Linking crown fire likelihood with post-fire spectral variability in Mediterranean fire-prone ecosystems

Evaluation of spectral unmixing techniques using MODIS in a structurally complex savanna environment for retrieval of green vegetation, nonphotosynthetic vegetation, and soil fractional cover

Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission

First evaluation of fire severity retrieval from PRISMA hyperspectral data

Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data

Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries

Ticks are important ectoparasites and vectors of pathogens that cause disease in humans and animals. The natural habitat of Ixodes ricinus ticks is forests, which are convenient habitats to search for hosts, including reservoir hosts, and therefore can be an important habitat source of tick-borne pathogens. The aim of the study was to assess the usefulness of detailed forest habitat-type maps to estimate the tick-borne risk at a local scale (Lower Silesia, SW Poland). For the purposes of estimating tick abundance, we used the land cover maps available from the Forest Data Bank. For I. ricinus collection, nine sites located in three forest habitat types were chosen: broadleaf forest, mixed broadleaf and coniferous forest and coniferous forest. Ticks were collected once a month from April to June 2018 and 2019 using the standard flagging method. At each of the nine sites, ticks were collected in four plots, of 100 m2 each. Tick abundance was analysed using general linear mixed models (GLMM). A total of 2196 (10.1/100 m2) ticks were collected, including 2093 Ixodes ricinus (95.3%; 9.6/100 m2), 46 Dermacentor reticulatus (2.1%; 0.2/100 m2) and 57 Haemaphysalis concinna (2.6%; 0.3/100 m2). Among the collected I. ricinus were 589 larvae (28.1%; 2.7/100 m2), 1261 nymphs (60.3%; 5.8/100 m2), 128 females (6.1%; 0.6/100 m2) and 115 males (5.5%; 0.5/100 m2). We found a highly significant effect of forest habitat type on the density of ticks for broadleaf forest (coefficient = 1.87267, p-value = 2.79e - 07). Additionally, a significant influence of air temperature and relative humidity on the abundance of ticks was observed. During spring, the peak activity of I. ricinus was recorded in May and June. For DNA amplification of Borrelia burgdorferi s.l., a nested PCR method was used. Out of 494 I. ricinus, 83 (16.8%) were positive for Borrelia spp. The RFLP method showed the occurrence of five species including four belonging to the B. burgdorferi s.l. complex: B. afzelii (30.1%), B. garinii (38.6%), B. valaisiana (2.4%) and B. lusitaniae (18.1%). Furthermore, B. miyamotoi (9.6%), a species belonging to bacteria that cause relapsing fever as well as co-infection of B. miyamotoi/B.lusitaniae (1.2%) were found. The differences in the infection level of Borrelia spp. between broadleaf forest and mixed broadleaf and coniferous forest were statistically significant.

Increases in the deposition of atmospheric nitrogen (N) influence N cycling in forest ecosystems and can result in negative consequences due to the leaching of nitrate into groundwaters. From December 1995 to February 1998, the Pan-European Programme for the Intensive and Continuous Monitoring of Forest Ecosystems measured forest conditions at a plot scale for conifer and broadleaf forests, including the performance of time series of soil solution chemistry. The influence of various ecosystem conditions on soil solution nitrate concentrations at these forest plots (n = 104) was then analyzed with a statistical model. Soil solution nitrate concentrations varied by season, and summer concentrations were approximately 25% higher than winter ones. Soil solution nitrate concentrations increased dramatically with throughfall (and bulk precipitation) N input for both broadleaf and conifer forests. However, at elevated levels of throughfall N input (more than 10 kg N ha−1 y−1), nitrate concentrations were higher in broadleaf than coniferous stands. This tree-specific difference was not observed in response to increased bulk precipitation N input. In coniferous stands, throughfall N input, foliage N concentration, organic layer carbon–nitrogen (C:N) ratio, and nitrate concentrations covaried. Soil solution nitrate concentrations in conifer plots were best explained by a model with throughfall N and organic layer C:N as main factors, where C:N ratio could be replaced by foliage N. The organic layer C:N ratio classes of more than 30, 25–30, and less than 25, as well as the foliage N (mg N g−1) classes of less than 13, 13–17, and more than 17, indicated low, intermediate, and high risks of nitrate leaching, respectively. In broadleaf forests, correlations between N characteristics were less pronounced, and soil solution nitrate concentrations were best explained by throughfall N and soil pH (0–10-cm depth). These results indicate that the responses of soil solution nitrate concentration to changes in N input are more pronounced in broadleaf than in coniferous forests, because in European forests broadleaf species grow on the more fertile soils.

Evaluation of potential of multiple endmember spectral mixture analysis (MESMA) for surface coal mining affected area mapping in different world forest ecosystems

Improved vegetation maps are required for fire management and biodiversity assessment, from critical inputs for hydrological and biogeochemical models and represent a means for scaling-up point measurements. At scales greater than 10 meters, vegetation communities are typically mixed consisting of leaves, branches, exposed soil and shadows. To map mixed vegetation, many researchers employ spectral mixture analysis (SMA). In most SMA applications, a single set of spectra consisting of green vegetation, soil, non- photosynthetic vegetation and shade are used to 'unmix' images. However, because most scenes contain more than four components, this simple approach leads to fraction errors and may fail to differentiate many vegetation types. In this work, we apply a new approach called multiple endmember spectral mixture analysis, in which the number and types of endmembers vary per-pixel. Using this approach, hundreds of unique models are generated that account for community specific differences in plant chemistry, physical attributes and phenology. Additionally, we describe a new strategy for developing and organizing regionally specific spectral libraries. We present result from a study in the Santa Monica Mountains using AVIRIS data, in which we map grassland and chaparral communities, mapping species dominance in some cases to a high degree of accuracy.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征 DOI: 10.5846/stxb202104271113 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31960330);宁夏自然科学基金(2020AAC03112) Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River Basin based on Google Earth Engine cloud platform Author: Affiliation: Fund Project: National Natural Science Foundation of China (31960330)；Ningxia Natural Science Foundation (2020AAC03112) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域是我国重要的生态屏障,研究黄河流域森林植被净初级生产力(Net Primary Productivity,NPP)的时空变化特征及驱动机制,对解释黄河流域森林碳汇/源变化具有重要意义。基于Google Earth Engine (GEE)云平台,利用MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,采用岭回归分析、Hurst指数和冗余分析(Redundancy Analysis,RDA)对黄河流域2001-2019年森林NPP的时空变化特征及影响因子进行分析。结果表明:(1)2001-2019年,黄河流域森林平均总面积为3.66万km2,其中阔叶林、针叶林、混交林平均面积分别为:2.64万km2、0.01万km2和1.01万km2,森林NPP年总量呈线性增加趋势,其均值为8.99Tg C,年均增速为0.36Tg C/a,19a增长率为173.60%;不同森林类型的NPP年总量均值分别为:4.79Tg C (阔叶林)、6.04×10-5Tg C (针叶林)和0.64Tg C (混交林),年均增速为:阔叶林(0.16Tg C/a)>混交林(0.04Tg C/a)>针叶林(6.98×10-6Tg C/a)。(2)2001-2019年,黄河流域森林年均NPP呈线性增加趋势,其均值为241.58g C m-2 a-1,年均增速为7.18g C m-2 a-1,19a增长率为108.63%;不同森林类型的年均NPP均值分别为:178.48g C m-2 a-1(阔叶林)、0.60g C m-2 a-1(针叶林)和62.49g C m-2 a-1(混交林),年均增速为:阔叶林(4.75g C m-2 a-1)>混交林(2.39g C m-2 a-1)>针叶林(0.04g C m-2 a-1)。(3)黄河流域森林NPP呈增加趋势的面积占94.50%,其中显著增加的面积占73.29%;呈减少趋势的面积占5.50%,其中显著减少的面积占1.57%。阔叶林NPP显著增加的面积最高(76.78%),其次为混交林(60.84%),针叶林最少(56.76%)。(4)黄河流域森林NPP的Hurst指数(H)介于0.38-1.00之间,平均值为0.87,其中H≥0.5的像元数约占99.34%,黄河流域森林NPP在未来一段时间内仍保持持续增加趋势。(5)归因分析表明环境因子对黄河流域森林NPP时空变化的总解释率为55.80%,显著影响的环境因子为经度(35.50%)、降水(8.00%)、气温(6.50%)和纬度(5.40%)。2001-2019年黄河流域森林NPP呈增加趋势,且呈现较强的可持续性;GEE云平台结合冗余分析可及时、高效获取黄河流域森林NPP的时空变化并对其进行归因分析。 Abstract:The Yellow River Basin is an important ecological barrier in China. To study the spatio-temporal variation characteristics and driving mechanisms of the Net Primary Productivity (NPP) of forest is of great significance to explain the change of forest carbon sink and source in the Yellow River Basin. Based on Google Earth Engine (GEE) cloud platform, MOD17A3H V6 NPP data, MCD12Q1 V6 land cover data, ECMWF/ERA5 weather data, USGS/SRTMGL1_003 elevation data, and ridge regression analysis, Hurst index, redundancy analysis (RDA) were used to analyze the spatio-temporal variation characteristics and influencing factors of the forest NPP in the Yellow River Basin from 2001 to 2019. The results showed that (1) from 2001 to 2019, the average total area of forest in the Yellow River Basin was 36600 km2, of which the average area of broadleaf forest, coniferous forest and mixed forest was 26400 km2, 100 km2 and 10100 km2. The annual total forest NPP showed a linear increasing trend, with an average value of 8.99 Tg C, an average annual growth rate of 0.36 Tg C/a, and a 19-year growth rate of 173.60%. The averagely annual total NPP of different forest types were:4.79 Tg C (broadleaf forest), 6.04×10-5 Tg C (coniferous forest) and 0.64 Tg C (mixed forest), and the averagely annual growth rate were:broadleaf forest (0.16 Tg C/a) > mixed forest (0.04 Tg C/a) > coniferous forest (6.98×10-6 Tg C/a). (2) From 2001 to 2019, the annual average NPP of the forest in the Yellow River Basin increased linearly, with an average value of 241.58 g C m-2 a-1, an average annual growth rate of 7.18 g C m-2 a-1, and a 19-year growth rate of 108.63%. The mean annual average NPP of different forest types were 178.48 g C m-2 a-1 (broadleaf forest), 0.60 g C m-2 a-1 (coniferous forest) and 62.49 g C m-2 a-1(mixed forest), and the averagely annual growth rate were:broadleaf forest (4.75 g C m-2 a-1) > mixed forest (2.39 g C m-2 a-1) > coniferous forest (0.04 g C m-2 a-1). (3) The area of forest NPP showed an increasing trend in the Yellow River Basin accounting for 94.50%, of which 73.29% was significantly increased; the area showed a decreasing trend accounting for 5.50%, of which the area with a significant decrease accounted for 1.57%. The area of the significantly increased of the broadleaf forest NPP was the highest (76.78%), followed by mixed forest (60.84%) and coniferous forest (56.76%). (4) The Hurst index (H) of the forest NPP in the Yellow River Basin was between 0.38-1.00, with an average value of 0.87. Among them, the amount of H ≥ 0.5 accounted for about 99.34%, and the forest NPP in the Yellow River Basin would continue to increase in the future. (5) Attribution analysis showed that the total interpretation rate of the environmental factors on the spatio-temporal variation of the forest NPP in the Yellow River Basin was 55.80%, and the environmental factors that had significant effects were longitude (35.50%), precipitation (8.00%), temperature (6.50%) and latitude (5.40%). From 2001 to 2019, the forest NPP of the Yellow River Basin increased and showed strong sustainability. The GEE cloud platform combined with redundant analysis can timely and efficiently obtain the spatio-temporal variation of the forest NPP of the Yellow River Basin and perform an attribution analysis. 参考文献 相似文献 引证文献

Crown fires are extremely dangerous, very difficult to fight and often have a rate of spread over 100 times more than a surface fire. Therefore, it is important to determine the type of forest fire in the early detection based on wireless sensor networks (WSNs) to adopt the proper strategy to fight the fire. It is shown that this could be done analyzing the noise power spectrum of forest fires: surface fires noise spectrum can be modeled as the red noise (gradual increase of trend line amplitude toward lower frequencies), while for crown fires noise spectrum trend line has an almost bell-shaped (Gaussian) type. The noise frequency range is relatively narrow for crown fires and ranged from 250 to 450 Hz. The intermediate type of fires (strong surface fire and incipient crown fire) has a transient noise spectrum from broadband red to narrowband Gaussian. The article presents the spectrums of 9 different forest fires. The different trend line of the forest fire noise power spectrum is the parameter that can be used to determine the type of forest fire in WSNs.

Graham, Russell T.; McCaffrey, Sarah; Jain, Theresa B. (tech. eds.) 2004. Science basis for changing forest structure to modify wildfire behavior and severity. Gen. Tech. Rep. RMRS-GTR-120. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 43 p. Fire, other disturbances, physical setting, weather, and climate shape the structure and function of forests throughout the Western United States. More than 80 years of fire research have shown that physical setting, fuels, and weather combine to determine wildfire intensity (the rate at which it consumes fuel) and severity (the effect fire has on vegetation, soils, buildings, watersheds, and so forth). Millions of acres of forestlands (mainly in dry forests dominated by ponderosa pine and/or Douglas-fir) contain a high accumulation of flammable fuels compared to conditions prior to the 20th century. Forests with high stem density and fuel loading combined with extreme fire weather conditions have led to severe and large wildfires (such as those seen in the summers of 2000, 2002, and 2003) that have put a number of important values at risk. Although homes in the path of a wildfire are perhaps the most immediately recognized value, these wildfires also put numerous other human and ecological values at risk, such as power grids, drinking water supplies, firefighter safety, critical habitat, soil productivity, and air quality. For a given set of weather conditions, fire behavior is strongly influenced by stand and fuel structure. Crown fires in the dry forest types represent an increasing challenge for fire management as well as a general threat to the ecology of these forests and the closely associated human values. Crown fires are dependent on the sequence of available fuels starting from the ground surface to the canopy. Limiting crown fire in these forests can be accomplished by actions that manage in concert the surface, ladder, and crown fuels. Reducing crown fire and wildland fire growth across landscapes decreases the chances of developing large wildfires that affect human values adjacent to forested areas. However, a narrow focus on minimizing crown fire potential will not necessarily reduce the damage to homes and ecosystems when fires do occur. Homes are often ignited by embers flying far from the fire front, and by surface fires. Fire effects on ecosystems can also occur during surface fires where surface and understory fuels and deep organic layers are sufficient to generate high temperatures for long periods. Fuel treatments can help produce forest structures and fuel characteristics that then reduce the likelihood that wildfires will cause large, rapid changes in biophysical conditions. Fuel treatments can also help modify fire behavior sufficiently so that some wildfires can be suppressed more easily. Subsequent, sustained fuel treatments can maintain these conditions. Different fuel reduction methods target different components of the fuel bed. Thinning mainly affects standing vegetation, and other types of fuel treatments such as prescribed fire and pile burning woody fuels are needed to modify the combustion environment of surface fuels. In forests that have not experienced fire for many decades, multiple fuel treatments—that is, thinning and surface fuel reduction—may be required to significantly affect crown fire and surface fire hazard. Fuel treatments cannot guarantee benign fire behavior but can reduce the probability that extreme fire behavior will occur. Fuel treatments can be designed to restore forest conditions to a more resilient and resistant condition than now exists in many forests, and subsequent management could maintain these conditions, particularly in dry forests (ponderosa pine and Douglas-fir) where crown fires were historically infrequent. The degree of risk reduction will depend to some degree on the level of investment, social and economic acceptability of treatments, and concurrent consideration of other resource values (for example,

Aim Carbon (C) and nitrogen (N) stoichiometry is a critical indicator of biogeochemical coupling in terrestrial ecosystems. However, our current understanding of C : N stoichiometry is mainly derived from observations across space, and little is known about its dynamics through time. Location Global secondary forests. Methods We examined temporal variations in C : N ratios and scaling relationships between N and C for various ecosystem components (i.e. plant tissue, litter, forest floor and mineral soil) using data extracted from 39 chronosequences in forest ecosystems around the world. Results The C : N ratio in plant tissue, litter, forest floor and mineral soil exhibited large variation across various sequences, with an average of 145.8 ± 9.4 (mean ± SE), 49.9 ± 3.0, 38.2 ± 3.1 and 18.5 ± 0.9, respectively. In most sequences, the plant tissue C : N ratio increased significantly with stand age, while the C : N ratio in litter, forest floor and mineral soil remained relatively constant over the age sequence. N and C scaled isometrically (i.e. the slope of the relationship between log-transformed N and C is not significantly different from 1.0) in litter, forest floor and mineral soil both within and across sequences, but not in plant tissue either within or across sequences. The C : N ratio was larger in coniferous forests than in broadleaf forests and in temperate forests than in tropical forests. In contrast, the N–C scaling slope did not reveal significant differences either between coniferous and broadleaf forests or between temperate and tropical forests. Main conclusions These results suggest that C and N become decoupled in plants but remain coupled in other ecosystem components during stand development.

Degradation of cattle pastures is a management concern that influences future land use in Amazonia. However, “degradation” is poorly defined and has different meanings for ranchers, ecologists, and policy makers. Here we analyze pasture degradation using objective scalars of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed soil (S) derived from Landsat imagery. A general, probabilistic spectral mixture model decomposed satellite spectral reflectance measurements into subpixel estimates of PV, NPV, and S covers at ranches in western and eastern Amazonia. Most pasture management units at all ranches fell along a single line of decreasing PV with increasing NPV and S, which could be considered a degradation continuum. The ranch with the highest stocking densities and most intensive management had greater NPV and S than a less intensively managed ranch. The number of liming, herbiciding, and disking treatments applied to each pasture management unit was positively correlated with NPV and negatively correlated with PV. Although these objective scalars revealed signs of degradation, intensive management kept exposed soil to <40% cover and maintained economically viable cattle production over several decades. In ranches with few management inputs, the high PV cover in young pastures declined with increasing pasture age, while NPV and S increased, even where grazing intensity was low. Both highly productive pastures and vigorous regrowth of native vegetation cause high PV values. Analysis of spectral properties holds promise for identifying areas where grazing intensity has exceeded management inputs, thus increasing coverage of senescent foliage and exposed soil.

Vegetation biomass and soil organic carbon (SOC) pools for the three representative forest types, i.e. conifer forest (CF), mixed conifer and broad-leaf forest (CBF), evergreen broad-leaf forest (EBF) in South China were investigated. We found that SOC stock of the three chief forest ranged from 55.54 to 151.16 MgC·ha-1, and it increased with increasing vegetation biomass under the same type forest within 100cm depth. The organic carbon contents at an equivalent level of forest maturity tended to be in the following decreasing order: EBF > CBF > CF, various active organic carbon (AOC) fractions in the 0-20cm topsoil layer tended to be in the following decreasing order: light fraction carbon (LFC) ≈ particulate organic carbon (POC) > easily oxidisable carbon (EOC) > microbial biomass carbon (MBC) > water-soluble carbon (WSC). At an equivalent level of forest maturity, there was a trend that each of these five AOC fractions increased from CF to CBF to the EBF.