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Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing 被引量:2
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作者 WANG Xuecheng YANG Fei +2 位作者 GAO Xing WANG Wei ZHA Xinjie 《Chinese Geographical Science》 SCIE CSCD 2019年第3期405-416,共12页
The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged sever... The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer(MODIS)13 Q1 products are used, which include two vegetation indices data of NDVI(Normalized Difference Vegetation Index) and EVI(Enhanced Vegetation Index). Furtherly, after Quality Screening(QS) and Savizky-Golay(S-G) filtering of MODIS 13 Q1 data, four evaluation indices are obtained, which are NDVI with QS(QSNDVI), EVI with QS(QSEVI), NDVI with S-G filtering(SGNDVI) and EVI with S-G filtering(SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters. 展开更多
关键词 ice-snow DISASTER vegetation index forest remote sensing SOUtheRN China
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Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots
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作者 Alexander S. Alekseev Dmitry M. Chernikhovskii 《Open Journal of Ecology》 2022年第8期513-528,共16页
The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied... The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures. 展开更多
关键词 remote sensing Sentinel-2B Imagery ICP-forest Sample Plot Tree Stand Damage Class k-NN (Nearest Neighbor Method) Vegetation index SWVI Nonlinear Regression Systematic Error Random Error
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STUDY ON FOREST FIRE DANGER MODEL WITH REMOTE SENSING BASED ON GIS 被引量:1
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作者 Fang Huang Xiang-nan Liu Jin-guo Yuan 《Chinese Geographical Science》 SCIE CSCD 2000年第1期62-68,共7页
Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. A... Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. As fire prevention is probably the most efficient means for protecting forests, suitable methods should be developed for estimating the fire danger. Fire danger is composed of ecological, human and climatic factors. Therefore, the systematic analysis of the factors including forest characteristics, meteorological status, topographic condition causing forest fire is made in this paper at first. The relationships between biophysical factors and fire danger are paid more attention to. Then the parameters derived from remote sensing data are used to estimate the fire danger variables, According to the analysis, not only PVI (Perpendicular Vegetation Index) can classify different vegetation but also crown density is captured with PVI. Vegetation moisture content has high correlation with the ratio of actual evapotranspiration (LE) to potential ecapotranspiration (LEp). SI (Structural Index), which is the combination of TM band 4 and 5 data, is a good indicator of forest age. Finally, a fire danger prediction model, in which relative importance of each fire factor is taken into account, is built based on GIS. 展开更多
关键词 forest fire DANGER index models for DANGER prediction INVERSION of remote sensing data OVERLAY analysis GEOGRAPHICAL information system(GIS)
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Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China 被引量:12
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作者 LI Zhen-wang XIN Xiao-ping +3 位作者 TANG Huan YANG Fan CHEN Bao-rui ZHANG Bao-hui 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第2期286-297,共12页
Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling bio... Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of deci- sion trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable re- duction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the vari- ables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models. 展开更多
关键词 leaf area index Random forests grassland remote sensing Hulunber
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Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images
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作者 Coşkun Okan Güney Ahmet Mert Serkan Gülsoy 《Journal of Forestry Research》 SCIE CAS CSCD 2023年第6期1747-1761,共15页
Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire char... Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study. 展开更多
关键词 remote sensing forest fire Fire severity Spectral indices Composite burn index
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Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing
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作者 ZHANG Zheyuan WANG Jia +2 位作者 XIONG Nina LIANG Boyi WANG Zong 《Chinese Geographical Science》 SCIE CSCD 2023年第2期320-332,共13页
Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing ai... Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters. 展开更多
关键词 air quality index(AQI) population pollution exposure nighttime light remote sensing Luojia-1 random forest
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Identification of Forest Vegetation Using Vegetation Indices 被引量:1
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作者 Yuan Jinguo & Wang Wei College of Resource and Environmental Sciences, Hebei Normal University, Shijiazhuang 050016, China 《Chinese Journal of Population,Resources and Environment》 2004年第4期12-16,共5页
Spectral feature of forest vegetation with remote sensing techniques is the research topic all over the world, because forest plays an important role in human beings' living environment. Research on vegetation cla... Spectral feature of forest vegetation with remote sensing techniques is the research topic all over the world, because forest plays an important role in human beings' living environment. Research on vegetation classification with vegetation index is still very little recently. This paper proposes a method of identifying forest types based on vegetation indices, because the contrast of absorbing red waveband with reflecting near-infrared waveband strongly for different vegetation types is recognized as the theoretic basis of vegetation analysis with remote sensing. Vegetation index is highly related to leaf area index, absorbed photosynthetically active radiation and vegetation cover. Vegetation index reflects photosynthesis intensity of plants and manifests different forest types. According to reflectance data of forest canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun of China, many vegetation indices are calculated and analyzed. The result shows that the relationships between 展开更多
关键词 forest vegetation IDENTIFICATION vegetation index remote sensing
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长白山区4种针叶林有效叶面积指数遥感精细反演及空间分布规律
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作者 包广道 刘婷 +4 位作者 张忠辉 任志彬 翟畅 丁铭铭 姜雪菲 《林业科学》 EI CAS CSCD 北大核心 2024年第5期127-138,共12页
[目的]研究快速、准确、宏观获取不同森林类型有效叶面积指数(LAI_(e))的方法,探讨其空间分布规律,为中小尺度森林LAI_(e)遥感产品的开发提供新思路,为林业精细化监测和森林生态系统碳水循环模拟提供科学可靠的技术手段。[方法]以长白... [目的]研究快速、准确、宏观获取不同森林类型有效叶面积指数(LAI_(e))的方法,探讨其空间分布规律,为中小尺度森林LAI_(e)遥感产品的开发提供新思路,为林业精细化监测和森林生态系统碳水循环模拟提供科学可靠的技术手段。[方法]以长白山为研究区,基于Sentinel-2A多光谱影像,运用三维卷积神经网络提取研究区4种针叶林型(长白落叶松、樟子松、红松和红皮云杉)的空间分布;采用区分林型和全样本2种方案,分析样地实测LAI_(e)与7种植被指数(增强植被指数、反红边叶绿素指数、改进简单植被指数、归一化水体指数、归一化植被指数、土壤调节植被指数、简单植被指数)的相关关系;利用各林型对应的最优植被指数,构建区分林型和全样本LAI_(e)与植被指数的回归模型,并基于验证样本数据对比区分林型模型、全样本模型和PROSAIL模型在LAI_(e)反演中的精度表现;结合地理因子分析4种针叶林型LAI_(e)空间格局及变化规律。[结果]所有样本组中7种植被指数与相对的LAI_(e)均存在极显著相关关系(P<0.01),除增强植被指数(EVI)与红松LAI_(e)、简单植被指数(SR)与红皮云杉LAI_(e)外,相关系数均大于0.6,但组间LAI_(e)与不同植被指数相关性具有较大差异;红松、长白落叶松和樟子松LAI_(e)与反红边叶绿素指数(IRECI)相关性最高,红皮云杉、红松LAI_(e)分别与EVI、改进简单植被指数(MSR)相关性最高;4种不同林型模型比全样本模型的R2提高12.7%以上,RMSE降低34.5%;研究区内4种林型LAI_(e)范围在0.37~5.86之间,平均LAI_(e)由高至低依次为红松、长白落叶松、樟子松、红皮云杉。红松对海拔、坡度、坡向的变化最为敏感,红皮云杉、樟子松次之,长白落叶松最小。[结论]不同林型LAI_(e)与遥感植被指数的相关程度存在明显差异,区分林型构建回归模型能够提高LAI_(e)反演精度;区分林型后拟合的线性模型精度整体较PROSAIL模型和全样本模型更高,但LAI_(e)高值区域没有PROSAIL模型表现稳定;4种针叶林型LAI_(e)对地理因子变化的反应差异较大。本研究可为精细区分森林类型的中小尺度针叶林LAI_(e)遥感反演研究提供参考。 展开更多
关键词 有效叶面积指数 针叶树种 森林类型 卫星遥感 空间分布规律
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基于遥感生态指数的广东石门台国家级自然保护区生态环境质量评价 被引量:1
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作者 黎毅 戴克元 +5 位作者 唐国平 杜建会 陈桃 江南 牛香豫 余扬波 《热带地理》 CSCD 北大核心 2024年第3期429-441,共13页
基于遥感生态指数(RSEI)定量评估了1997—2021年石门台自然保护区的生态环境质量变化,探究该自然保护区建立与管控级别提升前后的生态环境质量变化情况,并运用随机森林算法和相关性分析方法探讨演变的原因。结果表明:石门台自然保护区R... 基于遥感生态指数(RSEI)定量评估了1997—2021年石门台自然保护区的生态环境质量变化,探究该自然保护区建立与管控级别提升前后的生态环境质量变化情况,并运用随机森林算法和相关性分析方法探讨演变的原因。结果表明:石门台自然保护区RSEI值从1997年的0.637动态上升到2011年的0.714,随着自然保护区从省级升至国家级,生态环境质量稳步提高,2021年RSEI达到0.788;保护区生态环境质量变好的区域占比高达64.5%,其中,缓冲区改善较为明显,变差的区域主要集中在实验区人类活动频繁地区以及核心区高海拔山脊地带;RSEI对高程的响应最为明显,300~600 m生态环境质量最好,300 m以下和超过900 m生态环境质量相对较差;当土壤酸碱度为5.3、有机碳质量分数为4.1%、黏土质量分数为32%时,RSEI最高;居民活动从外向内(即实验区-缓冲区-核心区)对生态环境质量的影响程度逐渐降低且带来负面影响;时间上,RSEI与降水呈现正相关性,有23.6%的区域通过显著性检验(P<0.05),局部年份受降水的影响较大。总体上,石门台自然保护区生态环境质量变好趋势明显,今后应加强对高山顶部裸土区域以及低海拔人类活动频繁地区的保护和治理。 展开更多
关键词 生态环境质量 遥感生态指数 时空变化 随机森林 石门台自然保护区
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低覆盖草地叶面积指数遥感估算方法 被引量:1
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作者 张云峰 任鸿瑞 《草业科学》 CAS CSCD 北大核心 2024年第3期588-598,共11页
有效估算低覆盖草地叶面积指数(LAI),对监测低覆盖草地生长状况、优化完善草地管理具有重要意义。以往针对草地叶面积指数的研究大多集中于中高覆盖度草地,对低覆盖草地的研究相对较少。利用谷歌地球引擎(GEE),基于Landsat-8卫星数据提... 有效估算低覆盖草地叶面积指数(LAI),对监测低覆盖草地生长状况、优化完善草地管理具有重要意义。以往针对草地叶面积指数的研究大多集中于中高覆盖度草地,对低覆盖草地的研究相对较少。利用谷歌地球引擎(GEE),基于Landsat-8卫星数据提取所需特征变量,通过特征变量与叶面积指数的相关性及其在模型中的重要性进行特征优选,确定模型最佳变量个数,以此构建机器学习模型,探寻适合在低覆盖区草地估算叶面积指数的方法。结果显示,基于相关性特征优选的梯度提升回归树模型(r-GBRT)在低覆盖草地估算叶面积指数的效果较好,测试集的R 2为0.686,均方根误差(RMSE)为0.101。结果表明,基于特征优选构建的机器学习模型在低覆盖条件下估算草地叶面积指数方面具有较好的应用价值。 展开更多
关键词 叶面积指数 低覆盖草地 机器学习 特征优选 随机森林 梯度提升回归树 遥感
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湛江湾水体颗粒物后向散射特性及其遥感反演研究
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作者 余果 钟雅枫 +2 位作者 付东洋 刘大召 徐华兵 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2024年第3期793-799,共7页
湛江湾2018年1月的原位调查,获得了原位遥感反射率(Rrs)、颗粒物后向散射系数(b_(bp))、叶绿素a(Chl a)和无机悬浮颗粒物(ISM)浓度等参数,分析了湛江湾水体颗粒物后向散射特性,并对颗粒物后向散射系数进行了遥感反演研究。研究结果显示... 湛江湾2018年1月的原位调查,获得了原位遥感反射率(Rrs)、颗粒物后向散射系数(b_(bp))、叶绿素a(Chl a)和无机悬浮颗粒物(ISM)浓度等参数,分析了湛江湾水体颗粒物后向散射特性,并对颗粒物后向散射系数进行了遥感反演研究。研究结果显示:在研究区域观察到表层水体6个波段(420, 442, 470, 510, 590和700 nm)颗粒物后向散射系数的变异系数均在50%~60%之间,其变化范围为0.026 1~0.211 2 m^(-1),这意味着水体光学性质的复杂性。为了更好地量化b_(bp)的光谱特性,研究以510 nm为参考波段构建了b_(bp)幂函数光谱模型,获得的光谱模型斜率指数n=1.55。研究发现b_(bp)(510)与ISM呈现乘幂关系,与颗粒物组成(Chla/ISM)呈现指数关系,决定系数R2分别为0.74和0.62,表明研究区域颗粒物后向散射系数一阶驱动因子主要为无机悬浮颗粒物浓度,二阶驱动因子颗粒物组成对b_(bp)(510)变异也具有重要的贡献。为了准确估算湛江湾颗粒物后向散射系数,研究基于原位遥感反射率构建了随机森林模型,并与QAA-v6、 QAA-RGB和QAA-705三种半分析算法进行对比。随机森林模型的R2为0.86,平均绝对百分比误差MAPE为12%,均方根误差RMSE为0.02 m^(-1), QAA-v6、 QAA-RGB和QAA-705三种半分析算法R2分别为0.63、 0.71和0.53, MAPE分别为186%、 117%和243%, RMSE分别为0.16、 0.09和0.18 m^(-1),三种半分析算法虽然也具有较高的R2,但估计值和测量值之间存在显著差异,且MAPE和RMSE也较大,三种半分析算法显著低于随机森林方法的反演精度,表明运用遥感反演湛江湾b_(bp),随机森林方法具有较大的应用潜力。 展开更多
关键词 颗粒物后向散射特性 遥感反演 湛江湾 随机森林
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不同尺度的土壤含水量主被动微波联合反演方法研究
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作者 刘奇鑫 顾行发 +2 位作者 王春梅 杨健 占玉林 《地学前缘》 EI CAS CSCD 北大核心 2024年第2期42-53,共12页
土壤含水量是水文、农业和气象等领域的关键参数,而微波遥感是目前监测土壤含水量最有效的手段之一。本文利用主动微波与被动微波数据,结合其他多源遥感数据,运用随机森林算法分别在主动微波数据分辨率尺度和被动微波数据分辨率尺度下... 土壤含水量是水文、农业和气象等领域的关键参数,而微波遥感是目前监测土壤含水量最有效的手段之一。本文利用主动微波与被动微波数据,结合其他多源遥感数据,运用随机森林算法分别在主动微波数据分辨率尺度和被动微波数据分辨率尺度下完成主被动微波数据的土壤含水量联合反演。首先对被动微波尺度的地表覆盖类型与归一化植被指数(NDVI)参数进行空间分辨率优化,再利用回归ReliefF方法对两种尺度所用的输入变量的重要性进行评估,并对输入变量进行优选,最后对比主被动微波数据土壤含水量联合反演和单独利用主动/被动微波数据进行反演的精度,分析主被动微波联合反演方法的有效性。结果表明:在主动微波尺度,主被动微波联合反演的精度相比单独利用主动微波数据反演的精度有所提升,相关系数r由0.691升至0.744,RMSE由0.0848 cm^(3)/cm^(3)降至0.0796 cm^(3)/cm^(3);在被动微波尺度,主被动微波联合反演的精度反而比单独利用被动微波数据反演的精度更低,相关系数r由0.944变为0.939,RMSE由0.0435 cm^(3)/cm^(3)变为0.0451 cm^(3)/cm^(3)。因此在主动微波尺度更适合进行主被动微波的联合反演。 展开更多
关键词 土壤含水量 微波遥感 联合反演 随机森林 空间优化 重要性评估
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森林草原火灾遥感监测预警技术及示范应用
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作者 何彬彬 陈瑞 +5 位作者 全兴文 姚劲松 殷长明 王子励 张启明 杨帅 《电子科技大学学报》 EI CAS CSCD 北大核心 2024年第5期698-705,共8页
提升森林草原火灾综合防控与应急响应的能力,达到减少森林草原火灾发生和火情快速动态监测的目的,关键在于发展基于卫星遥感、地理空间信息、计算机、林火生态等多学科交叉融合的火灾监测预警信息化、智能化理论与方法。基于可燃物遥感... 提升森林草原火灾综合防控与应急响应的能力,达到减少森林草原火灾发生和火情快速动态监测的目的,关键在于发展基于卫星遥感、地理空间信息、计算机、林火生态等多学科交叉融合的火灾监测预警信息化、智能化理论与方法。基于可燃物遥感定量反演和时空大数据挖掘等方法,构建灾前-灾时-灾后的森林草原火灾遥感监测预警技术体系,包括“可燃物-气象-地形”多源时空大数据协同的森林草原火险预报预警技术、多源卫星数据协同的高精度火点快速检测技术和灾情精准评估技术。针对我国西南地区实际业务需求,定制化研发森林草原火灾监测预警系统,集成火灾监测预警关键理论与技术,实现灾前早期风险预警、灾时近实时监测、灾后灾情精准评估等功能。该系统可为森林草原火灾监测预警提供便捷、高效的动态信息服务,其应用成效显著,具有大范围推广应用的潜力。 展开更多
关键词 森林草原火灾 火险预报预警 火点检测 燃烧烈度 时空大数据 遥感反演 可燃物
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基于Landsat 8遥感数据的森林火灾过火面积估算——以贵州毕节市赫章县“3·18”火灾为例
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作者 董奎 董平 陈兰 《林业调查规划》 2024年第1期187-191,共5页
以贵州省毕节市赫章县2021年3月18日较大森林火灾为例,利用火灾前后Landsat 8遥感数据及ENVI遥感数据处理分析软件,通过图像预处理、计算归一化植被指数和燃烧面积指数等方法,提取森林火灾过火区域,计算过火面积。结果表明,利用Landsat ... 以贵州省毕节市赫章县2021年3月18日较大森林火灾为例,利用火灾前后Landsat 8遥感数据及ENVI遥感数据处理分析软件,通过图像预处理、计算归一化植被指数和燃烧面积指数等方法,提取森林火灾过火区域,计算过火面积。结果表明,利用Landsat 8数据能够较好地提取森林火灾过火区域,过火面积估算准确率达96.2%。 展开更多
关键词 Landsat 8遥感数据 森林火灾 过火面积 归一化植被指数 燃烧面积指数
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The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing 被引量:5
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作者 FAN WenJie GAI YingYing +1 位作者 XU XiRu YAN BinYan 《Science China Earth Sciences》 SCIE EI CAS 2013年第9期1548-1554,共7页
The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scali... The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery. 展开更多
关键词 the spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing
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连云港主要入海河流叶绿素a及综合营养状态指数遥感定量反演 被引量:1
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作者 王林 王祥 +3 位作者 周超 王新新 孟庆辉 陈艳拢 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2023年第10期3314-3320,共7页
入海河流是陆源污染物向海洋输送过程的关键环节,其富营养化对区域生态环境及人类生活安全构成严重威胁。由于习近平生态文明思想的深入贯彻,“河长制”、“湾长制”等管理制度的广泛推行,以及污染防治攻坚战的全面打响,入海河流与近岸... 入海河流是陆源污染物向海洋输送过程的关键环节,其富营养化对区域生态环境及人类生活安全构成严重威胁。由于习近平生态文明思想的深入贯彻,“河长制”、“湾长制”等管理制度的广泛推行,以及污染防治攻坚战的全面打响,入海河流与近岸海域的水质状况得到稳步改善,但仍存在波动,污染防治形势依然严峻,实时有效的大范围遥感监测能力亟待加强。随着近年来高分卫星与无人机遥感技术的迅速发展,如何将定量遥感技术应用于河流水体的生态环境要素监测,从而推进污染防治水平进一步提升已成为该领域研究热点。基于2022年6月期间在连云港境内蔷薇河、临洪河、古泊善后河及灌河获取的叶绿素a、总磷、总氮浓度等实测数据,并采用Sentinel-2A MSI L2A卫星影像,开展了连云港主要入海河流叶绿素a及综合营养状态指数[TLI(Σ)]遥感定量反演研究。结果表明,叶绿素a浓度、综合营养状态指数与可见光波段反射率的相关性明显高于其他波段,尤以490、560、665 nm三个波段最佳,其中R(λ)与Chl a的相关系数分别为-0.697、-0.681、-0.693,R(λ)与TLI(Σ)的相关系数分别为-0.728、-0.744、-0.706,可作为建模的敏感波段;经反演模型的精度对比发现,以R(665)为自变量,在叶绿素a浓度对数坐标下的乘幂模型为其遥感定量反演的最优模型(R^(2)=0.67,MAPE=47.34%,RMSE=12.89μg·L^(-1)),而以R(560)为自变量的乘幂模型是综合营养状态指数遥感定量反演的最优模型(R^(2)=0.61,MAPE=4.36%,RMSE=3.45);将最优模型应用于2022年6月25日Sentinel-2A MSI L2A影像,得到连云港主要入海河流叶绿素a浓度和综合营养状态指数的空间分布结果,发现蔷薇河/临洪河、古泊善后河及灌河均处于富营养化状态,叶绿素a浓度和综合营养状态指数均以蔷薇河/临洪河最高,古泊善后河次之,灌河最低,且河流上游反演结果普遍高于下游。 展开更多
关键词 叶绿素A 综合营养状态指数 遥感定量反演 入海河流 连云港
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基于退耕还林工程的生态环境质量动态变化研究——以云南兰坪县为例 被引量:3
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作者 李益敏 李盈盈 +2 位作者 刘师旖 吴博闻 赵娟珍 《环境工程技术学报》 CAS CSCD 北大核心 2023年第1期359-367,共9页
2014年云南省怒江傈僳族自治州(简称怒江州)正式启动新一轮退耕还林还草工程,为探讨该轮退耕还林工程对怒江州兰坪白族普米族自治县(简称兰坪县)生态环境质量的影响,选择退耕还林工程实施前(2013年)、实施中(2017年)和实施末期(2020年)... 2014年云南省怒江傈僳族自治州(简称怒江州)正式启动新一轮退耕还林还草工程,为探讨该轮退耕还林工程对怒江州兰坪白族普米族自治县(简称兰坪县)生态环境质量的影响,选择退耕还林工程实施前(2013年)、实施中(2017年)和实施末期(2020年)3个时间,基于遥感生态指数模型,选取绿度、干度、湿度和热度4个指标,运用主成分分析法对兰坪县2013—2020年的生态环境质量动态变化进行研究。结果表明:1)2013—2020年兰坪县生态环境质量处于上升趋势,中排乡、石登乡和河西乡的生态环境质量改善较为明显;2)2013—2020年,75%以上区域的生态环境质量等级保持不变,其他区域的生态环境质量等级呈小幅度变化,生态环境质量变好的区域面积要大于生态环境质量变差的区域面积;3)退耕还林斑块的生态环境质量提升程度要高于退耕还林2 km缓冲区以及整个研究区的生态环境质量提高程度,退耕还林工程对生态环境质量的改善具有显著影响,可通过进一步推进退耕还林工程来改善兰坪县的生态环境质量;4)研究区潜在的退耕区主要分布在中排乡、石登乡、营盘镇和兔峨乡4个乡镇,其可作为进一步退耕还林的重点区域。 展开更多
关键词 生态环境质量 退耕还林工程 遥感生态指数(RSEI)模型 主成分分析 动态变化
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基于Sentinel-3 OLCI数据的渤海水色状况时空变化特征
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作者 王林 王祥 +4 位作者 陈艳拢 高思雯 曾怡乐 孟庆辉 王新新 《中国环境科学》 EI CAS CSCD 北大核心 2023年第9期4828-4836,共9页
采用福莱尔水色计可将自然水体颜色从深蓝到红棕分为21个级别,用来记录全球海洋和内陆水体的颜色.目前,与福莱尔水色计21个级别颜色相对应的FUI(Forel-Ule index)水色指数已有成熟的卫星遥感提取方法.以渤海为研究区,利用Sentinel-3 OLC... 采用福莱尔水色计可将自然水体颜色从深蓝到红棕分为21个级别,用来记录全球海洋和内陆水体的颜色.目前,与福莱尔水色计21个级别颜色相对应的FUI(Forel-Ule index)水色指数已有成熟的卫星遥感提取方法.以渤海为研究区,利用Sentinel-3 OLCI卫星数据提取了2018~2022年近5a渤海月度、年度FUI水色指数,研究了水色状况的时空变化特征.结果表明,渤海FUI时空变化特征显著,辽东湾、渤海湾及莱州湾沿岸海域FUI较高,水体浑浊,水色状况较差;而秦皇岛海域及其它离岸海域FUI较低,水体相对清澈,水色状况较好.1~12月,渤海FUI月平均值呈“V”型变化特征,即FUI由大变小再变大,体现出渤海水色状况由浊变清再变浊的过程.2018~2022年,FUI年平均值整体呈现下降趋势,水色状况转好;2018~2019年FUI年平均值的降幅最高,渤海综合治理攻坚战初见成效,2020年降至攻坚战实施期间最低水平,2021年出现小幅回升,2022年FUI年平均值则降至历年最低;与2020年相比,2022年渤海水色状况转好、稳定、变差的海域面积占比分别为36.05%、50.45%、13.50%,达到近5a最佳状态,可见渤海综合治理攻坚战对其生态环境的改善发挥了重要作用.因此,利用遥感技术监测FUI水色指数可有效提升我国海洋生态环境常态化巡查监管能力,具有非常重要的实际应用价值. 展开更多
关键词 FUI水色指数 时空变化 Sentinel-3 OLCI数据 遥感反演 渤海
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基于FY-3D MERSIⅡ数据的辽宁省作物生长季日平均气温估算方法比较
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作者 王岩 汪利诚 +3 位作者 武晋雯 杨欣虹 尹佳琪 张梅 《灾害学》 CSCD 北大核心 2023年第2期89-96,105,共9页
作为反映气候特征的重要指标之一,日平均气温在农业气象灾害监测和气候变化研究等领域承担着至关重要的作用。与传统日平均气温的监测和估算方式相比,遥感技术具有全方位、宏观、动态等不可比拟的绝对优势,能够准确地描述日平均气温的... 作为反映气候特征的重要指标之一,日平均气温在农业气象灾害监测和气候变化研究等领域承担着至关重要的作用。与传统日平均气温的监测和估算方式相比,遥感技术具有全方位、宏观、动态等不可比拟的绝对优势,能够准确地描述日平均气温的空间异质性。为提高农业服务质量,保证农业健康、可持续发展,探索作物生长季日平均气温遥感反演方法,提高农业气象灾害监测精确度,以FY-3D MERSIⅡ遥感数据为基础,提取研究区日间地表温度(LST_(day))、夜间地表温度(LST_(night))、归一化植被指数(NDVI),同时还考虑了高程(DEM)、坡度(Slope)两个变量,结合气象站日平均气温数据,分别构建多元线性回归和随机森林日平均气温遥感反演模型,开展辽宁省2021年作物生长季(5—9月)日平均气温遥感监测的应用研究。结果表明:(1)基于多元线性回归模型反演的日平均气温均方根误差(RMSE)为1.71℃,平均绝对误差(MAE)为1.45℃;基于随机森林反演误差RMSE为1.17℃,MAE为0.96℃;整体上,随机森林的日平均气温反演结果更好,适用性更强。(2)实验当天和前1 d的降水总量对日平均气温的估算结果具有很大影响,降水量随时间的变化曲线与日平均气温的反演误差散点分布情况基本一致,呈现降水总量越大,日平均气温的反演误差越大的趋势,日平均气温反演结果受大气水汽含量的影响很大。(3)对输入的气温影响因子的重要性进行动态的统计分析,发现LST_(day)和DEM是日平均气温反演时两个最重要的变量,且LST_(day)对日平均气温反演的影响最为重要,但是随着作物的生长,DEM的重要性也越来越凸显。 展开更多
关键词 FY-3D MERSIⅡ 遥感反演 日平均气温 随机森林 多元回归 作物生长季 辽宁省
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中国森林遥感Rao′s Q指数时空演变 被引量:1
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作者 蒋啸 蔡红艳 +1 位作者 杨小唤 李果 《生态学报》 CAS CSCD 北大核心 2023年第8期3045-3056,共12页
明确宏观森林植物多样性时空分布格局,对于评估森林生态工程效果以及有效开展区域生物多样性保护工作具有重要意义。遥感生物多样性研究能够弥补原位观测在时空连续性方面的不足,研究旨在为区域尺度生物多样性研究引入一种新方法——遥... 明确宏观森林植物多样性时空分布格局,对于评估森林生态工程效果以及有效开展区域生物多样性保护工作具有重要意义。遥感生物多样性研究能够弥补原位观测在时空连续性方面的不足,研究旨在为区域尺度生物多样性研究引入一种新方法——遥感Rao′s Q指数。结果表明:基于归一化植被指数(NDVI)计算的Rao′s Q指数与收集的物种丰富度在空间分布上基本一致,且两者的数值拟合通过极显著检验(R^(2)=0.66,P<0.001),Rao′s Q指数随物种丰富度增加而增加,适用于表征宏观尺度跨气候带地区的森林植物多样性。2000—2017年间,中国森林的Rao′s Q指数时空分布基本一致,整体呈南高北低的态势;不同时段中均以降低为主,降低面积占总面积的44.23%—54.08%,武夷山-南岭一带增高特征凸显,而2010—2017年也表现出降低特征,该区域森林植物多样性本底丰富,应加强管控力度,阻止降低热点区扩散。引入遥感Rao′s Q指数有效表征区域森林植物多样性分布特征,可为区域生态质量评估提供一种新的监测指标,进而为森林植物多样性保护及区域生态质量改善相关政策制定提供支撑。 展开更多
关键词 森林植物多样性 遥感Rao′s Q指数 演变 遥感
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