Land Fires Research Articles

Peran BPBD dalam Penanganan Bencana Angin Kencang di Kecamatan Mungkid Kabupaten Magelang

Disaster potentials that must be watched out for in Magelang Regency include Mount Merapi Eruptions, Flash Floods and Rain Lava, Landslides, Forest and Land Fires, and Strong Winds. Therefore, in reducing the impact and providing services, the Regional Disaster Management Agency (BPBD) of Magelang Regency has the function of coordinator, command and implementer in disaster management from the pre-disaster, emergency response, and post-disaster stages. This study aims to describe how BPBD Magelang Regency deals with disasters, especially strong winds. This study uses a qualitative method with a descriptive approach by collecting data through interviews and observations with informants from affected residents and the BPBD. The results of this study are that the BPBD of Magelang Regency carries out disaster management activities starting from the pre- disaster, emergency response and post-disaster stages in strong wind disasters. Then to make it easier for the public to access information about disasters, BPBD Magelang Regency has created an application called Sistem Informasi Kebencanaan Kabupaten Magelang (SIKK).

Rancang Bangun Sistem Pendeteksi Kebakaran Hutan Menggunakan Drone Berbasis Computer Vision

The danger of forest and land fires in Indonesia is a serious threat that continues to recurevery year. According to U.S. data Fire Service, there are more than 700 forest fires everyyear burning more than 7 million hectares of land. These fires not only damage ecosystems,but also cause the loss of important biodiversity. Many of Indonesia's endemic flora andfauna have fallen victim, increasing the risk of extinction. The use of UAVs can also changedisaster management. This technology can be integrated with early warning systems basedon analytical data and artificial intelligence, increasing the accuracy of fire predictions andmore proactive responses. Thus, the application of this technology is not just a temporarysolution, but a long-term strategic step to maintain environmental sustainability andcommunity welfare. Technological solutions such as UAVs and cooperation betweenvarious parties are needed to overcome this challenge effectively. The aim of this researchis to create a computer vision-based system that uses deep learning algorithms, especiallyYou Only Look Once (YOLO), to detect forest fires quickly and accurately. In mitigating forest fire disasters, early detection of hotspots is an important step to prevent fires fromspreading to other areas and reduce the damage caused by fires to humans and theenvironment. To achieve the objectives of this study, several actions were taken. First,forest fire image data is collected and processed for use in training the YOLO model. Next,the model is trained using a dataset that covers various forest fire conditions, such as fireintensity, time of day, and weather conditions, to ensure that the model can find firehotspots in various conditions.

Spatial-Temporal Analysis of Land Cover Change and Oil Palm Expansion in Gunung Mas

Abstract: The area of oil palm plantations in Indonesia has increased over time. The growth of oil palm plantations even exceeds the percentage of the area of paddy fields, especially in Gunung Mas Regency. This phenomenon is accompanied by the implementation of "Regional Regulation No. 1 of 2020 concerning Land Fire Control". The regulation aims to prevent land fires due to human activities. However, local people believe that burning can increase land productivity. As a result, many people convert their land to plant oil palm, because it is considered to guarantee economic improvement compared to paddy fields. Remote sensing satellite imagery is the basis and main source in the analysis. With spatio-temporal analysis, mapping of land use changes in a certain period of time and its relationship with the distribution of agricultural wet and dry areas. As a result, the proportion of palm oil land in Gunung Mas Regency is higher than the proportion of paddy fields. If oil palm expansion continues in the future, agricultural drought could become more widespread. Keywords: Expansion of oil palm, Land use change, Paddy Fields, Remote sensing, Spatio-Temporal Analysis

Community Awareness and Participation for Land and Forest Fire Prevention and Implementation of Lancang Kuning Nusantara System Application in Toba District, North Sumatra Province

This study employs a sociometric analysis of Community Concern and Participation in Implementing the Lancang Kuning Nusantara Application for Forest and Land Fire Prevention in Toba Regency, Indonesia. With a sample of 401 respondents, the study aims to comprehensively understand social interactions and community engagement in this context. Results indicate that the perception score (X(i.1)) and participation score (X(i.2)) are high or significant, reflecting positive community awareness and active involvement in fire prevention activities. However, the acceptability score (X(i.3)) is low or insignificant, suggesting potential dissatisfaction or skepticism regarding the effectiveness or implementation of current programs. Furthermore, standard deviation analysis reveals that variability in respondent ratings is generally within control, with only 2 out of 15 testing points showing high standard deviation, while 13 are within acceptable limits. Nevertheless, the proportion of respondents who rate low (PRMR) exceeds the desired threshold of 10% across all variables, indicating that many respondents have low evaluations. These findings suggest a need for improved communication, education, and potential adjustments to enhance the acceptability and effectiveness of the fire prevention initiatives and the Lancang Kuning Nusantara System application.

SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR

Soil moisture is one of the factors that has recently become the focus of research because it is strongly correlated with forest and land fires, where low soil moisture will increase drought and the incidence of forest and land fires. For this reason, this study aims to create a prediction model for soil moisture as an early prevention of fires in peatlands using the Random Forest Regressor (RFR) algorithm. RFR is used because of its ability to predict values and its resistance to overfitting and outliers. A dataset covering soil moisture, precipitation, temperature, maturity, and peat thickness was collected from August 2019 to December 2023. The data includes soil moisture, precipitation, temperature, maturity, and peat thickness. The data were divided into 80% for modeling and 20% for testing. Model performance was optimized through random search CV, resulting in significant prediction accuracy R-squared: 0.914, MAE: 0.0081, MSE: 0.0007, RMSE: 0 .0271, and MAPE: 0.969. These findings demonstrate the effectiveness of RFR in soil moisture prediction and pave the way for more appropriate and timelier implementation of fire mitigation strategies.

CLUSTER MAPPING OF HOTSPOTS USING KERNEL DENSITY ESTIMATION IN WEST KALIMANTAN

Forest and land fires pose a recurring concern every year in Indonesia, often taking place in West Kalimantan Province, particularly during the dry season. This study aims to use the Kernel Density Estimation (KDE) to categorize the data of the hotspots in the province of West Kalimantan according to their density and to map the cluster level of the fire risks in the region. The data utilized in this study are secondary data obtained from the images of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument, which are available on firms.modaps.eosdis.nasa.gov and provided by NASA. The data focuses on hotspots dispersed across West Kalimantan province during 2020. The variables examined in the study were the confidence level (≥80%) of forest and land fire hotspots, the distance from each point to the nearest river, and the distance from each point to the nearest road. The kernel density estimation method with a Gaussian kernel function yielded clustering results into three distinct groups according to their vulnerability levels. Low vulnerability areas comprise Cluster 1, which consists of 127 points or 50.97% of the total hotspots. Medium vulnerability areas belong to Cluster 2, which has 47 points or 30.32% of the total. Cluster 3 includes high vulnerability locations, consisting of 29 points or 18.71% of the total. The most susceptible areas to forest and land fires are located within the Ketapang regency.

Drought Analysis in Ketapang District using the Keetch-Byram Drought Index Method

Ketapang Regency is one of the areas in West Kalimantan that is prone to drought. Drought can trigger forest and land fires. In this research, the Keetch-Byram Drought Index (KBDI) method was used to determine the level of drought in Ketapang Regency. The KBDI method relies on annual rainfall accumulation, daily rainfall, and maximum air temperature. The KBDI values obtained were correlated with the number of hotspots using Pearson correlation. This research was conducted throughout 2018-2022. Based on the monthly average KBDI value, the highest drought in Ketapang Regency occurred in August and September, while the lowest drought occurred in December and January. In terms of the annual average, the highest drought occurred in 2019. During the ENSO phenomenon in 2019, the El Niño phase experienced higher drought than the La Niña phase and normal years. In the El Niño phase, drought levels reach high to extreme categories. The correlation value between annual KBDI and the number of hotspots is 0.88, indicating a solid relationship. An increase in the KBDI value will be followed by an increasing number of hotspots.

Disaster Communication in Forest and Land Fire Management in South Kalimantan

The recurrence of forest and land fires in the South Kalimantan region can be attributed to the insufficient implementation of disaster risk communication. Hence, it is imperative for this study to address the topic of disaster risk communication as a means of mitigating forest and land fires in the region of South Kalimantan. This study addresses the scientific subject of why the southern Kalimantan provincial government is still struggling to coordinate the resolve of forest and land fires, despite the implementation of forest and land fire disaster control measures. The research methodology employed in this study utilizes a qualitative approach, namely of a descriptive nature. The data collection approaches employed in this study include interviews, documentation analysis, and literature review. This study aims to elucidate the dearth of educational information pertaining to the prevention and mitigation of forest and land fires within communities. The presence of suitable information and education boards is lacking. Despite the government's extensive endeavors in forest and land fire disaster management in the South Kalimantan region, the absence of an early preventive system pertaining to such calamities remains evident. The proper implementation of educational initiatives pertaining to disaster prevention and early detection has not been effectively carried out. The current state of optimizing information pertaining to public education within the realm of catastrophes remains constrained by the limited involvement of the government in actively engaging with other relevant parties, particularly the media.

Changes in plantation area and spatial modelling of forest and land fire vulnerability based on hotspot analysis 2019-2023 Ogan Ilir regency, South Sumatra

Changes in plantation area and spatial modelling of forest and land fire vulnerability based on hotspot analysis 2019-2023 Ogan Ilir regency, South Sumatra

Development of a Prediction Model for Potential Forest and Land Fires using Machine Learning Algorithms Based on Patrol Data

Indonesia allocates 120 million hectares or 64% of its land area as forest areas. Indonesia's forests continue to experience deforestation; one of the causes is forest and land fires (karhutla). The government conducts forest and land fire prevention through integrated patrols with the Forest and Land Fire Prevention Patrol Information System (SIPP Karhutla) facility for patrol data management. However, the patrol data are still primarily used for data observation and simple spatial analysis in the spatial module. Patrol data has not been used for further forest and land fire prevention studies. Based on these problems, this research aims to build a prediction model of potential forest and land fires using SVM, Random Forest, and XGBoost algorithms and compare model performance to get the best model. The preprocessing stage uses the SMOTE-ENN method to handle data class imbalance, and the k-fold cross-validation stage and hyperparameter tuning use the random search method. The confusion matrix evaluation method to see the model performance in terms of accuracy is XGBoost (94.81%), Random Forest (90.23%), SVM-linear (79.58%), SVM-polynomial model (73.99%), SVM-rbf (74.26%), and SVM-sigmoid (35.04%). Therefore, the best prediction model is XGBoost (94.81%) with boosting technique. The results of this study have implications for helping early prevention of forest and land fires on the islands of Sumatra and Kalimantan.

Culture-Based Justice: Legal Decisions on Forest and Land Fires by Indigenous People with Local Wisdom Approach in the Middle Sentence

Culture-Based Justice: Legal Decisions on Forest and Land Fires by Indigenous People with Local Wisdom Approach in the Middle Sentence

<b>PEMBAKARAN LAHAN UNTUK PEMBUKAAN LAHAN PERKEBUNAN (STUDI PUTUSAN NOMOR 468/PID.B/LH/2021/PN RHL)</b>

Forest and land burning incidents are a phenomenon that often occurs in Indonesia. Most people still carry out actions to clear land by burning land. The formulation of the problem is about the suitability of District Court Decision Number 468/Pid.B/Lh/2021/Pn Rhl regarding the case of land burning to clear plantation land with the provisions of UUPPLH and the efforts that must be made by the regional government of Rokan Hilir Regency, Riau Province to overcome land fires plantations in the Rokan Hilir Regency area, Riau Province. This type of research uses normative law which is descriptive analytical and uses secondary data which is analyzed qualitatively by drawing deductive conclusions. Research results and discussion and hhe conclusion is that the District Court Decision Number 468/Pid.B/Lh/2021/Pn Rhl is in accordance with the provisions of Article 99 paragraph (1) UUPPLH but according to the author this decision can add Article 69 paragraph (1) letter a as a strengthening article for Article 99 paragraph (1) and Article 69 paragraph (1) letter h UUPPLH. With the Riau Province Regional Regulation Number 1 of 2019, the Riau Regional Government is not prepared enough to monitor technical preparedness measures, other efforts include extinguishing and rehabilitation

Discourse Analysis of Forest and Land Fire Management in Indonesia

The agriculture, forestry, and land use (AFOLU) sector is Indonesia's largest contributor to greenhouse gas emissions. Within this sector, forest and peatland fires has a major emissions impact. Optimizing fire management requires collaboration among various parties and should address the underlying causes of burning. Scientific media provides insights into important actors, potential causes, and implementation of forest fire control at national and local levels. This research uses discourse analysis to analyze the role of scientific media in providing information regarding forest fires and fire management activities. It finds an inconsistency between scientific articles and observational data. Furthermore, there is a bias in addressing the causes and efforts to control forest fires. National journals indicate that forest and land fire management in Indonesia focus primarily on ecological aspects and do not take into economic factors, despite economic reasons being the most common cause of fires. Keywords: discourse, forest fire, management, scientific media

Zero-Burning Policy in Land Preparation: Social Changes and Its Impact on Communities and the Environment

In Indonesia, forest and land fires have become an annual occurrence, with devastating effects. Following major fires in 2015, the government implemented policies restricting the use of fire in land preparation. This policy has affected the livelihoods of traditional farmers who still use fire for land clearing. The research aims to analyze the social changes that have occurred among traditional farmers as a result of the policy to ban burning in land preparation, and to assess its impact. The study was undertaken in Pulang Pisau Regency (Central Kalimantan) and Ogan Komering Ilir Regency (South Sumatera). Findings reveal two types of social transformation processes, namely, changes in agricultural commodities and changes in livelihoods. In general, fire policy restrictions in land preparation improved peatland ecosystems, but has had a detrimental impact on some rural communities living around peatlands. To anticipate these effects, the government promoted no-burn land preparation techniques and initiated a new paddy field construction scheme. However, both were less successful due to local resistance for their unsuitability in the local context.

Thermal Performance of Clay and Millet Waste Compressed Earth Blocks Stabilized with Cement

As a contribution of the building sector to mitigating the effects of climate change, namely rising sea levels, floods, droughts, cyclones, sandstorms, retreat of arable land and forest fires, in anticipation of the objectives of the Paris Agreement, on the one hand, and energy efficiency on the other hand and the development of sustainable and environmentally friendly building materials, this paper presents the thermal characterization of compressed earth blocks using two clays used by the population of MARADI in Niger for the construction of habitats. The clays are mixed with sand (10%), cement (4%) and varying proportions of millet waste from 0% to 10%. The study shows that the thermal conductivity of composites decreases as the amount of millet waste increases. Conversely, the thermal resistance increases with each addition. Conductivity values varies from 0.268 W. m<sup>−1</sup>.K<sup>−1</sup> to 0.644 W. m<sup>−1</sup>.K<sup>−1</sup> for MARADAWA clay (BAM) samples and from 0.275 W. m<sup>−1</sup>.K<sup>−1</sup> to 0.723 W. m<sup>−1</sup>.K<sup>−1</sup> for Jiratawa clay (BAJ) samples. This represents a reduction of 61.96% for Jiratawa clay and 58.39% for MARADAWA clay compared to non-added materials. Composite materials are more effective in terms of thermal insulation.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

Pengembangan Kapasitas Kelompok Tani Tunas Makmur Dalam Pengelolaan Produk Nanas Di Kelurahan Sungai Pakning Kabupaten Bengkalis

This research was conducted because Sungai Pakning Village, Bengkalis Regency, was one of the areas that experienced land and forest fires that occurred in Riau Province in 2015. So far, peatland management has often failed, both by the government and companies. One of their failures is that they only focus on land restoration and ignore community empowerment. With the independent peat village program created by PT. Pertamina RU II Production, Sungai Pakning, Bengkalis Regency. Among the independent peat village programs, there is an environmental conservation program which has become the center of attention for environmental observers, namely the development of peat forests to manage pineapple products. This research aims to determine the capacity development of the Tunas Makmur Farmer group in managing pineapple products and to find out what are the supporting factors for capacity development in the Tunas Makmur Farmer group in Sungai Pakning, Bengkalis Regency. This research was conducted using a qualitative descriptive method. Data was obtained through interviews and field observations. The results of this research show that the capacity development measure according to Ilato has demonstrated good capacity in aspects of organizational structure, decision making processes, work procedures and mechanisms and inter-organizational relationships. Good capacity development is supported by 1) facilities and infrastructure, which are being developed continuously, 2) good cooperation between various parties or institutions involved in developing the capacity of the Tunas Makmur Farmers Group.

Peatland wetness as an indicator of fire occurrence in forest and land fires (FLFs)

Background: Peatland ecosystems play an important role in the hydrological cycle and carbon cycling. In Indonesia, peatlands store about 28.6 gigatonnes of carbon which is equivalent to 10 years of global fossil fuel emissions. Peatlands act as a water storage during wet seasons and slowly release water during dry seasons to maintain river discharges and hydrological balance. However, climate change induced prolong drought has increased peatland dryness in recent decades which elevate the risks of unwanted peatland fires. During El Nino-induced drought in 2015, over 2.6 million hectares of forest and land burned, emitting 0.81–1.4 gigatonnes of greenhouse gases. The extreme fires damaged biodiversity, degraded water quality and displaced thousands of locals. This study aimed to analyze peatland wetness as an indicator of fire occurrences in forest and land fires (FLFs) in Riau, Indonesia by examining the relationship between degree of peatland wetness derived from satellite imagery and hotspots data. Methods: Peatland wetness was estimated from microwave backscattering coefficients at several RadarSat synthetic aperture radar (SAR) wavelengths and cross validated with water table depth measurements from 120 monitoring wells. Hotspots data between 2015-2020 were obtained from NASA's MODIS active fire product. Finding: Preliminary results showed significant negative correlations between peatland wetness and numbers of hotspots in peatlands, with more hotspots occurring in drier peatlands compared to wetter ones. This implies that maintaining peatland hydrological functions through continuoussaturation is pivotal to prevent severe peatland wildfires under future climate change. Conclusion: Conservation efforts to restore hydrological balance in degraded peatlands through re-wetting strategies are recommended. Further research utilizing machine learning algorithms to produce high-resolution peatland wetness maps can improve fire risk monitoring in peatlands. Novelty/Originality of this Study: This study introduces the novel concept of utilizing peatland wetness as a key indicator for predicting and mitigating forest and land fires in Indonesia, particularly in Riau Province. By combining peatland moisture and temperature data, the research establishes threshold values to better predict fire risks and guide timely mitigation efforts, thereby enhancing the efficiency and effectiveness of FLF response activities.

Enhancing fire disaster management: Innovative approaches using physical peatland monitoring data

Background: In Indonesia, the persistent occurrence of forest and land fires highlights the critical importance of early detection in determining the success of mitigation efforts. Method: This study explores several key aspects related to peatland wetness and its impact on fire prevention. Firstly, it examines the relationship between rainfall and the humidity and temperature of peatlands. Secondly, the study investigates peatland wetness as an indicator of hotspot emergence. Thirdly, the study evaluates stakeholder perceptions regarding the use of peat wetness monitoring in determining the emergency status of forest and land fire disasters. Findings: The study's results indicate that rainfall significantly influences peatland humidity, which in turn reflects the level of peat humidity and temperature. It was also found that peatlands with a Dry-Moderate humidity category can be a reliable indicator of the emergence of fire spots. The consensus among stakeholders is that monitoring peatland humidity is very important for decision-making related to emergency status. Finally, this study proposes a forest and land fire mitigation concept based on peatland humidity. Conclusion: This approach aims to reduce the risk of such fires by utilizing monitoring results to enhance preparedness, taking into consideration the current state of peatland wetness. Overall, this research underscores the importance of integrating peatland wetness monitoring into forest and land fire mitigation strategies to improve early detection and reduce the risk of fires. Novelty/Originality of this study: A study of forest fires in Indonesia links peatland wetness to fire hotspots, providing a reliable indicator for early fire detection. This is an innovative approach to forest fire prevention.

Legal Awareness of Citizens in Preventing Forest and Land Fires (Karhutla) in South Kalimantan Province

This article is based on an empirical study of forest and land fires in South Kalimantan Province. These fires frequently occur and persist, especially during drought seasons. The main causes are twofold: first, the expansion of agricultural land through clearing new areas; and second, the negligence of residents during hot, dry seasons, such as discarding cigarette butts carelessly without considering the potential consequences. The root issue behind these fires is the low level of legal awareness among citizens. This lack of awareness is due to the low educational levels of residents living near fire-prone areas, as well as insufficient and poorly structured government-led socialization efforts. Additionally, the economic conditions of these communities, where many are low-income farmers, contribute to their reliance on traditional, quick, and cost-effective methods like burning to manage land for agriculture. This practice not only harms the environment but also impacts public health. Therefore, effective measures to address forest and land fires require a dual approach from the authorities: enhancing legal awareness through improved socialization efforts and addressing the economic challenges faced by residents in fire-affected areas.