Occurrence Of Forest Fires Research Articles

Aspects of Forest and Forestry Management Laws on the Rate of Deforestation in Indonesia

The thick smoke resulting from forest fires in Indonesia has caught the attention of foreign countries. Through their news channels, foreign nations have commented on the frequent occurrences of forest fires in Indonesia. The phenomenon of forest fires remains an unresolved issue for Indonesia. Deforestation resulting from forest burning continues to persist, especially when carried out deliberately by human activities. While Indonesia has regulations prohibiting land and forest fires, the author will analyze the issues surrounding why land and forest fires incidents persist in Indonesia and how to address them. The researcher employs a descriptive research method with a literature review in data collection, drawing from primary and secondary legal materials. The author s observes inconsistencies in norms regarding the prohibition of land and forest burning. Sanctions for land and forest fires outlined in related regulations vary, lack consistency and lead to overlapping rules. Therefore, there is a need for reformulation of regulations with loopholes. Additionally, regulations concerning indigenous communities should be established to ensure clear recognition and legal certainty for these communities.

An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments

The accumulation of fuel and the homogenization of the landscape in Mediterranean forests are leading to an increasingly hazardous behavior of wildfires, fostering larger, more intense, severe, and frequent wildfires. The onset of climate change is intensifying this behavior, fostering the occurrence of extreme forest fires threatening the persistence of forest communities.In this study we present an assessment of the post-fire recovery potential of the most representative tree-forest communities affected by fire in Spain: Pinus halepensis, Pinus nigra, Pinus pinaster and Quercus ilex. A large database of field data collected during specific campaigns -carried out 25 years after the fire- is used in combination with remote sensing, forest inventory and geospatial data to build an empirical model capable of predicting the chances of recovery. The model, calibrated using Random Forest, combines information on burn severity (remote sensing estimates of the Composite Burn Index), local topography (slope and terrain aspect) and climatic data (mean values and trends of temperature and precipitation) to provide information on the degree of similarity (vegetation height, horizontal cover of the vegetation layer along vertical strata, aboveground biomass and species diversity) between the plots burned in the summer of 1994 and the unburned control.Overall, only 33 out of the 131 burned plots could be considered as recovered, that is, reaching a similar state to unburned stands in neighboring areas. Our results suggest a primary role played by burn severity (the higher the severity the lower the probability of recovery), but strongly modulated by local topographic features (higher probability of recovery on steep north-facing slopes). In turn, increasingly warm and wetter conditions increased the chance of recovery.

Determining the fire season by analyzing climatic conditions in northeastern Serbia

The territory of Serbia is sensitive to forest fires, which endanger various systems and play an important role in shaping the ecosystem. The probability of the occurrence and spread of forest fires in the area of northeastern Serbia was examined, depending on the influence of climatic conditions. Changes in climatic conditions are investigated at the annual and monthly level of air temperature, precipitation and air humidity values recorded at three main meteorological stations (Zaječar, Negotin, Crni Vrh) in the area of northeastern Serbia. Changes in climatic conditions in the period 2009-2022 compared to the period 1961-1990 were analyzed, with an emphasis on 2012 and 2014. The length of the average fire season was determined through a modified version of Thornthwaite’s evapotranspiration index. The fire season is longer in the area of Negotin and Zaječar than in the area of Crni Vrh. On the basis of the monthly humidity indices, a significantly longer fire season was determined in 2012 compared to 2014, as well as the multi-year period 1961-1990.An analysis of the value of the Burning Index (B) for the period 2009-2022 was performed and a correlation was established with the data on the number of forest fires. The burning index in the area of northeastern Serbia is the highest during the months of July, August and September. Significantly higher values of the burning index are for 2012 compared to 2014, which is correlated with the occurrence of forest fires in this period.

Investigating the Relationship between the Influencing Fire Factors and Forest Fire Occurrence in the Districts of Rompin, Pekan, and Kuantan in the State of Pahang, Malaysia, Using Google Earth Engine

Forest fires pose a significant threat to ecosystems and human livelihoods. Understanding the role of climatic factors in fire occurrence is crucial for effective fire management and prevention. This study analyses the influences of temperature, precipitation, and wind speed on fire incidents in the districts of Rompin, Pekan, and Kuantan in Pahang, Malaysia. The investigation is motivated by newspaper articles dated early March 2021, which report that the fires in these districts were triggered by an extended period of hot and dry conditions, as highlighted by the Director of the Fire and Rescue Department of Pahang, Malaysia. However, no further investigation or detailed discussion has been deliberated. By examining the historical climatic data and fire incidents, this study aims to investigate the relationships between these climatic variables and fire occurrences. The results reveal that higher temperatures and lower precipitation are associated with increased fire susceptibility due to reduced soil moisture. In contrast, wind speed does not appear to impact fire spread significantly. These findings will undoubtedly provide valuable insights into the complex interactions between climatic variables and regional fire incidents, enabling policymakers and fire management authorities to develop targeted fire prevention and mitigation strategies.

Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

UAV-FDN: Forest-fire detection network for unmanned aerial vehicle perspective

Forest fires can pose a serious threat to the survival of living organisms, and wildfire detection technology can effectively reduce the occurrence of large forest fires and detect them faster. However, the unpredictable and diverse appearance of smoke and fire, as well as interference from objects that resemble smoke and fire, can lead to the overlooking of small objects and detection of false positives that resemble the objects in the detection results. In this work, we propose UAV-FDN, a forest fire detection network based on the perspective of an unmanned aerial vehicle (UAV). It performs real-time wildfire detection of various forest fire scenarios from the perspective of UAVs. The main concepts of the framework are as follows: 1) The framework proposes an efficient attention module that combines channel and spatial dimension information to improve the accuracy and efficiency of model detection under complex backgrounds. 2) It also introduces an improved multi-scale fusion module that enhances the network’s ability to learn objects details and semantic features, thus reducing the chances of small objects being false negative during inspection and false positive issues. 3) Finally, the framework incorporates a multi-head structure and a new loss function, which aid in boosting the network’s updating speed and convergence, enabling better adaptation to different objects scales. Experimental results demonstrate that the UAV-FDN achieves high performance in terms of average precision (AP), precision, recall, and mean average precision (mAP).

Vegetation Analysis and Silvicultural Suggestions for Mitigating Fire Danger and Protecting Nature the Case of a Natura 2000 Site in Southern Greece

Purpose: The development of a methodological approach for the estimation of the wildfire danger in a protected area, located near a big city, based on the dominant factors contributing to a fire-prone environment and affect fire behavior. Theoretical framework: Vegetation management in protected areas characterized by sprawling of adjacent urban and suburban development patterns, is a complicated task, especially under Mediterranean climate due to the high vulnerability to wildfires. They should aim, among others, to mitigate forest-fire occurrence risk, eliminate the wildfire damages as well as to protect humans, and conserve the high biodiversity of the protected areas. Method: We generated a forest fire occurrence danger zone map based on: i) historical fire data ii) types of vegetation and forest ecosystem characteristics, iii) topographical characteristics, iv) distance from the settlements, and v) distance from the roads. A probability model equation for forest fire occurrence was produced. Wildland-Urban Interface mapping was also considered. Results and conclusion: Four categories of forest fire occurrence danger zones, ranging from very high to low, were classified. The zones with high and very high fire occurrence danger cover a low percentage of the study area (6.84% and 5.76%, respectively). In the very high fire danger zones, Pinus halepensis forest and evergreen sclerophyllous vegetation, prevail. A great part of the Wildland-Urban Interface WUI belongs to the zones of high and very high fire occurrence danger. Research implications: Several silvicultural and policy measures for creating fire-resilient ecosystems and mitigating the possibility of a wildfire occurrence and damages, are suggested. Originality/value: The determined site-specific vegetation management and policy measures are of great importance for land management in similar areas.

FOREST FIRE RISK MAPPING FOR THE HIMALAYAN STATE UTTARAKHAND USING GOOGLE EARTH ENGINE

Abstract. Climate change has exacerbated the intensity as well as frequency of forest fire events in the Indian state of Uttarakhand. The present study focusses on undertaking forest fire risk mapping across the state by utilizing geospatial technology along with Google Earth Engine. Ten parameters were identified that have a strong influence in determining fire prone areas. The Analytic Hierarchy Process (AHP) was then implemented for the development of the risk map in which criteria weights were assigned to the parameters based on their ability to influence a forest fire event. The analysis revealed that out of the total forest area, 24.22% is under ‘very high’ risk zone, 29.24% is under ‘high’ risk zone, 18.23% is under ‘moderate’ risk zone, 7.69% is under ‘low’ risk zone and 20.62% is under ‘very low’ risk zone of forest fire. Further study was carried out to determine fire risk levels in populated regions and in some of the most critical nature reserves having high ecological importance which reveals that ‘very high’ and ‘high’ risk zones have greater population density indicating the influence of anthropogenic activities on forest fire occurrence. The results additionally indicate that four national parks and wildlife sanctuaries are particularly vulnerable to forest fires at present which is a source of concern and requires intervention from the stakeholders.

Prediction of Forest Fire Occurrence in Southwestern China

Southwestern China is an area heavily affected by forest fires, having a complex combination of fire sources and a high degree of human interference. The region is characterized by karst topography and a mixture of agricultural and forested areas, as well as diverse and dynamic mountainous terrain. Analyzing the driving factors behind forest fire occurrences in this area and conducting fire risk zoning are of significant importance in terms of implementing effective forest fire management. The Light Gradient Boosting Machine (LightGBM) model offers advantages in terms of efficiency, low memory usage, accuracy, scalability, and robustness, making it a powerful predictive algorithm that can handle large-scale data and complex problems. In this study, we used nearly 20 years of forest fire data in Southwestern China as the data source. Using mathematical statistics and kernel density analysis, we studied the spatiotemporal distribution characteristics of forest fires in Southwestern China. Considering 16 variables, including climate, vegetation, human factors, and topography, we employed the LightGBM model to predict and zone forest fire occurrences in Southwestern China. The results indicated the following conclusions: (i) Forest fires in Southwestern China are primarily concentrated in certain areas of Sichuan Province (such as Liangshan Yi Autonomous Prefecture and Panzhihua City), Guizhou Province (such as Qiannan Buyi and Miao Autonomous Prefecture), Yunnan Province (such as Puer City, Xishuangbanna Dai Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture), and Chongqing Municipality. (ii) In terms of seasonality, forest fires are most frequent during the spring and winter, followed by the autumn and summer. (iii) The LightGBM forest fire prediction model yielded good results, having a training set accuracy of 83.088080%, a precision of 81.272437%, a recall of 88.760399%, an F1 score of 84.851539%, and an AUC of 91.317430%. The testing set accuracy was 79.987694%, precision was 78.541074%, recall was 85.978470%, F1 score was 82.091662%, and AUC was 87.977684%. These findings demonstrate the effectiveness of the LightGBM model in predicting forest fires in Southwest China, providing valuable insights regarding forest fire management and prevention efforts in the area.

Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City.

UAV Multispectral Imagery Predicts Dead Fuel Moisture Content

Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC on the forest surface. From 9 March to 23 March 2023, 5945 multispectral images and 480 sets of dead combustible samples were collected from an urban forestry demonstration site in Harbin, China, using an M300 RTK UAV with an MS600Pro multispectral camera. The multispectral images were segmented by a K-means clustering algorithm to obtain multispectral images containing only dead combustibles on the ground surface. The segmented multispectral images were then trained with the actual moisture content measured by the weighing method through the ConvNeXt deep-learning model, with 3985 images as the training set, 504 images as the validation set, and 498 images as the test set. The results showed that the MAE and RMSE of the test set are 1.54% and 5.45%, respectively, and the accuracy is 92.26% with high precision, achieving the accurate prediction of DFMC over a large range. The proposed new method for predicting DFMC via UAV multispectral cameras is expected to solve the real-time large-range accurate prediction of the moisture content of dead combustible material on the forest surface during the spring fire-prevention period in northeast China, thus providing technical support for improving the accuracy of forest fire risk-level forecasting and forest fire spread trend prediction.

THE INFLUENCE OF CLIMATIC CONDITIONS ON THE OCCURRENCE OF LARGE FOREST FIRES: A CASE STUDY OF STARA PLANINA NATURE PARK IN 2007 AND 2019

This work investigates the relationship between the occurrence of large forest fires (more than 1,000 ha) and meteorological data. Specifically, it analyzes the characteristics of large forest fires in July 2007 (2,500 ha) and October 2019 (2,108 ha) in Serbia’s Stara Planina Nature Park (NP). The results show that at these times, forest fires occurred during long heatwaves with low relative humidity and high air temperature. Dry climatic conditions in the preceding months also contributed to the occurrence of large forest fires, alongside the presence of wind. The study uses climate indices based on the combination of air temperature and precipitation. Forest drought index (FAI) data show that 2007 and 2019 were drier than the 1961–1990 climate period. According to the Lang Precipitation Factor Index (AILang), the periods June–July 2007 and July–October 2019 can be classified as dry. The De Martonne Drought Index (ImDM) shows that July 2007 and October 2019 had the characteristic of areism. These were also the periods when large wildfires were recorded. The results of this study could be used for wildfire risk assessment in protected areas and for fire prevention and suppression planning.

Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia.

Numerous natural disasters that threaten people's lives and property occur in Indonesia. Climate change-induced temperature increases are expected to affect the frequency of natural hazards in the future and pose more risks. This study examines the consequences of droughts and forest fires on the Indonesian island of Kalimantan. We first create maps showing the eleven contributing factors that have the greatest impact on forest fires and droughts related to the climate, topography, anthropogenic, and vegetation. Next, we used RF to create single and multi-risk maps for forest fires and droughts in Kalimantan Island. Finally, using the Coupled Model Intercomparison Project (CMIP6) integrated evaluation model, a future climate scenario was applied to predict multiple risk maps for RCP-SSP2-4.5 and RCP-SSP5-8.5 in 2040-2059 and 2080-2099. The probability of a 22.6% drought and a 21.7% forest fire were anticipated to have an influence on the study's findings, and 2.6% of the sites looked at were predicted to be affected by both hazards. Both RCP-SSP2-4.5 and RCP-SSP5-8.5 have an increase in these hazards projected for them. Researchers and stakeholders may use these findings to assess risks under various mitigation strategies and estimate the spatial behavior of such forest fire and drought occurrences.

Оценка современного состояния авиалесоохранных работ на землях особо охраняемых природных территорий федерального значения

The assessment of air protection works on the lands of specially protected natural territories is presented. The disadvantages of aviation forest monitoring are considered. The analysis of the actual fire danger of forests on the lands of specially protected natural territories is carried out. To assess the risks of 111 specially protected natural territories for the period from 2002 to 2021, a retrospective analysis of the registered thermal points in the systems of space monitoring of forest fires of the information system of remote monitoring of Rosleshoz (ISDM-Rosleshoz) and FIRMS was made. The absence of fires in certain periods does not exclude the risks of their occurrence. Based on the analysis of thermal points, the predominance of spring fires was revealed. The description of the areas characterized by the occurrence of fires depending on the season of the year is given. The result of the analysis of the situation with the occurrence of forest fires on the lands of specially protected natural territories in the context of federal districts is presented. During the study of the forest protection system on the lands of specially protected natural territories, an expert assessment of the organizational structure, forms and composition of fire extinguishing teams, as well as the availability of fire extinguishing forces and means was carried out. The structure of forest fire formations, a communication and warning system for ground and aviation forces are analyzed. An expert assessment of the effectiveness of air patrol routes on the lands of specially protected natural territories was carried out. The problem of remoteness of aircraft bases has been identified. Solutions to problems for areas with low and high burnability are proposed. The problem of the absence of an approved unified methodology for assessing the probability of landscape (natural) fires on lands of other categories, from the territory of which there may be a threat of fire transfer to the lands of specially protected natural territories, is revealed. Proposals have been developed for the formation of optimal patrol routes for manned and unmanned aircraft

Forest Fire Spread Simulation and Fire Extinguishing Visualization Research

There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

MASS MEDIA FRAMING ON DISASTER: CASE STUDY LANDFIRE IN SOUTH SUMATERA

Indonesia is among the nations with the highest number of natural disasters. This syndrome is aggravated by unbalanced information provided by the media. The media use framing to enforce information distribution rules, particularly in regards to forest and land fire reporting. Using a constructivist research methodology, this study argues that framing is the key to influencing public opinion towards actors who might be perceived positively or negatively. The results showed that mass media framing was divided into several themes. This framing explains how the mass media has an important role in disseminating disaster-related information to the public. Some evidence suggests that political competition among the local political elite contributes to the occurrence of forest fires, it’s also ensued in mass media in Indonesia. If the government is unable to enforce strong regulations on irresponsible parties, forest fires will persist. The mass media must exist as a tool to regulate its population so that they do not burn forests and land that is detrimental to the nation.

FOREST FIRE HAZARD MAPPING IN BHUTAN

The study intends to assimilate data for assessing fire hazards for the forest areas in Bhutan. Over the past few decades, there has been an increase in fire events and forest fires have made it urgent to research this issue. Bhutan documented 1,403 occurrences of forest fires in less than ten years (Kuensel, 11th Jan. 2020). Many fossil fuels are burned during forest fires, and a significant amount of carbon dioxide is produced, which adds to the most prevalent greenhouse gas and changes for the regional climate. Moreover, studies have indicated that one of the influential elements contributing to forest fires is climate change. The goal of this research is to consolidate all datasets from multiple sources onto a single GIS platform for the creation of maps that take climatic conditions into account while calculating forest fire hazard. To lessen the risk of forest fires, recommendations and adaptation methods are offered in this paper. In particular, the Department of Forest and Park Services in Bhutan can benefit from the findings of this research for the development, planning, and implementation works.

Causes and period of occurrence of forest fires in Brazilian federal protected areas from 2006 to 2012

Forest fires, regardless of their causes, represent one of the greatest threats to biodiversity in Brazilian protected areas. Collecting data on the causes, periods, and sites with the highest occurrence of fires allows for the adoption of more effective prevention strategies. The aim of this study was to characterize forest fires in Brazilian federal protected areas from 2006 to 2012, thus contributing to improving the knowledge of the dynamics of fires in these areas. Data were obtained from Fire Occurrence Records (ROIs, in Portuguese) available in the National Fire Information System (SISFOGO, in Portuguese) database. The total number of records found was 2,259, of which 88.2% had reported causes. Among the records with a reported cause, 42.2% correspond to unknown causes, 26.7% to debris burning, and 18.5% to arson events, which shows that the majority of fires with a known cause are the result of accidental or intentional human action. Forest fires were more frequent from July to October, with a mean of 50.6 occurrences per year, influenced by the annual precipitation distribution. Minas Gerais, Rio de Janeiro and Ceará were the states with the highest number of records, with 19.8%, 15.5%, and 12.0%, respectively. Forest fires reported without any cause information or reported as unknown cause indicate, respectively, a poor use of the tool (ROI) or an unsatisfactory expertise in identifying the cause of the fire, resulting in an obstacle for planning actions to prevent and fight forest fires in protected areas.

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making.

The Landscape Characteristics Analysis of Rawa Bunder Resort at Park National Way Kambas

The importance of research on the Rawa Bunder Resort’s landscape characteristics at Way Kambas National Park (WKPN) is as a basis for area management according to its function in a lowland tropical rain forest ecosystem. The purpose of this study is to find out the current characteristics of landscape include physical and biological characteristics as well as the driving factors for the establishment of good habitat restoration for flora and fauna life. The results showed that the landscape characteristics of the Rawa Bunder Resort consisted of natural forest and open land containing shrubs with almost the same area composition. The position of the forest appears to be united in the middle to the east of the WKNP. The pressure of Imperata fires can threat the existence of forests. If the preventive action is not taken immediately, the forest will decrease in size. The driving factor for the reduction of the forest matrix is ​​the frequent occurrence of forest fires every year. These fires were triggered by humans who intentionally or unintentionally driven by the wind so that it spread widely.