Using Remote Sensing for Sustainable Forest Management in Developing Countries

Nandasena, W. D. K. V.; Brabyn, Lars; Serrao‐Neumann, Silvia

doi:10.1007/978-3-030-38948-2_35-1

Cited by 5 publications

(3 citation statements)

References 66 publications

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“…Vegetation remote sensing classification and biomass estimation play a vital role in the development of forest fire prevention projects and the sustainable management of forest resources [48]. This study focuses on conducting comprehensive research on vegetation remote sensing classification and biomass inversion in the Chongli District of Zhangjiakou City.…”

Section: Discussionmentioning

confidence: 99%

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Xu,

Chen,

Xie

et al. 2024

Fire

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Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires.

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Section: Discussionmentioning

confidence: 99%

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Xu,

Chen,

Xie

et al. 2024

Fire

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“…However, given the limited availability of financial and human resources, monitoring agricultural encroachment in forests and evaluating the effect of forest policies pose significant challenges [13][14][15]. To overcome the challenges associated with the need for substantial investment of manpower and money for effective monitoring, remote sensing has been proposed as a cost-and time-effective tool for sustainable forest management [16]. Blackman (2013) also suggested remote sensing as a low-cost method to conduct ex post analysis of forest policies [12].…”

Section: Introductionmentioning

confidence: 99%

Policy Evaluation and Monitoring of Agricultural Expansion in Forests in Myanmar: An Integrated Approach of Remote Sensing Techniques and Social Surveys

San,

Kumar,

Biber-Freudenberger

et al. 2024

Land

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Agricultural expansion is the main driver of deforestation in Myanmar. We analyzed the effectiveness of a national policy intervention on agricultural encroachment in state forests in Taungoo District in Myanmar from 2010 to 2020. The policy aims to stop agricultural encroachment and reforest encroached areas through farmers’ participation in an agroforestry community forestry. We applied an integrated approach that involved a land cover change analysis together with a household survey about encroachment behavior. The remote sensing analysis for the years 2010, 2015 and 2020 showed the land cover change pattern and an increase in agricultural encroachment from 9.5% to 18.5%, while forests declined from 62.8% to 51.9%. The survey showed that most farmers (91%) believed that the policy intervention did not lead to a change in their encroachment behavior or farm size. The main reasons that incentivized encroachment were stated to be livelihood needs, immigration due to marriage and increased accessibility due to road construction. The main reason for reducing encroachment was plantation establishment, leading to a loss of land for encroaching farmers. In conclusion, the integrated approach showed that the policy intervention did not decrease encroachment, whereas other factors influenced encroachment behavior. We recommend solving interministerial conflicts of interest related to encroachment in Myanmar and using an integrated approach for future studies.

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“…These indices are particularly valuable for identifying trends in deforestation, reforestation, and ecological changes over time. This helps to gain a deeper understanding of the causes of forest dynamics, such as human interventions, climate change, or natural variations (Nandasena et al, 2022;Ojeh et al, 2022;Alarcón Borges et al, 2023;Darem et al, 2023). The application of remote sensing and GIS in assessing forest dynamics provides detailed and reliable information, enabling more effective forest management and promoting the conservation of precious natural resources (Al-Ghanmi and Al-Jabri, 2019; Kundu et al, 2020;Wang et al, 2022;Fontgalland et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Assessing forest changes in Yok Don National Park and surrounding areas, Dak Lak province, Vietnam

Duong,

Son

2024

J. Degrade. Min. Land Manage.

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Forests serve as vital ecosystems, playing a pivotal role in preserving biodiversity, maintaining air and water quality, and regulating the climate, all while delivering many benefits to humanity and serving as a critical resource for diverse industries. This study investigated changes in forest cover within Yok Don National Park and surrounding areas in Dak Lak province, Vietnam, over the period from 2001 to 2021. The primary objective was to identify decadal shifts in forest cover through the utilization of freely accessible satellite remote sensing data and geographic information systems (GIS) tools. The maximum likelihood classification technique was employed to categorize Landsat images captured in 2001, 2011, and 2021. Furthermore, we evaluated the efficacy of the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) based classifications in detecting forest cover changes when compared to supervised classification. Initial estimates placed forest cover at 2796.91 km2, constituting 88.44% of the total land area in 2001, which subsequently declined to 2609.97 km2 (82.53%) in 2011, and further reduced to 1843.57 km2 (58.29%) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging and illegal production land. Notably, the NDVI outperformed the SAVI in detecting forest cover changes. The findings of this research are anticipated to contribute to ongoing studies and enhance the effective monitoring of this protected forest area, thus providing invaluable insights for decision-makers.

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Using Remote Sensing for Sustainable Forest Management in Developing Countries

Cited by 5 publications

References 66 publications

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Policy Evaluation and Monitoring of Agricultural Expansion in Forests in Myanmar: An Integrated Approach of Remote Sensing Techniques and Social Surveys

Assessing forest changes in Yok Don National Park and surrounding areas, Dak Lak province, Vietnam

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