共查询到20条相似文献，搜索用时 31 毫秒
1.
Background
Fires emit significant amounts of CO2 to the atmosphere. These emissions, however, are highly variable in both space and time. Additionally, CO2 emissions estimates from fires are very uncertain. The combination of high spatial and temporal variability and substantial uncertainty associated with fire CO2 emissions can be problematic to efforts to develop remote sensing, monitoring, and inverse modeling techniques to quantify carbon fluxes at the continental scale. Policy and carbon management decisions based on atmospheric sampling/modeling techniques must account for the impact of fire CO2 emissions; a task that may prove very difficult for the foreseeable future. This paper addresses the variability of CO2 emissions from fires across the US, how these emissions compare to anthropogenic emissions of CO2 and Net Primary Productivity, and the potential implications for monitoring programs and policy development. 相似文献2.
Indonesia has the world’s largest tropical peatland, mostly located in the southern province of Sumatra, the south of Kalimantan, and Papua. The catastrophic fires between June and October 2015 induced by the El Niño event burnt most of these peatland areas. We analyzed spatio-temporal peat subsidence during pre- and post-fires in the peat hydrological unit of Sungai Sugihan – Sungai Saleh (KHGSS), South Sumatra using Sentinel-1 images by applying DInSAR-SBAS algorithm. Based on our analysis, the linear subsidence rate after the 2015 peat fires increased by a factor 6.4 compared to that of pre-fires. Generally, the estimated subsidence is temporally well-correlated with the precipitation variation. In addition, the subsidence patterns are spatially correlated with the hotspot distribution, peat thickness, and drainage networks. Furthermore, we mapped vegetation cover over the KHGSS by using the Sentinel-1 images as well. The results show that the vegetation degradation is correlated with the hotspot distribution and the highly-degraded vegetation associated with the 2015 peat fires. It demonstrated that the 2015 El Niño event has significant impacts on increasing the amount of the subsidence and the vegetation degradation in KHGSS area. 相似文献
3.
Prasun K. Gangopadhyay Freek van der Meer Paul van Dijk 《International Journal of Applied Earth Observation and Geoinformation》2009
Over the time-scale, earth's atmospheric CO2 concentration has varied and that is mostly determined by balance among the geochemical processes including burial of organic carbon in sediments, silicate rock weathering and volcanic activity. The best recorded atmospheric CO2 variability is derived from Vostok ice core that records last four glacial/interglacial cycles. The present CO2 concentration of earth's atmosphere has exceeded far that it was predicted from the ice core data. Other than rapid industrialization and urbanization since last century, geo-natural hazards such as volcanic activity, leakage from hydrocarbon reservoirs and spontaneous combustion of coal contribute a considerable amount of CO2 to the atmosphere. Spontaneous combustion of coal is common occurrence in most coal producing countries and sometimes it could be in an enormous scale. Remote sensing has already proved to be a significant tool in coalfire identification and monitoring studies. However, coalfire related CO2 quantification from remote sensing data has not endeavoured yet by scientific communities because of low spectral resolution of commercially available remote sensing data and relatively sparse CO2 plume than other geological hazards like volcanic activity. The present research has attempted two methods to identify the CO2 flux emitted from coalfires in a coalmining region in north China. Firstly, a band rationing method was used for column atmospheric retrieval of CO2 and secondly atmospheric models were simulated in fast atmospheric signature code (FASCOD) to understand the local radiation transport and then the model was implemented with the inputs from hyperspectral remote sensing data. It was observed that retrieval of columnar abundance of CO2 with the band rationing method is faster as less simulation required in FASCOD. Alternatively, the inversion model could retrieve CO2 concentration from a (certain) source because it excludes the uncertainties in the higher altitude. 相似文献
4.
Carbon dioxide (CO2) is one of the major gases that contribute to the global warming. Therefore, studying the distribution of CO2 can help people understand the carbon cycle. Based on the GOSAT retrieved CO2 products, the temporal and spatial distribution and seasonal variation of CO2 concentration were analyzed from 2011 to 2015. CO2 concentration has obvious seasonal variation. It was low in summer, and was high in spring, and the annual increase was about 2 ppm. Nevertheless, the annual growth rate of CO2 concentration in summer was higher than that in spring, it was 0.5425% in summer and was 0.46% in spring. CO2 concentration was low in the northwest and was high in the southeast. The growth rate of CO2 was 2.8 ppm in the northwest and was 3.42 ppm in the southeast. More human’s activities made CO2 concentration higher in the southeast than that in other regions. 相似文献
5.
Shufen Pan Guangsheng Chen Wei Ren Shree R. S. Dangal Kamaljit Banger Jia Yang 《International Journal of Digital Earth》2018,11(6):558-582
Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO2 concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO2 concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO2 concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO2. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO2 scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO2 concentrations and its interactions with climate. 相似文献
6.
短波红外通道卫星CO2遥感是近年国际研究热点。首先,开展了卫星观测对气溶胶及大气温度的敏感性研究;其次,针对基于最优化理论的非线性迭代反演方法反演过程中的不收敛问题,提出了修正的阻尼牛顿方法(MDNM),并利用模拟数据评估了MDNM方法的有效性;最后,利用GOSAT卫星数据反演CO2的垂直混合比浓度,并与地基TCCON站点数进行比对。研究结果表明:短波近红外CO2卫星遥感受气溶胶散射及温度影响明显;通过地基比对初步验证了MDNM具有良好的精度,两者的相关性R2达到了0.729。 相似文献
7.
8.
ABSTRACTWidespread forest fire events occurred in the foothills of North Western Himalaya during 24 April to 2 May 2016 (Event-1) and 20–30 May 2018 (Event-2). Their impacts were investigated on the distribution of pollutant gases ozone (O3), carbon monoxide (CO), and oxides of nitrogen (NOx) over Uttarakhand using simulations of Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and in-situ observations of these gases over Dehradun, the capital of Uttarakhand. During Event-1, the observed CO mixing ratio over Dehradun increased from 25 April 2016 onwards, attained maximum (705.8 ± 258 ppbv) on 2 May 2016 and subsequently decreased. The rate of increase of daily baseline CO was 29 ppbv/day during HFAP (High Fire Activity Period). During Event-2, daily average concentrations of CO, O3, and NOx showed systematic increase over Dehradun during HFAP period. The rate of increase of CO was 9 ppbv/day, while it was very small for NOx and O3. To quantitatively estimate the influence of forest fire emissions, two WRF-Chem simulations were made: one with biomass burning (BB) emissions and other without BB emissions. These simulations showed 52% (34%) enhancement in CO, 52% (32%) enhancement in NOx, and 11% (9%) enhancement in O3 during HFAP for Event-1 (Event-2). A clear positive correlation (r = 0.89 for Event-1, r = 0.69 for Event-2) was found between ?O3 (O3with BB minus O3without BB) and ?CO (COwith BB minus COwithout BB), indicating rapid production of ozone in the fire plumes. For both the events, the vertical distribution of ?O3, ?CO, and ?NOx showed that forest fire emissions influenced the air quality upto 6.5 km altitude. Peaks in ?O3, ?CO, and ?NOx during different days suggested the role of varying dispersion and horizontal mixing of fire plumes. 相似文献
9.
Cristina-Maria Iordan Xiangping Hu Anders Arvesen Pekka Kauppi Francesco Cherubini 《Carbon balance and management》2018,13(1):12
Background
Forests and forest products can significantly contribute to climate change mitigation by stabilizing and even potentially decreasing the concentration of carbon dioxide (CO2) in the atmosphere. Harvested wood products (HWP) represent a common widespread and cost-efficient opportunity for negative emissions. After harvest, a significant fraction of the wood remains stored in HWPs for a period that can vary from some months to many decades, whereas atmospheric carbon (C) is immediately sequestered by vegetation re-growth. This temporal mismatch between oxidation of HWPs and C uptake by vegetation generates a net sink that lasts over time. The role of temporary carbon storage in forest products has been analysed and debated in the scientific literature, but detailed bottom-up studies mapping the fate of harvested materials and quantifying the associated emission profiles at national scales are rare. In this work, we quantify the net CO2 emissions and the temporary carbon storage in forest products in Norway, Sweden and Finland for the period 1960–2015, and investigate their correlation. We use a Chi square probability distribution to model the oxidation rate of C over time in HWPs, taking into consideration specific half-lives of each category of products. We model the forest regrowth and estimate the time-distributed C removal. We also integrate the specific HWP flows with an emission inventory database to quantify the associated life-cycle emissions of fossil CO2, CH4 and N2O.Results
We find that assuming an instantaneous oxidation of HWPs would overestimate emissions of about 1.18 billion t CO2 (cumulative values for the three countries over the period 1960–2015).We also find that about 40 years after 1960, the starting year of our analysis, are sufficient to detect signs of negative emissions. The total amount of net CO2 emissions achieved in 2015 are about ??3.8 million t CO2, ??27.9 t CO2 and ??43.6 t CO2 in Norway, Sweden, and Finland, respectively.Conclusion
We argue for a more explicit accounting of the actual emission rates from HWPs in carbon balance studies and climate impact analysis of forestry systems and products, and a more transparent inclusion of the potential of HWP as negative emissions in perspective studies and scenarios. Simply assuming that all harvested carbon is instantaneously oxidized can lead to large biases and ultimately overlook the benefits of negative emissions of HWPs.10.
《International Journal of Digital Earth》2013,6(1):54-84
ABSTRACTThe Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature. 相似文献
11.
Biomass burning from vegetation fires is an important source of greenhouse gas emissions. In this study, we quantify biomass burning emissions from grasslands from the highly sensitive Kaziranga National Park, Assam, Northeast India. Most of the fires in the park are ‘controlled burning fires’ set by the park officials for management purposes. We evaluated the short-term impacts of fires and the resulting air pollution through integrating biomass burnt information from satellite remote sensing datasets. IRS-P6 Advanced Wide Field Sensor (AWiFS) data during March and April corresponding to dry season were evaluated to delineate the burnt areas. These burnt area estimates were then integrated with biomass data and emission factors for quantifying the greenhouse gas emissions. Results suggested that of the total study area of 37,822 ha, nearly 3163.282 ha has been burnt during March, 2005. Within one month, the burnt area increased to 7443.92 ha by April, i.e., from 8.36% to 19.68%. In total, biomass burning from the grasslands contributed to 29.65 Tg CO2, 1.19 Tg CO, 0.071 Tg NOx, 0.042 Tg CH4, 0.0625 Tg total non-methane hydrocarbons, 0.152 Tg of particulate matter, and 0.062 Tg of organic carbon and 0.008 Tg of black carbon during April. The importance of ‘fire’ as a management tool for maintaining the wildlife habitat has been highlighted in addition to some of the adverse affects of air pollution resulting from such management practices. The results from this study will be useful to forest officials as well as policy makers to undertake some sustainable forest management practices to maintain an ideal habitat for Kaziranga's wildlife. 相似文献
12.
森林火灾既严重影响森林生态系统的稳定,还威胁到人类生命财产安全。传统监测森林火灾方法,覆盖范围小,难以及时监测小面积火灾。遥感卫星能大范围精确监测火情,提高了监测方法的时效性,但使用单一卫星数据源很容易受到云雨等客观环境因素影响,降低监测的时效性。本文以四川木里藏族自治县"330森林火灾"区域为对象,开展多源卫星遥感数据对小范围火灾联合监测的研究。首先,充分挖掘高分四号高时空分辨率和中红外火烧敏感波段优势,联合烟幕、温度和植被指数时序变化确定火烧时间与位置;然后,使用Sentinel-2数据监测不同火烧区域光谱信息;接着,使用Sentinel-2数据提取dNBR(differenced Normalized Burn Ratio),提出了基于最大类间方差算法(OTSU)分步骤确定不同程度火烧迹地与面积的方法;最后,建立Sentinel-1A极化比值PR (Polarization Ratio)和NDVI之间关系,利用微波雷达突破云雨限制。结果表明:(1)高分四号联合IRS(InfraRed Scanner)和PMS(Panchromatic Multispectral Sensor)能够实时监测小范围火灾;(2)根据火点位置,确定火灾蔓延期间NDVI下降(由0.7降低至0.25),确定起火时间(3月30日);(3)火灾区域与未受灾区,以及不同类型火烧迹地之间的光谱在490—2200 nm范围存在差异;(4)基于OTSU算法自动确定阈值,确定林地损失面积41.56公顷(dNBR=0.35),精度达94.67%,提取林地过火未损失面积66.56公顷(dNBR=0.10),精度达90.94%,林地损失区域基本符合实际调查结果;(5)火灾前后极化比值由6.6 dB升高至10.8 dB,NDVI与PR经线性回归,R2=0.58,验证R2=0.50。联合多源卫星监测森林火灾,能提高森林火灾监测的时效性,避免了云雨等复杂环境的影响。研究成果能为小火点的及时识别和灾害评估提供参考,其应用可为林火应急响应提供技术支撑。 相似文献
13.
Satellite-based atmospheric CO2 observations have provided a great opportunity to improve our understanding of the global carbon cycle. However, thermal infrared (TIR)-based satellite observations, which are useful for the investigation of vertical distribution and the transport of CO2, have not yet been studied as much as the column amount products derived from shortwave infrared data. In this study, TIR-based satellite CO2 products – from Atmospheric Infrared Sounder, Tropospheric Emission Spectrometer (TES), and Thermal And Near infrared Sensor for carbon Observation – and carbon tracker mole fraction data were compared with in situ Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) data for different locations. The TES CO2 product showed the best agreement with CONTRAIL CO2 data resulting in R2 ~ 0.87 and root-mean-square error ~0.9. The vertical distribution of CO2 derived by TES strongly depends on the geophysical characteristics of an area. Two different climate regions (i.e., southeastern Japan and southeastern Australia) were examined in terms of the vertical distribution and transport of CO2. Results show that while vertical distribution of CO2 around southeastern Japan was mainly controlled by horizontal and vertical winds, horizontal wind might be a major factor to control the CO2 transport around southeastern Australia. In addition, the vertical transport of CO2 also varies by region, which is mainly controlled by anthropogenic CO2, and horizontal and omega winds. This study improves our understanding of vertical distribution and the transport of CO2, both of which vary by region, using TIR-based satellite CO2 observations and meteorological variables. 相似文献
14.
Mark Broich Matthew C. Hansen Peter Potapov Bernard Adusei Erik Lindquist Stephen V. Stehman 《International Journal of Applied Earth Observation and Geoinformation》2011
Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided free of charge by the U.S. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely forest cover loss updates from regional to biome scales. The Indonesian islands of Sumatra and Kalimantan are a center of significant forest cover change within the humid tropics with implications for carbon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational coverage compared to other centers of humid tropical forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud cover obscuring the land surface. At the same time, forest change in Indonesia is transient and does not always result in deforestation, as cleared forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quantify change, are one option for monitoring forest change in cloudy regions. However, the frequency of forest cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occurring within the composite period itself. In this paper, we analyzed all Landsat 7 imagery with <50% cloud cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify forest cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping forest cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized by highly unequal observation counts and frequencies due to persistent cloud cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross forest cover loss and expert mapped gross forest cover loss at 64 sample sites. The mapped gross forest cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86 Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces. 相似文献
15.
Background
Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO2 emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO2 emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO2 emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.Results
This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year?1 and subsequently caused a carbon loss of approximately 9 million Mg C year?1, which is equal to emissions of 33 million Mg CO2 year?1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha?1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.Conclusion
The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+?implementation in Malaysia.16.
森林火灾是最为常见的灾害之一,严重危及人类生命安全。及时准确监测森林火灾的发生及火场状况,对应对火灾及减少损失至关重要。当前,森林火灾卫星遥感监测主要以低空间分辨率的卫星遥感为主,空间分辨率过低导致无法探测规模较小火灾及掌握详细火场态势。针对这一问题,结合近些年中高空间分辨率卫星观测、共享及处理能力的发展,本文从森林火灾卫星遥感监测的基本原理、当前可用中高空间分辨率卫星数据及其特点、中高分辨率森林着火区监测算法,以及数据共享与云端存储与计算等4个技术环节,对森林火灾中高分辨率卫星遥感监测当前研究现状与存在问题进行了总结,阐述了近实时中高空间分辨率森林火灾监测系统的可行性。近实时中高空间分辨率森林火灾监测系统可对已有低空间分辨率森林火灾监测体系形成重要补充,依托其空间分辨率的优势有助于及早、准确发现小规模火情,进而为森林火灾的防治与管理提供更好支撑。 相似文献
17.
卫星观测不仅能反映区域宏观大气污染状况,也能从城市尺度上监测大气污染物的变化。基于以上优势,本文利用MODIS气溶胶光学厚度(AOD)和OMI对流层NO_2垂直柱浓度数据,比较2015年与2012年—2014年以及2015年3个时期(减排前、减排中、减排后)AOD和NO_2柱浓度的变化,定性分析了阅兵期间华北平原地区污染物减排效果,重点定量评估北京市联控减排措施的效果。研究发现2015年减排中华北平原重污染地区AOD和NO_2柱浓度相比于前3年同期有明显降低。定量分析北京市的减排效果得到:2015年减排中较前3年同期而言,AOD降低59%,NO_2柱浓度降低41%;较2015年减排前而言,AOD降低73%,NO_2柱浓度降低30%,去除气象条件影响后,AOD下降43%,NO_2柱浓度下降21%,说明严格的联控减排措施有效地改善了空气质量,气象条件也起到积极的作用。减排措施结束后,AOD和NO_2柱浓度比减排中分别增加159%和71%。研究结果表明,卫星遥感与地基监测评估效果相当,能反映北京地面污染物排放能力;它既能观测区域尺度大气污染变化,又可评估城市尺度大气污染减排。随着卫星技术水平的提高,期望未来卫星遥感可作为一种独立手段来定量评估区域及城市尺度空气质量减排措施的效果。 相似文献
18.
Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1). 相似文献
19.
中国地区大气CO2浓度对全球气候变化有重要的影响。本文基于日本GOSAT卫星短波红外CO2的长期观测数据,对2010年-2016年中国大陆上空CO2浓度的分布特征和变化趋势进行分析研究。利用高精度的地基TCCON站点CO2观测对GOSAT CO2数据进行精度验证,结果表明,GOSAT CO2具有较高的精度,相对TCCON CO2的偏差为-1.04±2.10 ppm,两者的相关系数高达0.90;利用中国地区7年的GOSAT CO2观测数据分析研究显示,CO2浓度高值主要分布在中国的浙江-江苏-安徽地区、京津冀地区和湖南-湖北-河南-陕西地区;截至2016年,中国大部地区CO2浓度超过400 ppm;中国大陆CO2平均浓度呈现明显的逐年增长趋势,从2010年的387.76 ppm增长到2016年的402.18 ppm,年增长率约为2.31 ppm/a,略高于同期全球平均水平。 相似文献
20.
Stutee Gupta Arijit Roy Dhruval Bhavsar Rajkanti Kala Sarnam Singh A. Senthil Kumar 《Journal of the Indian Society of Remote Sensing》2018,46(6):945-955
The hills of Uttarakhand witness forest fire every year during the summer season and the number of these fire events is reported to have increased due to increased anthropogenic disturbances as well as changes in climate. These fires cause significant damage to the natural resources which can be mapped and monitored using satellite images by virtue of its synoptic coverage of the landscape and near real time monitoring. This study presents burnt area assessment caused by the fire episode of April 2016 to the forest vegetation. Digital classification of satellite images was done to extract the burnt area which was found to be 3774.14 km2, representing 15.28% of the total forest area of the state. It also gives an account of cumulative progression of forest fire in Uttarakhand using satellite images of three dates viz. 23rd, 27th May and 2nd June, 2016. Results were analyzed at district, administrative and forest division level using overlay analysis. Separate area statistics were given for different categories of biological richness, forest types and protected areas affected by forest fire. The burnt area assessment can be used in mitigation planning to prevent drastic ecological impacts of the forest fire on the landscape. 相似文献