scholarly journals Forest Fire in East Nusa Tenggara during 2015-2019: Comparison to Forest Fire in Kalimantan and Sumatera

2021 ◽  
Vol 893 (1) ◽  
pp. 012010
Author(s):  
Sumaryati ◽  
D F Andarini ◽  
N Cholianawati ◽  
A Indrawati
Keyword(s):  
Carbon Monoxide ◽  
Forest Fire ◽  
Forest Fires ◽  
Forest Biomass ◽  
Comparison Method ◽  
Atmospheric Environment ◽  
Annual Average ◽  
Statistical Comparison ◽  
Fire Area ◽  
Total Column

Abstract East Nusa Tenggara is one of the provinces in Indonesia that has big forest fires following some provinces in Kalimantan and Sumatra. However, forest fires in East Nusa Tenggara have less attention in forest fires discussion in Indonesia. This study aims to analyze forest fires in East Nusa Tenggara and their impact on reducing visibility and increasing carbon monoxide (CO) from 2015 to 2019. In this study, hotspot, forest fire area, Oceanic Niño Index, visibility, and CO total column data were used to analyze the forest fires using a statistical comparison method in East Nusa Tenggara, Kalimantan, and Sumatra. The result shows that the number of hotspots in East Nusa Tenggara less than in Kalimantan and Sumatra for the same forest fire area. The forest fires in East Nusa Tenggara do not harm the atmospheric environment significantly. East Nusa Tenggara dominantly consists of savanna areas with no peatland, hence, the forest biomass burning produces less smoke and CO. Furthermore, the forest fire in East Nusa Tenggara has not an impact on decreasing visibility and increasing CO total column, in contrast, visibility in Sumatra and Kalimantan has fallen to 6 km from the annual average, and CO total column rise three times of normal condition during peak fire.

scholarly journals INTERCEPTION AND INFILTRATION OF RAINWATER ON THE LAND OF EX FOREST FIRE ON TAHURA R. SOERJO LEDUG BLOCK

2019 ◽  
Vol 2 (1) ◽  
pp. 29
Author(s):  
Oktavian Dwi Suhermanto ◽  
Tatag Muttaqin ◽  
Nugroho Tri Waskitho
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Soil Infiltration ◽  
Infiltration Capacity ◽  
Hydrological System ◽  
Fire Area

Forest fires often occur in many islands of indonesia including in Kalimantan, Sumatra, Java, Sulawesi and other regions. These fires can lead to damage for ecosystems, flora and fauna, even ecosystem hydrology. One of the hydrological system that was disturbed is the interception and infiltration. Interception is the ability of trees to retain water rain then rereleased in steam. Infiltration is the process of water absorbing into the soil, infiltration capacity is the soil’s ability of absorbing water per unit of time. This research is to know the rest of the tree's ability to retain water, and knowing the infiltration of ex forest fire area on TAHURA R. Soerjo, Ledug blocks. This research was carried out on 17-23 January 2019 in ex forest fire area on TAHURA R. Soerjo, with an elevation of 1100-1200 masl. In the ex forest fire area there are 2 dominant trees species to do measurements of interception, there are Tutup (Mallotus paniculatus) and Klerek (Sapindus rarak DC). The results of the interception on Klerek tree is 10% and Tutup is 60%.  For the capacity of the infiltration is 27, 6 mm/hour. 

scholarly journals Impact of spaceborne carbon monoxide observations from the S-5P platform on tropospheric composition analyses and forecasts

2017 ◽  
Vol 17 (2) ◽  
pp. 1081-1103 ◽  
Author(s):  
Rachid Abida ◽  
Jean-Luc Attié ◽  
Laaziz El Amraoui ◽  
Philippe Ricaud ◽  
William Lahoz ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Forest Fires ◽  
Weather Conditions ◽  
Emission Sources ◽  
Simulation Experiments ◽  
Northern Summer ◽  
Free Model ◽  
Comparison Of The Results ◽  
The Impact ◽  
Total Column

Abstract. We use the technique of Observing System Simulation Experiments (OSSEs) to quantify the impact of spaceborne carbon monoxide (CO) total column observations from the Sentinel-5 Precursor (S-5P) platform on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode associated with extremely hot and dry weather conditions. We describe different elements of the OSSE: (i) the nature run (NR), i.e., the truth; (ii) the CO synthetic observations; (iii) the assimilation run (AR), where we assimilate the observations of interest; (iv) the control run (CR), in this study a free model run without assimilation; and (v) efforts to establish the fidelity of the OSSE results. Comparison of the results from AR and the CR, against the NR, shows that CO total column observations from S-5P provide a significant benefit (at the 99 % confidence level) at the surface, with the largest benefit occurring over land in regions far away from emission sources. Furthermore, the S-5P CO total column observations are able to capture phenomena such as the forest fires that occurred in Portugal during northern summer 2003. These results provide evidence of the benefit of S-5P observations for monitoring processes contributing to atmospheric pollution.

scholarly journals Increased Northern Hemispheric carbon monoxide burden in the troposphere in 2002 and 2003 detected from the ground and from space

2004 ◽  
Vol 4 (5) ◽  
pp. 4999-5017 ◽  
Author(s):  
L. N. Yurganov ◽  
P. Duchatelet ◽  
A. V. Dzhola ◽  
D. P. Edwards ◽  
F. Hase ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Boreal Forest ◽  
Northern Hemisphere ◽  
Forest Fires ◽  
Ground Level ◽  
Mixing Ratios ◽  
Moderate Resolution ◽  
Northern Hemispheric ◽  
Co Emission ◽  
Total Column

Abstract. Carbon monoxide total column amounts in the atmosphere have been measured in the High Northern Hemisphere (30°–90° N, HNH) between January 2002 and December 2003, based on the analysis of infrared solar spectra recorded with spectrometers of high and moderate resolution. They are compared to ground-level CO mixing ratios and to total column amounts measured from space by the Terra/MOPITT instrument. In comparison to the unperturbed 2000–2001 period, all these databases reveal increased CO abundances in 2002–2003 summer-autumn times, with maximum anomalies observed in September 2002 and August 2003. Using a simple two-box model, the corresponding annual CO emission anomalies have been found equal to 98 Tg in 2002 and 142 Tg in 2003, thus close to those for 1996 and 1998. It is most likely that strong boreal forest fires in the HNH induced the increased CO burdens.

scholarly journals Forest Fires in the Ural Federal district and their impact on the environment

2020 ◽  
pp. 4-12
Author(s):  
В.К. Куплевацкий ◽  
Н.Ш. Шабалина
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Federal District ◽  
Economic Damage ◽  
Environmental Damage ◽  
Minimum Area ◽  
Forest Protection ◽  
Significant Damage ◽  
Fire Area ◽  
Chelyabinsk Region

На основе актов и книг учета лесных пожаров, а также статистической отчетности проанализированы показатели фактической горимости лесов за 2016–2020 гг. Установлено, что за 2020 г. на территории Уральского федерального округа зафиксировано 2182 лесных пожара. При этом пройденная огнем площадь составила 167,2 тыс. га. Указанные показатели несколько превышают значения количества и пройденной огнем площади по округу за последние 5 лет: 1961 случай и 124,6 тыс. га соответственно. Наибольшее количество лесных пожаров зафиксировано в 2020 г. в челябинской области – 587 случаев, а наименьшее – в Ямало-Ненецком автономном округе – 111 случаев. При этом максимальной пройден- ной огнем лесных пожаров площадью в 2020 г. характеризуется Ханты-Мансийский автономный округ – Югра – 144,7 тыс. га. Минимальная пройденная огнем площадь зафиксирована в Тюменской области – 1,6 тыс. га. Площадь среднего пожара за 2020 г. по округу составила 76,64 га, при этом в Ханты-Мансийском авто- номном округе – Югре она равнялась 308,0 га, а в Тюменской области – 6,98 га. Экономический ущерб от лесных пожаров составил по округу 4 109 793, 16 тыс. руб., при этом на тушение было затрачено 575 481,57 тыс. руб. Значительный размер ущерба от лесных пожаров, а также экологический ущерб вызывают необходи- мость дальнейшего совершенствования охраны лесов. On the bases and books of forest fire accounting, as well as statistical reporting the indicators of actual forest fire rates for 2016–2020 were and lyzed. It was established that in 2020 2182 forest fires were recorded in the Ural Federal Distict. While the area covered by fire was 167,2 th/ga. These indicators slightly exceed the value of the number and the area covered by fire over the past five years in the district. The latter account for 1961 cases and 124,6 thousands of ha respectively. The largest number of forest fires was recorded in 2020 in Chelyabinsk region – 587 cases, the least in the Yamalonenets autonomous okrug – 111 cases. At the same time the maximum area covered by forest fires in 2020 is charaiterized by the Khanty-Mansiysk autonomous okrug – Yugra – 144,7 thousands of has. The minimum area covered by the fire was recorded in the Tyumen region – 1,6 th. ha. The average fire area in 2020 around the Okrug was 76,64 ha, at the same time in the Khanty-Mansiysk autonomous okrug – Yugra, it is 308,0 ha, but in the Tyumen region – 6,98 ha. The economic damage from forest fires amounted to 4 109 793,16 th of roubes, at the same time 575 481,57 th of ronbes were spent fire suppressing significant damage from forest fires as well as environmental damage necessitates futher forest protection improvement.

scholarly journals Forest Fire Recognition Based on Feature Extraction from Multi-View Images

2021 ◽  
Vol 38 (3) ◽  
pp. 775-783
Author(s):  
Di Wu ◽  
Chunjiong Zhang ◽  
Li Ji ◽  
Rong Ran ◽  
Huaiyu Wu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Forest Fire ◽  
Forest Fires ◽  
Recognition Rate ◽  
Color Space ◽  
Image Features ◽  
Dynamic Features ◽  
Feature Extraction Method ◽  
Fire Area ◽  
Fire Recognition

Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

scholarly journals Increased Northern Hemispheric carbon monoxide burden in the troposphere in 2002 and 2003 detected from the ground and from space

2005 ◽  
Vol 5 (2) ◽  
pp. 563-573 ◽  
Author(s):  
L. N. Yurganov ◽  
P. Duchatelet ◽  
A. V. Dzhola ◽  
D. P. Edwards ◽  
F. Hase ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Forest Fires ◽  
Hot Spots ◽  
Ground Level ◽  
The Sun ◽  
Mixing Ratios ◽  
Moderate Resolution ◽  
Northern Hemispheric ◽  
Co Emission ◽  
Total Column

Abstract. Carbon monoxide total column amounts in the atmosphere have been measured in the High Northern Hemisphere (30°-90° N, HNH) between January 2002 and December 2003 using infrared spectrometers of high and moderate resolution and the Sun as a light source. They were compared to ground-level CO mixing ratios and to total column amounts measured from space by the Terra/MOPITT instrument. All these data reveal increased CO abundances in 2002-2003 in comparison to the unperturbed 2000-2001 period. Maximum anomalies were observed in September 2002 and August 2003. Using a simple two-box model, the corresponding annual CO emission anomalies (referenced to 2000-2001 period) have been found equal to 95Tg in 2002 and 130Tg in 2003, thus close to those for 1996 and 1998. A good correlation with hot spots detected by a satellite radiometer allows one to assume strong boreal forest fires, occurred mainly in Russia, as a source of the increased CO burdens.

scholarly journals Impact of Spaceborne Carbon Monoxide Observations from the S-5P platform on Tropospheric Composition Analyses and Forecasts

2016 ◽  
Author(s):  
R. Abida ◽  
J.-L. Attié ◽  
L. El Amraoui ◽  
P. Ricaud ◽  
W. Lahoz ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Forest Fires ◽  
System Simulation ◽  
Weather Conditions ◽  
Simulation Experiments ◽  
Northern Summer ◽  
Free Model ◽  
Comparison Of The Results ◽  
The Impact ◽  
Total Column

Abstract. We use the technique of Observing System Simulation Experiments (OSSEs) to quantify the impact of spaceborne carbon monoxide (CO) total column observations from the Sentinel-5 Precursor (S-5P) platform on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode associated with extremely hot and dry weather conditions. We describe different elements of the OSSE: (i) the Nature Run (NR), i.e., the "Truth"; ii) the CO synthetic observations; (iii) the assimilation run (AR), where we assimilate the observations of interest; (iv) the control run (CR), in this study a free model run without assimilation; and (v) efforts to establish the fidelity of the OSSE results. Comparison of the results from AR and the CR, against the NR, shows that CO total column observations from S-5P provide a significant benefit (at the 99 % confidence level) at the surface, with the largest benefit occurring over land in remote regions. Furthermore, the S-5P CO total column observations are able to capture phenomena such as the forest fires that occurred in Portugal during summer 2003. These results provide evidence of the benefit of S-5P observations for monitoring processes contributing to atmospheric pollution.

scholarly journals A comparative analysis of forest fire policies in protected areas in Portugal and in the state of Santa Catarina (Brazil): a general approach

2007 ◽  
pp. 71-79
Author(s):  
Fantina Tedim ◽  
Maria Lúcia de Paula Herrmann
Keyword(s):  
Comparative Analysis ◽  
Protected Areas ◽  
Forest Fire ◽  
Forest Fires ◽  
The State ◽  
Santa Catarina ◽  
Annual Average ◽  
Burned Areas ◽  
Area Burned ◽  
Average Area

Recent data suggest that both Portugal and Brazil have seen an increase in the number of forest fires in protected areas. In Portugal, between 1992 and 2003 the annual average area burned in protected areas was 10,418 ha and in the period 2001-2005 was 16,025 ha. Nevertheless, in Brazil, the state of Santa Catarina stands out as the state recording a decrease in the number of fires. Based on these facts, the main objectives of the present research are to analyse the incidence, severity and causes of forest fires in protected areas in both countries and to assess the impacts of prevention and combat policies as well as the strategies and models implemented in the recovery of burned areas.

scholarly journals Enhancement of carbon monoxide concentration in atmosphere due to large scale forest fire of Uttarakhand

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6507 ◽  
Author(s):  
Jaya Thakur ◽  
Prajesh Thever ◽  
Biswadip Gharai ◽  
MVR Sesha Sai ◽  
VNRao Pamaraju
Keyword(s):  
Carbon Monoxide ◽  
Forest Fire ◽  
Forest Fires ◽  
Large Scale ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Fire Event ◽  
North East ◽  
The North ◽  
Indian State

The richly forested Indian state of Uttarakhand experienced widespread forest fires in April to May 2016. The current study examines dispersion of carbon monoxide (CO) from the source regions of forest fire to distant places, using the Lagrangian particle dispersion model, FLEXPART. Atmospheric Infrared Sounder (AIRS) observations revealed that CO columnar concentrations had increased by almost 28 percentage during 24 April to 02 May 2016 with respect to the previous non-burning period of April 2016 at Uttarakhand. It is also seen that there is considerable enhancement of 45 percentage in average columnar concentration of CO during the burning period, compared to that in the previous 5 years as observed by AIRS. In the present study, concentrations of CO at different pressure levels and columnar CO over Uttarakhand during the forest fire event have been simulated using FLEXPART. The area averaged profile of model derived CO has been compared with the profile from AIRS onboard Aqua. Comparison between model derived columnar CO and satellite observations shows good agreement with coefficient of correlation (r) approximately 0.91 over the burnt areas. Further analysis using FLEXPART reveals that the transport of pollutants is towards north-eastern and eastern regions from the locations of forest fire events. Model derived vertical distribution of CO over Tibet, which is situated at the north-east of Uttarakhand, shows significant increase of CO concentration at higher altitudes around 3 km from the mean sea level during the fire event. FLEXPART results show that the emissions from the Uttarakhand fires were transported to Tibet during the study period.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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