Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

面向对象与卷积神经网络模型的GF-6 WFV影像作物分类

GF-6WFV影像是中国首颗带有红边波段的中高分辨率8波段多光谱卫星的遥感影像,对于其影像及红边波段对作物分类影响的研究利用亟待展开.本文结合面向对象和深度学习提出一种适用于GF-6 WFV红边波段的卷积神经网络(RE-CNN)遥感影像作物分类方法.首先采用多尺度分割和ESP工具选择最佳分割参数完成影像分割,通过面向对...     

高分六号宽幅多光谱数据人工林类型分类

高分六号(GF-6)卫星于2018年成功发射,2019-03正式投入使用。由于GF-6宽幅相机的WFV(Wide Field of View)影像较GF-1的同类影像新增2个红边波段,将会提高对农业、林业、草原等资源监测能力。为了分析GF-6的WFV影像在人工林分类方面的能力,促进高分数据在林业领域的应用,本文选取广西高峰林场为研究区,以最新的GF-6 WFV影像为数据源,结合地面实测类型数据,进行广西南宁高峰林场的桉树,杉木等人工林类型提取。主要运用随机森林(random forests)的分层分类法:首先计算6种植被指数,并利用随机森林法进行植被指数的特征优选,然后确定4种波段组合数据集的分类方案,4种数据集分别为(1)无红边的前4个波段,(2)有红边的8个波段,(3) 8个波段加上未优化的植被指数特征组合,(4) 8个波段加上优化的植被指数特征组合。再进行WFV影像4种数据集的随机森林分类,随机森林采用分类回归树(CART)算法来生成分类树,结合了bagging和随机选择特征变量的优点,是一种有效的分类方法。最后比较4个方案的分类结果并进行精度验证。结果表明:方案2比方案1精度提高了4.99%,Kappa系数提高了0.058。说明包含红边的8波段数据比4个波段数据精度有大幅提升。方案4的8波段加上优化植被指数特征组合的分类精度最高,达到了85.38%,比方案2包含红边波段组和方案1无红边波段组的精度分别提高了3.98%,8.97%,Kappa系数分别提高了0.046,0.104。说明WFV影像加入红边波段比无红边波段精度明显增高。由结果可知,红边指数的引入,增强了植被信息,能够较准确地反映人工林类型特征差异,明显提升了人工林的分类精度。本研究方法可以有效改善广西人工林类型信息提取效果,为GF-6影像质量的评价及其在林业应用潜力提供科学参考依据。     

基于高分六号红边特征的面向对象桉树人工林信息提取

有效监测桉树林空间分布对区域生态环境保护及有关部门的统筹决策具有重要指导意义.本文在现有研究的基础上,以国内首个提供红边波段的多光谱高分六号(GF-6)卫星影像为数据源,选取广西鹿寨县为典型研究区域,结合光谱特征、植被指数特征和红边特征,设计不同特征组合分类方案,采用面向对象多尺度分割方法,对不同尺度层分别构建隶属度函...     

植被叶面积指数估算的红边指数建模分析

针对在路域环境监测中,如何精确估算叶面积指数问题,该文提出以长韶娄高速路域为研究区,筛选出4种常用植被指数和4种红边指数两类指数,分别构建了经验模型和机器学习的反演模型,利用Sentinel-2影像数据和同步的LAI-2000地面实测数据完成路域植被叶面积指数反演。结果表明,红边波段参与运算的植被指数与植被叶面积指数敏感性是显著相关,红边指数在反演精度上更优。由此可知,相较于常见植被指数,红边指数增强了其与叶面积指数的敏感性,提高了叶面积指数估算模型精度。     

Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data

Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management.     

Detection and mapping of maize streak virus using RapidEye satellite imagery

The aim of this study was to detect and map MSV using RapidEye multispectral sensor in Ofcolaco farm. To achieve this objective, the acquired RapidEye sensor was classified using the robust Random Forest algorithm. Furthermore, the variable importance technique was used to determine the influence of each spectral band and indices on the mapping accuracy. For better performance of image data, the value of the commonly used vegetation indices in improving the classification accuracy was tested. The results revealed that the use of RapidEye spectral bands in detection and mapping of MSV yielded good classification results with an overall accuracy of 82.75%. The inclusion of vegetation indices computed from RapidEye sensor improved the classification accuracies by 3.4%. The most important RapidEye spectral bands in classifying MSV were near infrared, blue and red-edge. On the other hand, the most important vegetation indices were the Soil adjusted vegetation index, Enhanced vegetation index, Red index and Normalized Vegetation Index. The current study recommends future studies to assess the importance of multi-temporal remote sensing applications in detecting and monitoring the spread of MSV.     

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm.     

Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Jack pine budworm (Choristoneura pinus pinus (Free.)) is a native insect defoliator of mainly jack pine (Pinus banksiana Lamb.) in North America east of the Rocky Mountains. Periodic outbreaks of this insect, which generally last two to three years, can cause growth loss and mortality and have an important impact ecologically and economically in terms of timber production and harvest. The jack pine budworm prefers to feed on current year needles. Their characteristic feeding habits cause discolouration or reddening of the canopy. This red colouration is used to map the distribution and intensity of defoliation that has taken place that year (current defoliation). An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable.Automated classification of multispectral imagery, such as is available from airborne and new high resolution satellite systems, was explored as a viable tool for objectively classifying current discolouration. Airborne multispectral imagery was acquired at a 2.5 m resolution with the Multispectral Electro-optical Imaging Sensor (MEIS). It recorded imagery in six nadir looking spectral bands specifically designed to detect discolouration caused by budworm and a near-infrared band viewing forward at 35° was also used. A 2200 nm middle infrared image was acquired with a Daedalus scanner. Training and test areas of different levels of discolouration were created based on field observations and a maximum likelihood supervized classification was used to estimate four classes of discolouration (nil-trace, light, moderate and severe). Good discrimination was achieved with an overall accuracy of 84% for the four discolouration levels. The moderate discolouration class was the poorest at 73%, because of confusion with both the severe and light classes. Accuracy on a stand basis was also good, and regional and within stand discolouration patterns were portrayed well. Only three or four well-placed spectral bands were needed for a good classification. A narrow red band, a near-infrared and short wave infrared band were most useful. A forward looking band did not improve discolouration estimation, but further testing is needed to confirm this result.This method of detecting and classifying discolouration appears to provide a mapping capability useful for conducting jack pine budworm discolouration surveys and integrating this information into decision support systems, forest inventory, growth and yield predictions and the forest management decision-making process.     

联合GEE与多源遥感数据的黑龙江流域沼泽湿地信息提取

湿地是地球上最重要的生态系统之一,在维持全球生态环境安全等方面发挥着举足轻重的作用.由于湿地独特的水文特征,传统的湿地监测需要耗费大量的人力和财力,对于大尺度的湿地信息提取更是困难重重.随着大数据和云计算的兴起,为大尺度和长时间序列的空间数据处理提供了契机.本文基于Google Earth Engine(GEE)云平台...     

Sentinel-2 red-edge spectral indices (RESI) suitability for mapping rubber boom in Luang Namtha Province,northern Lao PDR

Rubber (Hevea brasiliensis) tree cultivation is being continuously expanded northward by replacing evergreen forests and swidden-related regenerated vegetation across the uplands of mainland Southeast Asia (MSEA), e.g., Laos, a landlocked mountainous country. The non-native tree establishment in the northern tropical edge, or the non-traditional suitable planting area, provides stable supplies of natural latex, yet also leads to severe ecological degradation and environmental effects in water conservation, soil quality, rainforest fragmentation and biodiversity. Rubber plantations in the northern part of MSEA are normally characterized by periodic deciduous during the dry season, along with a lengthy defoliation-foliation duration, because of seasonal variations in temperature and precipitation. It thus lays a phenological and physiological base for dynamics monitoring with common multispectral (e.g., near-infrared and short-wave infrared bands) satellites, particularly Landsat. However, whether Sentinel-2 red-edge based algorithms are suitable for discriminating rubber plantations is not yet exclusively reported. Here, we developed a red-edge spectral indices (RESI) method through the normalization of three red-edge bands and applied it to identify and map rubber plantations in Luang Namtha Province of northern Laos, where a rubber boom begun in the mid-2000s. The RESI algorithm highlights the sensitivity of red-edge bands to the changes in moisture content and canopy density of rubber plantations. The area of mature rubber plantations was estimated to be 771.2 km² in this province bordering southwest China in 2018, which was nearly twice as much as that of 2011, with the overall accuracy and kappa coefficient up to 92.50% and 0.91, respectively. Our phenology-based RESI approach not only indicates that Sentinel-2 imagery holds significant potential for monitoring rubber plantations, but also improves the remotely-sensed methods of rubber boom mapping via introducing the red-edge channel.     

GF-6影像应用于林地与非林地识别的潜力分析

为研究我国首颗携带红边波段的高分六影像(GF-6)在林地与非林地上的识别贡献,本文选择复杂林地类型的安徽省黄山市作为研究区,采用特征优选(RFE)与随机森林(RF)相结合的方法开展了林地与非林地识别潜力研究。首先根据实地调查、Google Earth影像及林地"一张图"样本数据构建了样本库;然后基于DEM、多时相光谱特征、植被指数、红边指数等特征开展分类,并比较不同模型精度及不同变量的重要度。结果表明:GF-6红边信息对林地非林地识别较为重要,引入红边信息可将总体分类精度提升2%,其他新增波段及地形特征对林地与非林地识别贡献并不明显;多时相数据的运用相比单时相数据可整体提高林地类型的分类精度2.93%～4.1%,单时相分类结果 6月最好,9月次之,12月最差;特征优选可以有效减少数据输入维数(46到15),并取得最高分类精度,在不牺牲精度的同时保证了运算数据量的减少且明确了不同变量的贡献,具有较强的应用意义。     

Sentinel-2影像多特征优选的黄河三角洲湿地信息提取

以北方典型河口湿地—黄河三角洲湿地为研究区,采用在特征选择和分类提取等方面具有明显优势的随机森林算法,对研究区内的湿地信息进行提取。首先基于多时相、光谱信息丰富的Sentinel-2数据生成4类不同的特征变量,包括光谱特征、植被指数和水体指数、红边指数、纹理特征;再根据以上特征构建6种不同的提取方案,对黄河三角洲湿地信息进行提取并验证不同方案的提取精度,旨在选择最佳方案改善湿地信息提取的效果。结果表明:(1)有效地使用多种特征变量是提高湿地信息提取的关键,就不同特征对湿地信息提取的贡献率而言,红边指数植被指数和水体指数光谱特征纹理特征;(2)基于随机森林算法优选的特征变量提取效果最佳,总体精度高达90.93%,Kappa系数为0.90,表明随机森林算法可以有效地进行特征选择,在特征变量数据挖掘的同时,仍能保证湿地信息提取的精度,提高运行效率。本研究为湿地信息提取在数据源选择、特征选择和方法选择方面提供了一种新思路、方法和技术手段。     

基于Sentinel-2的潮间红树林提取方法

位于潮间带的红树林可能在高潮时被海水淹没的特点,使得传统的植被提取方法在红树林信息提取方面存在局限性。本文在对比分析了出露的红树林、高潮水位淹没的红树林、海水水体的光谱特征后,提出了一种利用归一化潮间红树林指数（NIMI）提取潮间带红树林的方法。该指数是由植被强吸收的红波段,强反射的两个红边波段和近红外波段组成的归一化表达式。利用该指数对福建省龙海九龙江口湿地的红树林进行了分类提取,提取结果与高分二号影像目视验证和现场调查结果进行了对照。结果显示,该方法提取红树林的用户精度达到93.98%,并显著优于利用归一化水体指数（NDWI）、归一化植被指数（NDVI）及随机森林的结果。     

Texture-based classification of sub-Antarctic vegetation communities on Heard Island

This study was the first to use high-resolution IKONOS imagery to classify vegetation communities on sub-Antarctic Heard Island. We focused on the use of texture measures, in addition to standard multispectral information, to improve the classification of sub-Antarctic vegetation communities. Heard Island’s pristine and rapidly changing environment makes it a relevant and exciting location to study the regional effects of climate change. This study uses IKONOS imagery to provide automated, up-to-date, and non-invasive means to map vegetation as an important indicator for environmental change. Three classification techniques were compared: multispectral classification, texture based classification, and a combination of both. Texture features were calculated using the Grey Level Co-occurrence Matrix (GLCM). We investigated the effect of the texture window size on classification accuracy. The combined approach produced a higher accuracy than using multispectral bands alone. It was also found that the selection of GLCM texture features is critical. The highest accuracy (85%) was produced using all original spectral bands and three uncorrelated texture features. Incorporating texture improved classification accuracy by 6%.     

Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.     

Sentinel-2数据的冬小麦地上干生物量估算及评价

作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来,遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数,之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型,通过分析两者间的相关性与敏感性,获取适宜进行生物量估算的指数。最后,绘制了研究区的生物量空间分布图。结果表明,所选的植被指数均与生物量显著相关。其中,红边叶绿素指数(CI_(re))与生物量的估算精度最高(决定性系数R~2为0.83;均方根误差RMSE为180.29 g·m~(–2))。虽然相关性较高,但部分指数,如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象,从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势,而且能够提高反演精度。此外,通过敏感性分析发现,归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR_(re))和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性,二者是生物量估算中最为稳定的指数。     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Analyzing fine-scale wetland composition using high resolution imagery and texture features

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).     

FY3B/MERSI数据的湖泊湿地面积提取对比分析

针对国产风云三系列中分辨率卫星快速有效地进行水体识别的问题,基于2011—2016年数据进行了辽宁省主要湖泊水库的光谱分析,提出了晴空条件和有云情况下分别采用归一化水体指数方法和通道值与归一化水体指数相结合方法进行湖泊水库的提取。结果表明:FY3B/MERSI湖泊光谱曲线具有水体光谱特征,具体表现为8通道的数据反射率最高,18通道数据反射率最低。通过与49景TM数据水体提取结果进行对比,FY3B/MERSI数据水体提取的面积精度达到85%以上,总体分类精度达到90%以上,Kappa系数在0.56~0.95之间。提出了简便、快捷的计算模型,为国产卫星数据的业务应用提供了初步方法。