scholarly journals Antecedent Rainfall, Excessive Vegetation Growth and Its Relation to Wildfire Burned Areas in California

2021 ◽  
Vol 8 (9) ◽  
Author(s):  
José J. Hernández Ayala ◽  
Jenna Mann ◽  
Elisabeth Grosvenor
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Víctor Fernández-García ◽  
Elena Marcos ◽  
Sara Huerta ◽  
Leonor Calvo

Abstract Background Wildfires are one of the major environmental concerns in Mediterranean ecosystems. Thus, many studies have addressed wildfire impacts on soil and vegetation in Mediterranean forests, but the linkages between these ecosystem compartments after fire are not well understood. The aim of this work is to analyze soil-vegetation relationships in Mediterranean burned forests as well as the consistency of these relationships among forests with different environmental conditions, at different times after fire, and among vegetation with different functional traits. Results Our results indicate that study site conditions play an important role in mediating soil-vegetation relationships. Likewise, we found that the nature of soil-vegetation relationships may vary over time as fire effects are less dominant in both ecosystem compartments. Despite this, we detected several common soil-vegetation relationships among study sites and times after fire. For instance, our results revealed that available P content and stoichiometry (C:P and N:P) were closely linked to vegetation growth, and particularly to the growth of trees. We found that enzymatic activities and microbial biomass were inversely related to vegetation growth rates, whereas the specific activities of soil enzymes were higher in the areas with more vegetation height and cover. Likewise, our results suggest that resprouters may influence soil properties more than seeders, the growth of seeders being more dependent on soil status. Conclusions We provide pioneer insights into how vegetation is influenced by soil, and vice-versa, in Mediterranean burned areas. Our results reflect variability in soil-vegetation relationships among study sites and time after fire, but consistent patterns between soil properties and vegetation were also detected. Our research is highly relevant to advance in forest science and could be useful to achieve efficient post-fire management.


Author(s):  
S. A. Lysenko

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.


2013 ◽  
Vol 15 (2) ◽  
pp. 270 ◽  
Author(s):  
Haida YU ◽  
Xiuchun YANG ◽  
Bin XU ◽  
Yunxiang JIN ◽  
Tian GAO ◽  
...  

Author(s):  
Д.В. Гусев

Естественное возобновление является важным фактором формирования насаждений, особенно главных лесообразующих пород. Растительное сообщество становится жизнестойким при условии способности восстановить численность популяций заменой погибших экземпляров новыми. Было выяснено в каком количестве происходит естественное возобновление сосны на гарях по сравнению с граничащими участками, не пройденными пожарами, взятые в качестве контроля. Район исследований относится к южной подзоне тайги на территории Ленинградской области в Кировском и Лужском лесничествах. Объектом исследований стали сосновые насаждения, где работы проводились в летний период с 2013 по 2015 год. Всего подобрано 36 участков (включая контроль) размером не более 0,3 га. Учет подроста проводился на учетных площадках. Каждая учетная площадка закладывалась при помощи шеста длиной 178,5 см. Площадь круговых площадок составляла 10 м2, они расположены последовательно друг за другом с непосредственным примыканием. На каждой площадке проводили перечет подроста и делили его по высоте на три категории крупности: мелкий до 0,5 м, средний – 0,6–1,5 м и крупный – более 1,5 м. А также естественное возобновление на участках делили по густоте – на три категории: редкий – до 2 тыс., средней густоты – 2–8 тыс., густой – более 8 тыс. растений на 1 га; по распределению по площади – на три категории в зависимости от встречаемости. Анализ послепожарного возобновления в сосняках показал, что на пробных площадях наблюдается отличное возобновление подроста сосны и обилие на площади, все это связано с уничтожением лесной подстилки, увеличением минерализации почвы что, в конечном счете, положительно влияет на естественное лесовосстановление, о чем свидетельствует появление всходов, а также лучше становится гидрологический режим почвы. Благодаря этому происходит хорошее восстановление. Количество благонадежного подроста составляет от 3,5 до 11,9 тыс. шт./га и его достаточно для естественного восстановления ценопопуляции после пожара. Подтверждена зависимость количество самосева и толщины лесной подстилки. Прогретая после пожара, богатая минеральными веществами почва благоприятна для появления всходов и самосева древесных растений. Natural regeneration is an important factor in the formation of plantations, especially the main forest-forming species. Plant community becomes viable, provided the ability to recover populations, replacement of lost copies new. Find out how much happens in a natural pine regeneration in burned areas compared to adjacent areas not affected by fires, are taken as a control. The study area belongs to the subzone of southern taiga on the territory of Leningrad region, the Kirov and Luga districts. The object of research became pine plantations where the work was carried out in year period from 2013 to 2015. Just picked up 36 stations (including the control) no larger than 0.3 hectares. accounting for the undergrowth was conducted on index sites. Each user platform was laid with a pole length of 178.5 cm the area of the circular pads was 10 m2, they are located successively one after another with a direct connection. At each site conducted the translation of the undergrowth and it was divided in height into three categories of size: small up to 0.5 m, average 0.6 to 1.5 meters and large – more than 1.5 meters. And natural regeneration on plots divided by the density for three categories: rare – up to 2 thousand, medium density – 2 to 8 thousand, thick – more than 8 thousand plants per 1 ha; on the distribution of the area – into three categories depending on the occurrence. Analysis of post-fire regeneration in pine forests showed that the sample areas there is a great renewal of undergrowth of pine and the abundance on the square, all this is due to the destruction of forest litter, increasing salinity of the soil which, ultimately, has a positive effect on natural regeneration, as evidenced by the appearance of seedlings, as well as better hydrological regime of the soil. Which a good recovery. The number of reliable undergrowth is from 3.5 to 11.9 thousand PCs/ha, enough for natural regeneration of seedlings after the fire. Confirmed the dependence of the number of self-seeding and thickness of forest litter. After the fire-warmed, mineral-rich soil is favorable for emergence and self-seeding of woody plants.


2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.


2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.


2021 ◽  
Vol 13 (6) ◽  
pp. 1147
Author(s):  
Xiangqian Li ◽  
Wenping Yuan ◽  
Wenjie Dong

To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.


Author(s):  
Barbara F. Günthardt ◽  
Juliane Hollender ◽  
Martin Scheringer ◽  
Konrad Hungerbühler ◽  
Mulatu Y. Nanusha ◽  
...  

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