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

2019 ◽  
Vol 11 (3) ◽  
pp. 308 ◽  
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.

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

2021 ◽  
Vol 13 (24) ◽  
pp. 5127
Author(s):  
Changming Yin ◽  
Minfeng Xing ◽  
Marta Yebra ◽  
Xiangzhuo Liu
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Coniferous Forest ◽  
Northern China ◽  
Tree Canopy ◽  
Burn Severity ◽  
Environmental Drivers ◽  
Obvious Effect ◽  
Tree Canopy Cover

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.

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

2018 ◽  
Vol 3 (1) ◽  
pp. 15 ◽  
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.

scholarly journals Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

2021 ◽  
Vol 13 (23) ◽  
pp. 4745
Author(s):  
Jennifer N. Hird ◽  
Jahan Kariyeva ◽  
Gregory J. McDermid
Keyword(s):  
Time Series ◽  
Forest Health ◽  
Forest Degradation ◽  
Google Earth ◽  
Forest Recovery ◽  
Percent Recovery ◽  
Health Initiatives ◽  
Spectral Recovery ◽  
Google Earth Engine ◽  
Normalized Burn Ratio

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

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

2013 ◽  
Vol 12 (2) ◽  
pp. 191-199 ◽  
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

scholarly journals Diversity of Medicinal Plants among Different Tree Canopies

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.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Regeneration dynamics in fragmented landscapes at the leading edge of distribution: Quercus suber woodlands as a study case

2021 ◽  
Author(s):  
Jorge Luis Montero-Muñoz ◽  
Carmen Ureña ◽  
Diego Navarro ◽  
Valentín Herrera ◽  
Pilar Alonso-Rojo ◽  
...  
Keyword(s):  
Tree Growth ◽  
Quercus Suber ◽  
Forest Recovery ◽  
Tree Cover ◽  
Distribution Area ◽  
Soil Parameters ◽  
Old Fields ◽  
Summer Drought ◽  
Regeneration Dynamics ◽  
Positive Effect

Abstract Aims We studied the regeneration dynamics of woodlands and abandoned old fields in a landscape dominated by Quercus suber in its lower limits of rainfall and temperature. Two hypotheses were established: (1) regeneration of Quercus species is strongly favored by the presence of tree cover; and (2) growth of Q. suber is driven by the climatic variables that represent the lower ecological limit of its leading distribution edge. Methods We selected woodlands and old fields with and without tree remnants (n = 3 per type), and analyzed stand structure, soil parameters and tree growth. Results Succession was arrested in old fields without tree remnants. By contrast, remnant trees were accelerators of forest recovery in old fields. Tree cover played a fundamental role in Quercus recruitment throughout seed dispersal and facilitation that mitigate the effects of summer drought on seedlings. Also, tree cover improved soil parameters (e.g., organic matter) that are important factors for understanding differences in regeneration. Winter/spring precipitation exerted a positive effect on tree growth, as well as temperatures during winter/spring and September. Conclusions Regeneration dynamics are modeled by the density of tree cover in the cold and dry edge of the distribution area of Q. suber where Q. ilex is increasing in abundance. Although temperature has a positive effect on the tree growth of Q. suber, when demographic processes are considered, decreases in water availability likely play a critical role in Q. ilex recruitment. This in turn changes dominance hierarchies, especially in abandoned areas with little or no tree cover.

scholarly journals Environmental Factors Associated With Loa loa Microfilaria Prevalence and Intensity in Diverse Bioecological Zones of Cameroon

2021 ◽  
Vol 2 ◽  
Author(s):  
Xavier Badia-Rius ◽  
Hannah Betts ◽  
Samuel Wanji ◽  
David Molyneux ◽  
Mark J. Taylor ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Polynomial Regression ◽  
Canopy Cover ◽  
Nonparametric Tests ◽  
Tree Canopy ◽  
Mean Annual Temperature ◽  
Loa Loa ◽  
Factors Associated ◽  
Tree Canopy Cover

Loiasis (African Eye Worm) is a filarial infection caused by Loa loa and transmitted by Chrysops vectors, which are confined to the tropical rainforests of Central and West Africa. Loiasis is a major impediment to control and elimination programmes that use the drug ivermectin due to the risk of serious adverse events. There is an urgent need to better refine and map high-risk communities. This study aimed to quantify and predict environmental factors associated with loiasis across five bioecological zones in Cameroon. The L. loa microfilaria (mf) prevalence (%) and intensity (mf number/ml) data from 42 villages within an Equatorial Rainforest and Savannah region were examined in relation to climate, topographic and forest-related data derived from satellite remote sensing sources. Differences between zones and regions were examined using nonparametric tests, and the relationship between L. loa mf prevalence, mf intensity, and the environmental factors using polynomial regression models. Overall, the L. loa mf prevalence was 11.6%, L. loa intensity 927.4 mf/ml, mean annual temperature 23.7°C, annual precipitation 2143.2 mm, elevation 790 m, tree canopy cover 46.7%, and canopy height 19.3m. Significant differences between the Equatorial Rainforest and Savannah region were found. Within the Equatorial Rainforest region, no significant differences were found. However, within the Savannah region, significant differences between the three bioecological zones were found, and the regression models indicated that tree canopy cover and elevation were significant predictors, explaining 85.1% of the L. loa mf prevalence (adjusted R2 = 0.851; p<0.001) and tree cover alone was significant, explaining 58.1% of the mf intensity (adjusted R2 = 0.581; p<0.001). The study highlights that environmental analysis can help delineate risk at different geographical scales, which may be practical for developing larger scale operational plans for mapping and implementing safe effective interventions.

scholarly journals Regeneration Dynamics in Fragmented Landscapes at the Leading Edge of Distribution: Quercus Suber L. as A Study Case

2021 ◽  
Author(s):  
Jorge Montero-Muñoz ◽  
Carmen Ureña ◽  
Diego Navarro ◽  
Valentín Herrera ◽  
Pilar Alonso-Rojo ◽  
...  
Keyword(s):  
Tree Growth ◽  
Quercus Suber ◽  
Forest Recovery ◽  
Tree Cover ◽  
Distribution Area ◽  
Soil Parameters ◽  
Old Fields ◽  
Summer Drought ◽  
Regeneration Dynamics ◽  
Positive Effect

Abstract AimsWe studied the regeneration dynamics of woodlands and abandoned old fields in a landscape dominated by Quercus suber in its lower limits for rainfall and temperature. Two hypotheses were established: (1) recruitment of Q. suber is restricted more by abiotic variations than other species adapted to more extreme Mediterranean conditions; and (2) decreases in precipitation reduce growth, but temperature positively affects growth in the leading cold edge of this species distribution area.MethodsWe selected nine sites containing forest stands and old fields with and without tree remnants, and analyzed stand structure, soil parameters and tree growth.ResultsSuccession was arrested in plots without tree remnants after cultivation abandonment. By contrast, remnant trees were accelerators of forest recovery. Tree cover played a fundamental role in Quercus recruitment throughout seed dispersal and facilitation effects that ameliorate summer drought. However, soil variables also significantly explained much of the variance observed and are important for understanding differences in regeneration. Winter and spring precipitation exerted a positive effect on tree growth, as well as temperatures during winter/spring and September.ConclusionsRegeneration dynamics are modeled by the density of tree cover in the cold and dry edge of the distribution area of Q. suber where Q. ilex is increasing in abundance. Although temperature has a positive effect on the tree growth of Q. suber, when demographic processes are considered, decreases in water availability likely play a critical role in Q. ilex recruitment. This in turn changes dominance hierarchies, especially in abandoned areas with little or no tree cover.

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