南瓮河自然保护区生态环境质量遥感评价

运用遥感技术,科学、快速地评价区域生态环境质量,能够为区域生态环境的保护、治理和规划等提供科学依据.本研究以1990、2000和2015年的Landsat TM/OLI/TIRS为数据源,使用主成分分析方法确定湿度分量、归一化植被指数、地表温度和干度指数4个指标的权重,运用遥感生态指数(RSEI)评价模型,对南瓮河自然保护区1990—2015年的生态环境进行评价.结果表明: 遥感生态指数能够较好地反映研究区生态环境质量状况及其时空分异;1990—2015年,保护区 RSEI均值由0.55上升至0.83,生态环境质量优良以上区域面积所占百分比逐年增加,这与该等级中森林所占比例增加有关;保护区生态环境质量变好的区域所占比例高达91.4%,这与保护区的建立及相关工程与非工程防护措施的实施密切相关;核心区、缓冲区北部生态环境质量下降的主要原因是火灾的发生导致森林植被遭到破坏,而道路附近和试验区的东南部生态环境质量的下降与人类活动的干扰密不可分.     

城市地表温度与关键景观要素的关系

利用Landsat ETM+遥感影像,提取上海市外环线范围内的地表温度、不透水面率、归一化差值植被指数、改进的归一化差异水体指数,定量研究地表温度与城市关键景观类型之间的关系.结果表明:地表温度与不透水面率呈显著的线性正相关( R2=0.837);地表温度与归一化差值植被指数和改进的归一化差异水体指数呈非线性关系,但地表温度与正的归一化差值植被指数和正的改进的归一化差值水体指数呈显著线性关系.鉴于归一化差值植被指数和改进的归一化差异水体指数大于0时才能真正代表植被和水体,因此,建议今后研究地表温度时使用正的归一化差值植被指数和改进的归一化差异水体指数;地表温度与不透水面率、归一化差值植被指数和改进的归一化差值水体指数的多元线性回归分析表明,不透水面起着增温作用,植被、水体起降温作用,植被较水体的降温作用大.     

四川省九寨沟县地震前后区域生态环境质量评价

基于地理信息系统和遥感数据处理软件平台,对四川省阿坝藏族羌族自治州九寨沟县在2017年8·8地震前后的生态环境质量进行了监测和分析。利用Landsat 8 OLI卫星传感器所获得的2016年(震前)和2017年(震后)的遥感影像数据,提取归一化植被指数、湿度分量、地表温度、归一化土壤指数分别作为绿度、湿度、热度、干度等4个指标,进行主成分分析得到遥感生态指数。根据该指数,可将九寨沟县生态环境质量状况分为优、良、中等、较差和差级。2016年九寨沟县有24.8%处于生态环境质量状况优,2017年震后则下降到19.84%,2017年生态环境质量状况较差和中等的面积占比呈增加趋势,分别占17.99%和33.93%;震中15.5 km缓冲区内生态环境质量状况优的面积占比下降最多,为5.01%;2017年8·8九寨沟地震对震中15.5 km缓冲区的生态环境质量状况有较明显的影响,县界内的三个自然保护区中九寨沟国家级自然保护区受到冲击最大。本研究可为九寨沟地区生态环境监测、灾害评价和恢复重建提供科学依据。     

新一代Landsat系列卫星：Landsat8遥感影像新增特征及其生态环境意义

美国Landsat 8卫星的成功发射使一度中断的Landsat对地观测得以继续。Landsat 8除了保持Landsat 7卫星的基本特征外,还在波段的数量、波段的光谱范围和影像的辐射分辨率上进行了改进。基于该卫星的首幅影像,针对这些新的特性进行了分析和研究。研究发现:(1)新增的卷云波段有助于区别点云和高反射地物;(2)卷云波段设计的波长范围位于粘土矿物光谱反射的强吸收带,有利于土壤与建筑不透水面信息的区别;(3)新增的深蓝波段有助于水体悬浮物浓度的监测;(4)全色影像波长范围的收窄有利于该影像上植被和非植被的区别;(5)辐射分辨率的提高可避免极亮/极暗区的灰度过饱和现象,这对反射率极低的水体的细微特征识别有很大帮助。显然,Landsat 8这些新增的优点将会对全球生态环境变化的监测产生积极的作用。     

建设项目引发的区域生态变化的遥感评估——以敖江流域为例

建筑用地规模的扩大,在很大程度上影响着区域生态质量,制约了区域的可持续发展,利用遥感对地观测技术及时监测区域生态质量具有十分重要的意义。以敖江流域为例,重点研究该流域中的贵安开发区建设项目引发的建筑用地变化及其对区域生态质量的影响。选取2010年建设前的ALOS影像和2016年建设后的GF-1影像,构建基于ALOS和GF-1影像的建筑用地提取模型;采用遥感生态指数(RSEI)来对生态质量进行综合评价,并构建了ALOS和GF-1影像的湿度分量,在此基础上定量分析了区域建筑用地变化及其生态效应。研究表明:2010—2016年间,研究区建筑用地面积显著增加,其中有86%是由于贵安开发区建设引起的。贵安开发区建筑用地的增加导致了区域生态质量的总体下降,其遥感生态指数RSEI均值从建设前的0.787下降到建设后的0.689,降幅达12.4%,生态优良等级所占面积的比例从2010年的91%下降到2016年的79%。定量分析表明,区域建筑用地面积比例与生态质量呈显著负相关关系,建筑用地面积占比每增加10%,其RSEI值将下降0.041。因此,应加强敖江流域生态环境的保护与治理,严格控制沿江的建设开发项目,切实保护好流域的生态环境。     

利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI) ——以岷江上游为例

短周期的低分辨率遥感数据为大面积估算LAI及季节动态和物候趋势提供了有利工具,但基于高分辨率LAI的遥感估算模型在低分辨率遥感数据上应用有很大的不确定性。研究利用LAI-2000冠层分析仪与跟踪辐射和冠层结构测量仪(TRAC),测定了岷江上游流域范围内490块野外调查样地（50m×50m样方）的LAI数据,结合同期较高精度卫星数据（TM）建立了不同植被类型的LAI-NDVI算法,在经过传感器的相对校正后,将这种算法应用到同期分辨率较低的MODIS数据和SPOT VEGETATION数据上。结果表明,30m 分辨率的TM LAI的均值为4.53,250m MODIS LAI的均值为3.55,1000m VGT LAI的均值为4.20,随着栅格分辨率的降低,总体标准差有增加的趋势,并且LAI值也有不同程度的低估,其中MODIS LAI值被低估约22%。但利用TM LAI数据验证MODIS 和VGT LAI数据后发现,250m的MODIS数据预测误差在30%左右,1000m的SPOT数据预测误差则高达50%,空间重采样分析表明,栅格分辨率的降低是导致预测误差扩大的主要原因,而这也是岷江流域植被分布破碎化的体现。     

基于多光谱卫星影像与机器学习算法的松材线虫病受害林分识别研究

松材线虫病(Pine Wilt Disease, PWD)被称为“松树癌症”,具有高传染率和高死亡率,对我国森林资源构成了严重的威胁,对我国的经济、社会和生态造成了重大损失。及时发现并清理疫木是遏制松材线虫病蔓延的有效手段,精准监测疫木是防控松材线虫病的前提,但是现阶段缺少大面积识别松材线虫病疫木的技术方法。本文旨在探索哨兵-2号与Landsat-8遥感卫星影像对受害松林的识别能力,采用随机森林(Random Forest, RF)、支持向量机(Support Vector Machine, SVM)、决策树(Decision Tree, DT)和极端梯度提升(Extreme Gradient Boosting, XGBoost)等4种机器学习算法建立了松材线虫病监测模型。结果表明：基于哨兵-2号影像数据建立的监测模型对受害松林的识别准确率高于Landsat-8遥感卫星影像,其中基于10 m分辨率的影像数据建立的监测模型识别准确率最高,随机森林、决策树、支持向量机和极端梯度提升等算法建立模型的准确率分别达到了79.3%、76.2%、78.7%和78.9%。在3种不同的影像数据集中,RF...     

福建省长汀县河田盆地区近35年来地表裸土变化的遥感时空分析

福建省长汀县河田盆地区是中国南方最典型的红壤水土流失区之一,当地人民和各级政府一直为治理该区的水土流失进行着不懈的努力.利用遥感技术对该区1976年以来地表裸土分布的时空变化进行了分析,基于所提出的双重遥感指数法对该区的裸土信息进行提取,查明了该区地表裸土分布的时空变化情况.研究表明,该区近35年的治理已大大减轻了地表的裸露程度,地表裸土面积从1976年的159.17 km2锐减到2010年的51.98 km2.在3个不同的观察时间段里,裸土面积的变化呈现逐次减少、减速加快的趋势,客观地反映了该区水土流失治理的3个重要历史时期和政策所产生的效应.     

基于城市控制性详细规划的热岛效应评价

随着城市规划法律地位的提升,控制性详细规划在指导土地出让、规范城市建设空间结构方面发挥着越来越重要的作用.在控规单元尺度上理解各项控制指标的热岛效应,在控规地块尺度上进一步揭示控制指标组合的“热环境绩效”,对于定量评价控规的热环境效应、提出缓减热岛的控制措施具有重要意义.本文利用遥感Landsat8 TIRS热红外遥感数据反演的地表温度来表征热岛效应,结合统计分析与GIS空间分析技术,定量评价城市控制性详细规划主要技术经济指标及其配置结构的热岛效应.结果表明: 绿地率、地块面积、建筑密度、建筑高度在控规单元尺度上对地表温度具有显著影响.通过构建三维模型发现,控规地块对热环境影响最小的组合为绿地率30%,容积率2.5～3.5.研究结果为城市控制性详细规划的优化提供了新的视角.     

鄱阳湖区生态环境质量的空间分异特征及其影响因素

本文基于Landsat 8 OLI/TIRS遥感影像数据,从绿度、湿度、干度和热度4方面分别选取归一化植被指数、湿度分量、干度指数、地表温度指标,采用主成分分析构建遥感生态指数(RSEI),分析了鄱阳湖区2014年生态环境质量空间分布特征.从不同尺度划分空间单元,运用空间自相关、半方差函数、最大信息系数和因子探测等方法定量分析了原始遥感生态指数(RSEI₀)的空间异质性及其影响因素.结果表明: 鄱阳湖区2014年生态环境质量总体处于中等水平,在空间上呈现由西南向东北逐渐好转的分布格局;经探索性空间数据分析得出,RSEI₀在2 km网格单元采样下的空间集聚性较强,空间异质性明显.RSEI₀块金效应为25.8%,属于中等空间变异,主要受结构性因素的影响.坡度与RSEI₀的相关性最强,呈正相关关系.坡度、高程、曲率、年均降雨量、年均温、园林地面积比例、耕地面积比例、建设用地面积比例、年均人口密度和年均GDP对RSEI₀的影响程度均显著,但对RSEI₀的影响程度不一.其中,坡度的独立解释力为57.1%,是影响鄱阳湖区RSEI₀空间变异的主要因素.研究结果可为鄱阳湖区的生态环境保护与治理提供参考依据.     

Use of multispectral satellite datasets to improve ecological understanding of the distribution of Invasive Alien Plants in a water-limited catchment,South Africa

Invasive Alien Plants (IAPs) pose major threats to biodiversity, ecosystem functioning and services. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Imager) offers an opportunity to map and monitor the occurrence and spatial distribution of IAPs. The use of two multispectral remote sensing data sets to map and monitor IAPs in the Heuningnes Catchment, South Africa, was therefore investigated using the maximum likelihood classification algorithm. It was possible to identify areas infested with IAPs using remote sensing data. Specifically, IAPs were mapped with a higher overall accuracy of 71%, using Sentinel-2 MSI as compared to using Landsat 8 OLI, which produced 63% accuracy. However, both sensors showed similar patterns in the spatial distribution of IAPs within the hillslopes and riparian zones of the catchment. This work demonstrates the utility of the two multispectral data sets in mapping and monitoring the occurrence and distribution of IAPs, which contributes to improved ecological modelling and thus to improved management of invasions and biodiversity in the catchment.     

基于Landsat-8影像的沿海城市公园冷岛效应——以厦门为例

城市公园主要由水体和绿色植被等景观构成,其形成的公园冷岛(Park cool island,PCI)已成为减缓城市热岛效应的重要途径之一。采用景感生态学中的景感营造理念规划设计城市公园或优化公园空间结构有助于增强公园冷岛效应,进一步改善局地城市热环境。选择沿海城市——厦门市为研究对象,基于2013年8月的Landsat-8 OLI/TIRS遥感影像和Google Earth高分影像数据,首先提取了研究区15个公园的土地利用信息,采用改进的地表温度单通道算法,获取了城市公园地表温度数据,并采用缓冲区分析、景观格局指数和多元统计等方法定量分析公园内部平均地表温度、城市公园冷岛强度(即降温幅度)、公园冷岛影响距离(即对周边区域的降温范围)三者的主要影响因子,并利用景感生态学原理初步剖析了城市公园景感营造的原则。结果显示:(1)公园面积和公园建设用地面积是影响公园平均温度的关键因子(R²=0.915),这两个因子与公园平均温度均呈显著非线性相关;公园面积存在阈值55 hm²左右;(2)公园冷岛PCI强度由公园绿地面积、公园建筑面积和面积-周长形状指数...     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

基于Landsat 8影像的不同燃烧指数在农田秸秆焚烧区域识别中的应用

农田秸秆焚烧造成秸秆资源的巨大浪费和大气环境的污染,利用热红外波段识别火点的方法可以实时、快速地获取焚烧情况,但是不能提供秸秆焚烧的面积、范围等详细的空间分布情况,也不能确定焚烧的严重程度.利用已燃烧与未燃烧区域的光谱差异选择某些波段构建燃烧指数的方法在森林火灾研究中得到了广泛应用,但是这些指数在农田秸秆焚烧中的潜在应用没有被研究.本文基于松嫩平原地区的两景Landsat 8卫星影像,采用归一化燃烧率（NBR）、引入热红外波段的归一化燃烧率（NBRT）、燃烧面积指数（BAI）3种燃烧指数对农田秸秆焚烧和未焚烧区域进行提取,并与秸秆覆盖度进行相关分析.结果表明：NBR、NBRT和BAI指数对焚烧和未焚烧区域的分类精度分别为91.9%、92.3%、87.8%,NBR、NBRT与覆盖度呈线性相关,R²分别为0.73、0.64,BAI与覆盖度呈幂指数相关关系,R²为0.68.燃烧指数方法可以在农田秸秆焚烧中得到很好的应用,可定量评估秸秆燃烧程度,为大气环境评价提供技术与数据支持.     

Characteristics of built-up land change in resource-based cities and their impact on land surface temperature—Taking Wu'an as an example

The effects of built-up land on land surface temperature (LST) and its spatial patterns remain poorly understood over resource-based cities. Using Landsat-5,8 satellite imagery of Wu'an, a resource-based city in China, from 2001, 2007, and 2015, this paper classified the land use types and calculated the percentage of built-up land (PLAND). Changes in built-up land and their effects on the distribution of LST were analyzed through statistics and regression analysis. Statistical results showed that the amount of built-up land in 2007 and 2015 increased significantly compared with 2001. The same PLAND level increased LST more in 2007 and 2015. Furthermore, PLAND = 35% split the heating effects of PLAND on LST. The LST in areas with high PLAND (PLAND >35%) was mainly impacted by the local built-up land. However, the LST in the areas with low PLAND (PLAND <35%) was mainly correlated with the built-up land of areas with high PLAND. The results of regression analysis showed that the k value (slope of the correlation analysis) in 2007 and 2015 was larger than that in 2001, indicating the local heating effects of built-up land on LST. The spatial regression analysis results revealed that there was a closer spatial correlation between PLAND and LST in 2015, and built-up land with high PLAND (PLAND >35%) would increase the LST of surrounding built-up land with low PLAND (PLAND <35%). These research results could provide more references for urban expansion in urban design and planning.     

高光谱植被指数与水稻叶面积指数的定量关系

基于不同水稻品种、施氮水平和不同生育期下的大田试验,确立了水稻叶面积指数（LAI）与冠层光谱特征参数的定量关系.结果表明：水稻叶面积指数与部分高光谱植被指数存在良好的相关性,其中原始光谱组成的2波段差值指数（DI）形式相关性最好,其次为比值（RI）和归一化（NI）植被指数.相关最好的原始光谱植被指数是由近红外波段组成的差值指数DI(854,760),相关最好的一阶导数光谱植被指数是红光和近红外光组成的导数差值指数DI(D₆₇₆, D₇₇₈),但总体上导数光谱指数不如原始光谱指数与LAI关系密切.独立试验数据检验结果表明,以差值指数DI(854,760)为变量建立的水稻LAI监测模型具有较好的表现,可用于水稻LAI的估测.     

Identifying Dicymbe corymbosa Monodominant Forests in Guyana Using Satellite Imagery

The ectomycorrhizal (EM) canopy tree Dicymbe corymbosa (Fabaceae subfam. Caesalpinioideae) forms monodominant forests in the Pakaraima Mountains of western Guyana. Like other tropical monodominants, D. corymbosa has several life-history traits that promote conspecific clumping, in contrast to density-dependent recruitment limitations characterizing most tropical trees. Dicymbe corymbosa forests, occurring in Guyana as patches within a largely non-EM mixed-species forest matrix, are important habitats for a diverse assemblage of EM fungi. Ground-based studies have not adequately determined the regional extent of D. corymbosa forests, nor are they practical due to the rugged, remote nature of the Pakaraima Mountains. We assessed the suitability of Landsat satellite imagery for mapping regional distribution of D. corymbosa forests in Guyana's Upper Potaro River Basin. Supervised image classification was performed on images from August 1989 (Landsat-5 TM) and October 1999 (Landsat-7 ETM+). In situ forest reference data were used to quantitatively assess accuracy of output classification maps. Classification performed well in distinguishing monodominant from mixed-species forests. For both images, D. corymbosa forest class accuracy was good (1989 user's accuracy = 89.8%, K_hat = 0.74; 1999 user's accuracy = 80.7%, K_hat = 0.59). The resulting output classification maps will be useful for planning fungal surveys and ecological studies in forests of the Pakaraima region. Classification of Landsat images may be effective for identifying monodominant forests in other remote regions of the tropics.     

经济快速增长区建筑用地变化及其对生态质量的影响——以福建晋江为例

改革开放以来,中国经济增长促进了建筑用地的快速增加,并在很大程度上影响着区域生态质量.本文以东南沿海经济快速增长的福建省晋江市为例,利用遥感技术重点研究经济快速增长导致的建筑用地变化及其对区域生态质量的影响.基于晋江市1996和2015年的Landsat遥感影像,利用建筑用地指数(IBI)提取建筑用地信息,并进行建筑用地变化分析;同时利用遥感生态指数(RSEI)对生态质量进行评价,定量分析城镇建筑用地变化与生态质量变化之间的关系.结果表明: 1996—2015年,晋江市建筑用地面积共增加68.54 km²,增幅达45%,建筑用地扩展强度为0.55.建筑用地的增加导致该市生态质量总体下降,RSEI均值从1996年的0.532下降到2015年的0.460,降幅达13.5%;生态优良等级所占面积的比例从1996年的39%下降到 2015年的21%.建筑用地扩展强度与生态质量呈显著负相关关系.     

Estimation of forest-ecosystem site index using remote-sensed data

The estimation of site index and site quality forms the fundamental theory and basic tools in forest-ecosystem management and silviculture practice. The study on the spatial pattern and temporal dynamics of site index and site quality of forest ecosystem still lacks technological advancement. It is a novel approach for estimating forest productivity in large areas using satellite remote-sensed data. The site-index spatial distribution pattern of spruce (Picea asperata) forest in Songpan-Zhengjiangguan watershed, northwestern Sichuan Province, China, was described using the remote-sensing vegetation index application and the established inverse models. The application potential of the methodology in broad regions and forests using the accuracy assessment was evaluated. The results show that the site index of the spruce forest is in linear correlation with the remote-sensed vegetation indices (normalized difference vegetation index (NDVI) and soil adjust NDVI (TNDVI)), as well as with these inverse models with high accuracy. This study demonstrated that this approach can be used in similar estimation of different forest ecosystems.     

Estimation of forest-ecosystem site index using remote-sensed data

The estimation of site index and site quality forms the fundamental theory and basic tools in forest-ecosystem management and silviculture practice. The study on the spatial pattern and temporal dynamics of site index and site quality of forest ecosystem still lacks technological advancement. It is a novel approach for estimating forest productivity in large areas using satellite remote-sensed data. The site-index spatial distribution pattern of spruce (Picea asperata) forest in Songpan-Zhengjiangguan watershed, northwestern Sichuan Province, China, was described using the remote-sensing vegetation index application and the established inverse models. The application potential of the methodology in broad regions and forests using the accuracy assessment was evaluated. The results show that the site index of the spruce forest is in linear correlation with the remote-sensed vegetation indices (normalized difference vegetation index (NDVI) and soil adjust NDVI (TNDVI)), as well as with these inverse models with high accuracy. This study demonstrated that this approach can be used in similar estimation of different forest ecosystems.