Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.

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Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Remote Sensing ◽

10.3390/rs12213590 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3590

Author(s):

Changming Yin ◽

Binbin He ◽

Xingwen Quan ◽

Marta Yebra ◽

Gengke Lai

Keyword(s):

Remote Sensing ◽

Forest Management ◽

Radiative Transfer ◽

Forest Fires ◽

Tree Canopy ◽

Burn Severity ◽

Estimation Accuracy ◽

Radiative Transfer Model ◽

Transfer Model ◽

Tree Canopy Cover

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.

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Diversity of Medicinal Plants among Different Tree Canopies

Sustainability ◽

10.3390/su13052640 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2640

Author(s):

Muhammad Zubair ◽

Akash Jamil ◽

Syed Bilal Hussain ◽

Ahsan Ul Haq ◽

Ahmad Hussain ◽

...

Keyword(s):

Medicinal Plants ◽

Coniferous Forest ◽

Canopy Cover ◽

Local Communities ◽

Tree Canopy ◽

Tree Cover ◽

Open Spaces ◽

Floral Diversity ◽

Tree Canopy Cover ◽

Closed Canopy

The moist temperate forests in Northern Pakistan are home to a variety of flora and fauna that are pivotal in sustaining the livelihoods of the local communities. In these forests, distribution and richness of vegetation, especially that of medicinal plants, is rarely reported. In this study, we carried out a vegetation survey in District Balakot, located in Northeastern Pakistan, to characterize the diversity of medicinal plants under different canopies of coniferous forest. The experimental site was divided into three major categories (viz., closed canopy, open spaces, and partial tree cover). A sampling plot of 100 m2 was established on each site to measure species diversity, dominance, and evenness. To observe richness and abundance, the rarefaction and rank abundance curves were plotted. Results revealed that a total of 45 species representing 34 families were available in the study site. Medicinal plants were the most abundant (45%) followed by edible plants (26%). Tree canopy cover affected the overall growth of medicinal plants on the basis of abundance and richness. The site with partial canopy exhibited the highest diversity, dominance, and abundance compared to open spaces and closed canopy. These findings are instrumental in identifying the wealth of the medicinal floral diversity in the northeastern temperate forest of Balakot and the opportunity to sustain the livelihoods of local communities with the help of public/private partnership.

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Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series

Remote Sensing ◽

10.3390/rs11030308 ◽

2019 ◽

Vol 11 (3) ◽

pp. 308 ◽

Cited By ~ 12

Author(s):

Donato Morresi ◽

Alessandro Vitali ◽

Carlo Urbinati ◽

Matteo Garbarino

Keyword(s):

Vegetation Index ◽

Rate Of Change ◽

Tree Canopy ◽

Forest Recovery ◽

Central Apennines ◽

Vegetation Development ◽

Regeneration Dynamics ◽

Tree Canopy Cover ◽

Spectral Recovery ◽

Normalized Burn Ratio

Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery.

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Wildfires and the Canadian Forest Fire Weather Index system for the Daxing'anling region of China

International Journal of Wildland Fire ◽

10.1071/wf09120 ◽

2011 ◽

Vol 20 (8) ◽

pp. 963 ◽

Cited By ~ 29

Author(s):

Xiaorui Tian ◽

Douglas J. McRae ◽

Jizhong Jin ◽

Lifu Shu ◽

Fengjun Zhao ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Coniferous Forest ◽

Northern China ◽

Fire Danger ◽

Fire Season ◽

Fire Weather ◽

Burnt Area ◽

Weather Index ◽

Fire Weather Index

The Canadian Forest Fire Weather Index (FWI) system was evaluated for the Daxing'anling region of northern China for the 1987–2006 fire seasons. The FWI system reflected the regional fire danger and could be effectively used there in wildfire management. The various FWI system components were classified into classes (i.e. low to extreme) for fire conditions found in the region. A total of 81.1% of the fires occurred in the high, very high and extreme fire danger classes, in which 73.9% of the fires occurred in the spring (0.1, 9.5, 33.3 and 33.1% in March, April, May and June). Large wildfires greater than 200 ha in area (16.7% of the total) burnt 99.2% of the total burnt area. Lightning was the main ignition source for 57.1% of the total fires. Result show that forest fires mainly occurred in deciduous coniferous forest (61.3%), grass (23.9%) and deciduous broad leaved forest (8.0%). A bimodal fire season was detected, with peaks in May and October. The components of FWI system were good indicators of fire danger in the Daxing'anling region of China and could be used to build a working fire danger rating system for the region.

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Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

Remote Sensing ◽

10.3390/rs12193249 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3249

Author(s):

Ankit Shekhar ◽

Jia Chen ◽

Shrutilipi Bhattacharjee ◽

Allan Buras ◽

Antony Oswaldo Castro ◽

...

Keyword(s):

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Coniferous Forest ◽

Mixed Forest ◽

Terrestrial Ecosystem ◽

Added Value ◽

Summer Drought ◽

Vegetation Types ◽

The Impact

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

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Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111454 ◽

2020 ◽

Vol 236 ◽

pp. 111454 ◽

Cited By ~ 2

Author(s):

Changming Yin ◽

Binbin He ◽

Marta Yebra ◽

Xingwen Quan ◽

Andrew C. Edwards ◽

...

Keyword(s):

Radiative Transfer ◽

Model Simulation ◽

Canopy Cover ◽

Tree Canopy ◽

Burn Severity ◽

Radiative Transfer Model ◽

Transfer Model ◽

Tree Canopy Cover

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A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Environments ◽

10.3390/environments6030036 ◽

2019 ◽

Vol 6 (3) ◽

pp. 36 ◽

Cited By ~ 9

Author(s):

Ana Teodoro ◽

Ana Amaral

Keyword(s):

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Burn Severity ◽

Landsat 8 ◽

Fire Event ◽

Burned Areas ◽

Area Burned ◽

Sentinel 2A ◽

Normalized Burn Ratio

Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

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Modified Soil-Adjusted Vegetation Index In Multispectral Remote Sensing Data for Estimating Tree Canopy Cover Density at Rubber Plantation

Journal of Geoscience Engineering Environment and Technology ◽

10.24273/jgeet.2018.3.01.1003 ◽

2018 ◽

Vol 3 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Wenang Anurogo ◽

Muhammad Zainuddin Lubis ◽

Mir'atul Khusna Mufida

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Canopy Cover ◽

Remote Sensing Data ◽

Research Area ◽

Tree Canopy ◽

Vegetation Canopy ◽

Sensing Data ◽

Tree Canopy Cover ◽

Cover Density

Forest inventories such as tree canopy density information require a long time and high costs, especially on extensive forest coverage. Remote sensing technology that directly captures the surface vegetation character with extensive recording coverage can be used as an alternative to carrying out such inventory activities. This research aims to determine the level of vegetation canopy cover density on rubber plants that became the location of the research and know the accuracy of the resulting data. The method used in this research is a combination of remote sensing image interpretation, geographic information system, and field measurement. Information retrieval from remote sensing data is done by using ASTER data imagery. This stage includes three parts, namely: pre-field stage, field stage, and post-field stage. The pre-field stage includes the collection of data to be used (including literature studies related to the theme of the study), image processing (geometric and radiometric correction), cropping, masking, land cover classification, vegetation index transformation, and sample determination. The final result of data processing showed that the density of the vegetation canopy in the research area ranged between 7.31 – 12.952 cm / m2 in each grade of vegetation density. These values indicate the range of low-class vegetation canopy cover density to high-class vegetation canopy cover density in the research area. In this research error rate or root mean square error obtained from the calculation of canopy cover density is equal to 1.89.

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Zoning, land use, and urban tree canopy cover: The importance of scale

Urban Forestry & Urban Greening ◽

10.1016/j.ufug.2012.12.005 ◽

2013 ◽

Vol 12 (2) ◽

pp. 191-199 ◽

Cited By ~ 22

Author(s):

Sarah K. Mincey ◽

Mikaela Schmitt-Harsh ◽

Richard Thurau

Keyword(s):

Land Use ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Tree ◽

Tree Canopy Cover ◽

Urban Tree Canopy

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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