical understanding of tropical deforestation can provide the basis for effective conservation policies.

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14

Sarney Filho, José. "Cutting down deforestation." Our Planet 2017, no. 1 (March 14, 2018): 12–13. http://dx.doi.org/10.18356/043d4757-en.

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15

Swingland, Ian R., and Alan Grainger. "Controlling Tropical Deforestation." Geographical Journal 161, no. 1 (March 1995): 99. http://dx.doi.org/10.2307/3059951.

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16

Bienen, Leslie. "Deforestation and Disease." Frontiers in Ecology and the Environment 2, no. 7 (September 2004): 340. http://dx.doi.org/10.2307/3868345.

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17

Abel, N., and A. C. Hamilton. "Deforestation in Uganda." Journal of Ecology 74, no. 1 (March 1986): 304. http://dx.doi.org/10.2307/2260371.

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18

Seymour, Frances, and Nancy L. Harris. "Reducing tropical deforestation." Science 365, no. 6455 (August 22, 2019): 756–57. http://dx.doi.org/10.1126/science.aax8546.

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19

Kaimowitz, David. "Amazon Deforestation Revisited." Latin American Research Review 37, no. 2 (2002): 221–35. http://dx.doi.org/10.1017/s0023879100019592.

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20

Ingram, Gordon Brent, and Rodolphe de Koninck. "Deforestation in Vietnam." Pacific Affairs 73, no. 1 (2000): 145. http://dx.doi.org/10.2307/2672318.

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21

Scarrow, Ryan. "Corruption drives deforestation." Nature Plants 3, no. 12 (November 20, 2017): 910. http://dx.doi.org/10.1038/s41477-017-0075-8.

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22

Laurance, W. F. "Deforestation in Amazonia." Science 304, no. 5674 (May 21, 2004): 1109b—1111b. http://dx.doi.org/10.1126/science.304.5674.1109b.

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23

Stairs, David C. "Design and Deforestation." Leonardo 32, no. 4 (August 1999): 273–79. http://dx.doi.org/10.1162/002409499553424.

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Throughout their history, Americans have utilized technology to convert wilderness to civilization. Much of this development has been historically described as “progressive.” The author examines one example of this—the reduction of the Michigan pineries in the nineteenth century—in detail in an effort to reconcile design advances with our changing perceptions of wilderness. The author also discusses the development of tools and design techniques together with the evolution of the environmental movement.

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24

Sugden, Andrew M. "Degradation exceeds deforestation." Science 369, no. 6509 (September 10, 2020): 1335.7–1336. http://dx.doi.org/10.1126/science.369.6509.1335-g.

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25

Monastersky, Richard. "The Deforestation Debate." Science News 144, no. 2 (July 10, 1993): 26. http://dx.doi.org/10.2307/3977523.

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26

Scarrow, Ryan. "Frontiers and deforestation." Nature Plants 5, no. 2 (February 2019): 124. http://dx.doi.org/10.1038/s41477-019-0371-6.

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27

Islam, Sheikh Tawhidul. "Deforestation in Bangladesh." Power and Narrative 17, no. 1 (October 30, 2007): 141–56. http://dx.doi.org/10.1075/ni.17.1.10isl.

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This article considers the narratives of power acting upon the Madhapur forests of Bangladesh. Power is unequally distributed amongst the actors involved: the native people, the national government, international agencies. This article will first consider this issue in the context of political ecology, and then discuss the resources within the forest and the settlement of the local forest dwellers. It will demonstrate that the power within narratives have been effectively utilised as weapons by the more powerful groups against the vulnerable to seize their lands, livelihoods and homes, and to mobilise planning development and its subsequent deforestation, with relative ease and even impunity.

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28

Anonymous. "How much deforestation?" Eos, Transactions American Geophysical Union 71, no. 3 (1990): 201. http://dx.doi.org/10.1029/eo071i003p00201-04.

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29

Brown, Alastair. "Deforestation changes rainfall." Nature Climate Change 1, no. 8 (October 27, 2011): 393. http://dx.doi.org/10.1038/nclimate1277.

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30

Contestabile, Monica. "Cost of deforestation." Nature Climate Change 1, no. 8 (October 27, 2011): 393. http://dx.doi.org/10.1038/nclimate1283.

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31

Brown, Alastair. "World without deforestation." Nature Climate Change 6, no. 6 (May 25, 2016): 541. http://dx.doi.org/10.1038/nclimate3047.

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32

Apan, Armando A., and James A. Peterson. "Probing tropical deforestation." Applied Geography 18, no. 2 (April 1998): 137–52. http://dx.doi.org/10.1016/s0143-6228(98)00004-6.

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33

Camara, G. "Amazonian Deforestation Models." Science 307, no. 5712 (February 18, 2005): 1043c—1044c. http://dx.doi.org/10.1126/science.307.5712.1043c.

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34

Clark, Colin. "Deforestation and Floods." Environmental Conservation 14, no. 1 (1987): 67–69. http://dx.doi.org/10.1017/s0376892900011127.

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35

Vaughan, Adam. "Deforestation for rubber." New Scientist 245, no. 3266 (January 2020): 10. http://dx.doi.org/10.1016/s0262-4079(20)30143-3.

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36

Hughes, J. Donald. "Ancient Deforestation Revisited." Journal of the History of Biology 44, no. 1 (July 29, 2010): 43–57. http://dx.doi.org/10.1007/s10739-010-9247-3.

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37

Plumptre, R. A. "Deforestation in Uganda." Biological Conservation 43, no. 1 (1988): 77–78. http://dx.doi.org/10.1016/0006-3207(88)90079-1.

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38

Culas, Richard J. "Debt and Deforestation." Journal of Developing Societies 22, no. 4 (December 2006): 347–58. http://dx.doi.org/10.1177/0169796x06071524.

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39

Goodland, Robert J. A. "Alternatives to deforestation." Trends in Ecology & Evolution 8, no. 2 (February 1993): 72–73. http://dx.doi.org/10.1016/0169-5347(93)90165-l.

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40

Busch, Jonah, Oyut Amarjargal, Farzad Taheripour, Kemen G. Austin, Rizki Nauli Siregar, Kellee Koenig, and Thomas W. Hertel. "Effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia." Environmental Research Letters 17, no. 1 (January 1, 2022): 014035. http://dx.doi.org/10.1088/1748-9326/ac435e.

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Abstract Demand-side restrictions on high-deforestation commodities are expanding as a climate policy, but their impact on reducing tropical deforestation and emissions has yet to be quantified. Here we model the effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia. We do so by integrating a model of global trade with a spatially explicit model of land-use change in Indonesia. We estimate a European ban on high-deforestation palm oil from 2000 to 2015 would have led to a 8.9% global price premium on low-deforestation palm oil, resulting in 21 374 ha yr−1 (1.60%) less deforestation and 21.1 million tCO2 yr−1 (1.91%) less emissions from deforestation in Indonesia relative to what occurred. A hypothetical Indonesia-wide carbon price would have achieved equivalent emission reductions at $0.81/tCO2. Impacts of a ban are small because: 52% of Europe’s imports of high-deforestation palm oil would have shifted to non-participating countries; the price elasticity of supply of high-deforestation oil palm cropland is small (0.13); and conversion to oil palm was responsible for only 32% of deforestation in Indonesia. If demand-side restrictions succeed in substantially reducing deforestation, it is likely to be through non-price pathways.

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41

Zhu, Shanyou, Hailong Zhang, Ronggao Liu, Yun Cao, and Guixin Zhang. "Comparison of Sampling Designs for Estimating Deforestation from Landsat TM and MODIS Imagery: A Case Study in Mato Grosso, Brazil." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/919456.

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Sampling designs are commonly used to estimate deforestation over large areas, but comparisons between different sampling strategies are required. Using PRODES deforestation data as a reference, deforestation in the state of Mato Grosso in Brazil from 2005 to 2006 is evaluated using Landsat imagery and a nearly synchronous MODIS dataset. The MODIS-derived deforestation is used to assist in sampling and extrapolation. Three sampling designs are compared according to the estimated deforestation of the entire study area based on simple extrapolation and linear regression models. The results show that stratified sampling for strata construction and sample allocation using the MODIS-derived deforestation hotspots provided more precise estimations than simple random and systematic sampling. Moreover, the relationship between the MODIS-derived and TM-derived deforestation provides a precise estimate of the total deforestation area as well as the distribution of deforestation in each block.

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42

Castro-Nunez, Augusto Carlos, Ma Eliza J. Villarino, Vincent Bax, Raphael Ganzenmüller, and Wendy Francesconi. "Broadening the Perspective of Zero-Deforestation Interventions in Peru by Incorporating Concepts from the Global Value Chain Literature." Sustainability 13, no. 21 (November 3, 2021): 12138. http://dx.doi.org/10.3390/su132112138.

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Global narratives around the links between deforestation and agricultural commodity production have led to the application of voluntary zero-deforestation agreements between companies, governments, and civil society. The continued tropical deforestation warrants a re-examination of this approach in order to customize its application for a particular location. Our paper contributes to this by exploring the spatial associations between deforestation and the production of cacao, coffee, and oil palm in the Amazon region in Peru. The geographical overlaps between deforestation, and the distribution of these commodity crops, indicate four types of spatial associations: (1) a high degree of deforestation and a high degree of commodity production (high-high); (2) a high degree of deforestation and a low degree of commodity production (high-low); (3) a low degree of deforestation and a high degree of commodity production (low-high); and (4) a low degree of deforestation and a low degree of commodity production (low-low). On the basis of these associations, we present four scenarios in which zero-deforestation supply chain interventions may operate in Peru and argue that broadening the perspective of such interventions by adopting a global value chain lens can improve the use of previously deforested lands, prevent unintended or future deforestation and, in turn, ensure that no forest area is left behind.

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43

Yazid, Muhammad. "Apakah pendapatan utama dari perkebunan mempengaruhi deforestasi?: Studi dari tingkat desa di Kalimantan, Indonesia." Jurnal Penelitian Sosial dan Ekonomi Kehutanan 19, no. 1 (September 20, 2022): 1–14. http://dx.doi.org/10.20886/jpsek.2022.19.1.1-14.

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The complexity of the drivers of deforestation in Indonesia requires various research on deforestation, especially to support policies related to reducing deforestation. The economy is one aspect that contributes to providing an overview of deforestation. This study investigates the relationship between deforestation and village with dominant income from plantation as main commodity. A panel data analysis using data from 3260 villages in Kalimantan in 2011, 2014, and 2018 was analyzed to determine whether plantation as main income affected deforestation. The dependent variable is deforestation in each village. There are 14 independent variables used, with the main variable is villages with main income from plantation as a dummy variable. The results of random effect model show that villages with plantation as main commodity have a positive impact on deforestation. Ten independent variables show a positive relationship with deforestation. Four independent variables show a negative relationship with deforestation, which include the use of firewood, practice of burning land, non-wood small industries, and logging companies (PBPH Hutan Alam). The recommendation of this research is the need to replanting the degraded and non-productive land for the wood plantation areas, thus the conversion of forest into non-forest area can be avoided.

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44

Sboui, Tarek, Salwa Saidi, and Ahmed Lakti. "A Machine-Learning-Based Approach to Predict Deforestation Related to Oil Palm: Conceptual Framework and Experimental Evaluation." Applied Sciences 13, no. 3 (January 30, 2023): 1772. http://dx.doi.org/10.3390/app13031772.

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Deforestation is recognized as an issue that has negative effects on the ecosystem. Predicting deforestation and defining the causes of deforestation is an important process that could help monitor and prevent deforestation. Deforestation prediction has been boosted by recent advances in geospatial technologies and applications, especially remote sensing technologies and machine learning techniques. This paper highlights the issue of predicting deforestation related to oil palm, which has not been focused on in existing research studies. The paper proposes an approach that aims to enhance the prediction of deforestation related to oil palm plantations and palm oil production. The proposed approach is based on a conceptual framework and an assessment of a set of criteria related to such deforestation. The criteria are assessed and validated based on a sensitivity analysis. The framework is based on machine learning and image processing techniques. It consists of three main steps, which are data preparation, model training, and validation. The framework is implemented in a case study in the Aceh province of Indonesia to show the feasibility of our proposed approach in predicting deforestation related to oil palm. The implementation of the proposed approach shows an acceptable accuracy for predicting deforestation.

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45

Weatherley-Singh, Janice, and Aarti Gupta. "“Embodied Deforestation” as a New EU Policy Debate to Tackle Tropical Forest Loss: Assessing Implications for REDD+ Performance." Forests 9, no. 12 (December 1, 2018): 751. http://dx.doi.org/10.3390/f9120751.

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The need to tackle international drivers of deforestation has long been acknowledged; but remains little addressed via policy measures. In the European Union (EU), a new policy debate is emerging around the concept of “embodied deforestation”, which targets EU agricultural commodity imports as drivers of deforestation. The notion views deforestation as an externality generated by EU imports associated with tropical deforestation. Our article examines whether this concept represents a shift in tackling international-level drivers of tropical deforestation within EU policy. We also examine, from a networked governance perspective, whether this new debate fuels further fragmentation or rather a move towards a more integrated approach to combating tropical forest loss within EU policy, and what the implications are for other initiatives, such as the climate change related “reducing emissions from deforestation and forest degradation” (REDD+). Our analysis draws on an extensive analysis of EU policy documents and semi-structured interviews with stakeholders and EU decision-makers. We find that, despite growing debate around the concept of embodied deforestation, policy measures necessary to reduce the impact of EU consumption of agricultural commodities associated with tropical deforestation have not yet been developed. We conclude that “embodied deforestation” remains more an idea than reality within EU policy to date, with the burden of responsibility for addressing international deforestation drivers still largely remaining on developing countries. There is still potential, however, for this debate to lead to a more integrated approach to tackling tropical deforestation within EU policy, if it comes to be seen, together with REDD+, as one of a number of linked approaches to EU efforts to combat deforestation.

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46

Rijal, Syamsu, Ismiah Mutmainnah, Munajat Nursaputra, and A. Chairil. "Deforestation Vulnerability Based Administrative Boundary and Forest Area in Nusa Tenggara, Indonesia." IOP Conference Series: Earth and Environmental Science 1277, no. 1 (December 1, 2023): 012018. http://dx.doi.org/10.1088/1755-1315/1277/1/012018.

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Abstract Deforestation is a change in forest cover into other types of cover which is a global problem. Deforestation that occurs varies in each region. Deforestation hazard is a better behavioral conclusion or profile information about deforestation that occurs because it displays three aspects of assessment, namely the Percentage of Forest Area (PFA), the proportion of deforestation events (PDE) and the rate of deforestation (RD). Analysis of deforestation profiles in Nusa Tenggara, which consists of two provinces from 1990 to 2020. This study uses overlapping data on land cover, district administration, and Area functions for forest-non-forest analysis. Analysis of deforestation profiles based on district administration produces 15 forms of deforestation profiles. The largest profile in West Nusa Tenggara Province is the 3-1-1 profile (large percentage of low-rate events), which is a non-vulnerable category. The highest profile in East Nusa Tenggara Province is 1-3-1 (a small percentage of late events) and is a very vulnerable category. Dominant deforestation occurs outside forest areas, but if viewed from a profile perspective, all functions of the area are classified as very vulnerable in East Nusa Tenggara Province, and conservation forest areas in West Nusa Tenggara Province. This happened because the largest deforestation occurred in the end period in each function of the forest area. This is a warning for the management of forest areas in Nusa Tenggara.

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Hasan, Rakibul, Syeda Farjana Farabi, Md Kamruzzaman, Md Khokan BHUYAN, Sadia Islam Nilima, and Atia Shahana. "AI-Driven Strategies for Reducing Deforestation." American Journal of Engineering and Technology 6, no. 6 (June 1, 2024): 6–20. http://dx.doi.org/10.37547/tajet/volume06issue06-02.

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Recent advancements in data science, coupled with the revolution in digital and satellite technology, have catalyzed the potential for artificial intelligence (AI) applications in forestry and wildlife sectors. Recognizing the critical importance of addressing land degradation and promoting regeneration for climate regulation, ecosystem services, and population well-being, there is a pressing need for effective land use planning and interventions. Traditional regression approaches often fail to capture underlying drivers' complexity and nonlinearity. In response, this research investigates the efficacy of AI in monitoring, predicting, and managing deforestation and forest degradation compared to conventional methods, with a goal to bolster global forest conservation endeavors. Employing a fusion of satellite imagery analysis and machine learning algorithms, such as convolutional neural networks and predictive modelling, the study focuses on key forest regions, including the Amazon Basin, Central Africa, and Southeast Asia. Through the utilization of these AI-driven strategies, critical deforestation hotspots have been successfully identified with an accuracy surpassing 85%, markedly higher than traditional methods. This breakthrough underscores the transformative potential of AI in enhancing the precision and efficiency of forest conservation measures, offering a formidable tool for combating deforestation and degradation on a global scale.

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Rijal, Syamsu, Roland A. Barkey, Nasri, and Munajat Nursaputra. "Profile, Level of Vulnerability and Spatial Pattern of Deforestation in Sulawesi Period of 1990 to 2018." Forests 10, no. 2 (February 20, 2019): 191. http://dx.doi.org/10.3390/f10020191.

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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.

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Furtado Lima, Cássio, Fillipe Tamiozzo Pereira Torres, Luciano José Minette, Fernanda Araujo Lima, Roldão Carlos Andrade Lima, Michel Keisuke Sato, Arthur Araújo Silva, Bruno Leão Said Schettini, Francisco de Assis Costa Ferreira, and Mateus Xavier Lima Machado. "Is there a relationship between forest fires and deforestation in the Brazilian Amazon?" PLOS ONE 19, no. 6 (June 28, 2024): e0306238. http://dx.doi.org/10.1371/journal.pone.0306238.

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The Brazilian Legal Amazon is an extensive territory in which different factors influence the dynamics of forest fires. Currently, the Brazilian government has two tools in the public domain and free of charge, PRODES and BDQueimadas, to monitor and make decisions to combat deforestation and forest fires. This work aimed to evaluate and correlate the forest fire alerts and deforestation in the Amazon Forest in the state of Pará. The analyses were based on carrying out a diagnosis of forest fires and deforestation; the behavior of forest fires and deforestation over the last twenty years; the statistical relationship between deforestation and forest fires and their spatialization. This work identified that Pará is the state in the Legal Amazon with the highest occurrence of forest fires and deforestation. Deforestation in the four-year period Jan/2003-Dec/2006 showed a higher rate compared to the four-year periods Jan/2011-Dec/2018. A high correlation was found between forest fire alerts and increases in deforestation. There is a spatial relationship between cities with greater increases in deforestation and high numbers of fire alerts. In relation to the occurrence of forest fires and deforestation, the south of the state was the most critical region and the north had lower rates.

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Mammadova, Aynur, Jelle Behagel, Mauro Masiero, and Davide Pettenella. "Deforestation as a Systemic Risk: The Case of Brazilian Bovine Leather." Forests 13, no. 2 (February 3, 2022): 233. http://dx.doi.org/10.3390/f13020233.

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Tropical deforestation and forest degradation driven by agricultural commodity production remains one of the important sustainability challenges of our times. The responses to tropical deforestation so far have not managed to reverse global trends of forest loss, reigniting the discussion about more robust and systemic measures. The concept of deforestation risk is highly relevant for current debates about policy and trade, and likely to increase in importance in the context of the proposed EU Regulation on Deforestation-free Products and EU-Mercosur Trade Agreement. We argue that deforestation is a systemic risk that permeates through different economic sectors, including production, manufacturing, service and control sectors. International trade, investment and economic policies thus act as a systemic trap that cause the production sector to continue with nature’s destruction. This article seeks to more clearly define deforestation risk and uses the case of bovine leather from Brazil to illustrate how pressures for deforestation accumulate across economic sectors towards production, while deforestation risk is dispersed in an opposite trajectory. The article draws on multiple datasets and an extensive literature review. Included are quantitative data sources on annual slaughter, bovine hide/leather registry and annual deforestation, slaughterhouse and tannery locations. We argue that the EU banning unsustainable products from entry and putting incentives for more sustainable agricultural production in the tropics addresses deforestation risks that are currently visible and relatively easy to identify. These response mechanisms are conditioned upon traceability of deforestation risk across supply chains, which is prone to falsifications, leakage and laundry. Although proven to be essential, the proposed EU responses still miss out deeper leverage points to address the systemic drivers of deforestation coming from the manufacturing, service and control sectors that make production through deforestation profitable in the first place.

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