Update and spatial extension of strategic forest inventories using time series remote sensing and modeling     

《International Journal of Applied Earth Observation and Geoinformation》2020

Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R² = 0.74, %RMSE = 23.1%), followed by stand age (R² = 0.62, %RMSE = 35.6%), and stem density (R² = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.  相似文献   

1990—2018年中巴经济走廊冰湖时空变化特征     

姚超  王欣  赵轩茹  魏俊锋  张勇 《冰川冻土》2020,42(1):33-42

中巴经济走廊是贯通南北丝路的关键枢纽。在全球变暖的背景下， 区域内冰川变化情况复杂， 部分冰川出现前进或跃动现象， 冰湖溃决的风险在不断上升， 进而威胁中巴经济走廊的建设与民生安全。基于1990—2018年Landsat TM/ETM+/OLI遥感影像， 利用目视解译方法提取了中巴经济走廊3期冰湖编目数据， 并分析了28年来该区域内冰湖的总体变化趋势、 空间异质性以及成因。结果表明： 中巴经济走廊目前共发育有2 380个冰湖， 总面积为（131.76±19.08） km²， 集中分布于喀喇昆仑山脉和喜马拉雅山脉； 1990—2018年期间， 冰湖总体面积扩张速度为0.48%·a^-1， 但各个山脉不同规模冰湖面积变化差异较大。中巴经济走廊在气温和降水的共同作用下， 区内冰湖面积呈扩张趋势， 同时气温和降水变化率的空间差异使得冰湖面积变化存在空间差异； 冰川的快速退缩增加了区内冰湖溃决的风险。  相似文献   

油砂的光谱反射特征及其遥感应用     

樊瑞雪  邢立新  潘军  单玄龙  仲伟敬 《吉林大学学报(地球科学版)》2019,49(2):603-610

油砂作为21世纪以来世界能源的重要组成部分，已经有大量的地质工作者对其形成的地质特征、成矿带分布特征等进行了详细研究，并取得一定成果，但是对于遥感在油砂勘探中的应用少有研究。遥感技术识别不同地物的理论基础是同一波长位置处的反射率差异及反射率随波长的变化规律，因此本文通过分析油砂的实测光谱数据，得到其反射光谱的特征及规律。为实现在Landsat 8 OLI卫星遥感影像上对油砂的识别，根据油砂实测光谱的研究，分别提出基于油砂光谱特征和相关性分析的Landsat 8遥感影像油砂识别模式，并将其应用在柴达木盆地冷湖和马海两个研究区。结果表明：利用遥感影像进行油砂找矿靶区圈定是可行的；本文总结出的基于相关性分析的油砂提取模式优于光谱特征分析模式；冷湖地区用比值方法进行油砂遥感信息增强效果较好，马海地区则差值方法较好。  相似文献   

Study on the Area Variation of Qinghai Lake Based on Long-Term Landsat 5/8 Multi-Band Remote Sensing Imagery   总被引：1，自引：0，他引：1  

Weixiao Han  Chunlin Huang  Yunchen Wang  Juan Gu 《地球科学进展》2019,34(4):346-355

  相似文献   

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015     

《International Journal of Applied Earth Observation and Geoinformation》2020

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.  相似文献   

Decumbent development: Urban sprawl in the Guwahati Metropolitan Area,India     

Chandra Kant Pawe  Anup Saikia 《Singapore journal of tropical geography》2020,41(2):226-247

Urban sprawl has become a global phenomenon as an outcome of growing population and rapid urbanization. Previous studies have addressed the rising incidence of uncontrollable urban development, particularly in peri-urban areas of cities, leading to chronic urban sprawl. The city of Guwahati, a million city in north east India, has expanded significantly in recent years. In this article, the links between population and growth of built-up areas were examined using geo-spatial techniques and remotely sensed datasets. The results indicate that the sprawl has accentuated in recent years. The intensity of land use remained uneven due to marked variations in the distribution of built-up areas, plausibly an outcome of unplanned urban growth. If current trends are anything to go by, future urban sprawl could pose serious threats to the vulnerable eco-sensitive and peri-urban areas of Guwahati. Secondary cities have unfortunately received scant attention in urban policy research, and Guwahati, epitomizes urban woes in a developing country.  相似文献   

机载WIDAS数据的Landsat卫星反照率初步验证   总被引：1，自引：1，他引：0  

何丹丹  焦子锑  董亚冬  张小宁  丁安心  尹思阳  崔磊  常雅轩 《遥感学报》2019,23(1):53-61

随着精细化监测的需求,中高空间分辨率的地表反照率产品逐渐成为气候模型的主要输入。目前,中高空间分辨率反照率产品的验证主要基于地表站点的通量塔观测数据,区域机载飞行数据的验证依然相对较少。因此,本文基于区域机载数据验证Landsat反照率产品。针对内蒙古自治区根河森林试验区所获取的机载红外广角双模式成像仪(WIDAS)多角度反射率数据,应用BRDF原型反演算法估算其反照率,分析了应用机载数据验证中高空间分辨率反照率产品的潜力。2016年内蒙古根河森林试验区机载WIDAS飞行多角度观测的可用多角度范围为25°,以前的研究表明BRDF原型反照率反演算法表现出对小观测角度的反照率反演结果的鲁棒性。因此,机载WIDAS反照率在一定程度可用于星载反照率的验证。首先,基于核驱动模型和各向异性平整指数(AFX)提取了试验区4种MODIS二向性反射分布函数(BRDF)原型;然后,将其作为先验知识应用到根河森林WIDAS机载数据的反照率反演中;最后,用WIDAS反照率和单个地面通量塔观测的反照率对Landsat卫星数据的反照率进行初步验证。验证结果表明Landsat反照率与WIDAS反照率结果较为一致,但略有低估,总体均方根误差(RMSE)约为0.02,偏差为0.0057。在多角度观测范围较小时,BRDF原型的反照率反演算法可为星载地表反照率的验证提供了一种有效的验证手段。  相似文献   

Regional‐scale mapping of groundwater discharge zones using thermal satellite imagery          下载免费PDF全文

G. Z. Sass  I. F. Creed  J. Riddell  S. E. Bayley 《水文研究》2014,28(23):5662-5673

Mapping groundwater discharge zones at broad spatial scales remains a challenge, particularly in data sparse regions. We applied a regional scale mapping approach based on thermal remote sensing to map discharge zones in a complex watershed with a broad diversity of geological materials, land cover and topographic variation situated within the Prairie Parkland of northern Alberta, Canada. We acquired winter thermal imagery from the USGS Landsat archive to demonstrate the utility of this data source for applications that can complement both scientific and management programs. We showed that the thermally determined potential discharge areas were corroborated with hydrological (spring locations) and chemical (conservative tracers of groundwater) data. This study demonstrates how thermal remote sensing can form part of a comprehensive mapping framework to investigate groundwater resources over broad spatial scales. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models          下载免费PDF全文

Andrés Varhola  Nicholas C. Coops  Younes Alila  Markus Weiler 《水文研究》2014,28(15):4433-4448

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Extracting water-related features using reflectance data and principal component analysis of Landsat images     

Boglárka Balázs  Tibor Bíró  Gareth Dyke  Sudhir Kumar Singh 《水文科学杂志》2018,63(2):269-284

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.  相似文献