View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest

Because of the pointing capability of the Hyperion/Earth Observing-One (EO-1) to improve the revisit time of the scene, temporal series of narrowband vegetation indices (VIs) can be generated to study the phenology of the Amazonian tropical forests. In this study, 10 selected narrowband VIs calculated from Hyperion nadir and off-nadir data and from different view directions (forward scattering and backscattering) were analyzed for their sensitivity to view-illumination effects along the dry season on the Seasonal Semi-deciduous Forest. Data analysis was also supported by PROSAIL modeling to simulate the spectral response of this forest type in both directions. Hyperion and PROSAIL results showed that the Enhanced Vegetation Index (EVI) and Photochemical Reflectance Index (PRI) were the two more anisotropic VIs, whereas the Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI) and the Vogelmann Red Edge Index (VOG) were comparatively less sensitive to view-illumination effects. When compared to the other VIs and because of the greater dependence on the near-infrared (NIR) reflectance, EVI showed a different spectral behavior. EVI increased from forward scattering to backscattering and with decreasing solar zenith angle (SZA) towards the end of the local dry season, due to reduction in shading and enhancement of the illumination effects. On the other hand, PRI was higher with increasing shading in the forward scattering direction, as deduced from the PROSAIL simulation. Results emphasized the importance of taking into account bidirectional effects when analyzing temporal series of VIs collected over tropical forests by imaging spectrometers with pointing capability or even by multispectral sensors with large field-of-view (FOV).     

针叶林冠层热点及多角度无人机遥感观测及特征分析

多角度遥感观测是研究植被冠层BRDF(Bidirectional Reflectance Distribution Function)特性的重要手段,但目前对森林冠层进行连续间隔采样的多角度遥感观测及数据较少,热点方向的观测尤为缺乏.本研究基于无人机多角度高光谱成像系统,在主平面上对针叶林冠层以等角度连续间隔采样进行多...     

无人机载高光谱成像设备研究及应用进展

无人机载高光谱成像设备因其机动灵活、操作简单等优点,被应用于诸多研究领域,具有广泛的发展前景。本文对当前无人机载高光谱成像设备的相关研究进展进行了系统的总结和评述。首先,基于高光谱成像仪的发展过程阐述了无人机载高光谱成像设备在数据获取中的显著优势;其次,从成像方式和研究现状方面指出了目前无人机载高光谱成像设备影像幅宽窄、成本高和国产化水平低的问题,并介绍了基于无人机高光谱成像设备的最新应用研究成果,针对当前的设备缺点提出解决方案;最后,指出了无人机载高光谱成像设备的未来发展趋势。本文可为无人机载高光谱成像设备的研制提供一定的借鉴和参考,也可为基于无人机载高光谱成像设备的应用提供一定帮助。     

Urbanization effects on vegetation cover in major African cities during 2001-2017

Urbanization effects on vegetation cover (VC) have been analyzed in many regions. However, little attention has been paid to Africa, which has undergone rapid urbanization in recent decades. In this study, MODIS land cover and enhanced vegetation index (EVI) data were used to examine urbanization effects on VC in 59 large cities in Africa during 2001–2017. The ΔEVI (urban EVI minus rural EVI) was used to represent urbanization effects on VC. Major findings include: (1) for 59 cities averaged, annual ΔEVI averaged from 2015 to 2017 was -0.116. Negative annual ΔEVI (i.e. urban EVI lower than rural) were observed in 56 of 59 cities. (2) For 59 cities averaged, urban area increased 17.9% from 2001 (262.8 km²) to 2016 (309.8 km²). (3) Annual ΔEVI decreased significantly (p<0.05) in 44 of 59 cities for the period 2001–2017, and annual average area of urbanization effects on VC increased significantly in 40 of 59 cities. For 59 cities averaged, the percentage of urban area with significant decreasing trends of annual ΔEVI was 60.0%. Spatially, cities near the Gulf of Guinea showed more significant decreasing ΔEVI than cities in other regions. In addition, the trends and spatial distributions of urbanization effects on VC differed little by seasons. These results suggested that urbanization and its effects on VC in Africa should arouse more attentions.     

联合PROSAIL模型和植被水分指数的低矮植被含水量估算

植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义.本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了...     

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m²/m². The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r² = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.     

4种常用植被指数的地形效应评估

植被指数已经广泛应用于地表植被覆盖监测,但是地形对植被指数的影响难以避免,却经常在大尺度遥感应用时被忽略。本文利用山区森林的Landsat TM数据计算SR、NDVI、RSR、MNDVI4种常用植被指数,评估了地形对这些植被指数的影响,并利用余弦校正和C校正模型分别对它们进行地形校正。结果表明,近红外和短波红外比红光波段的地形影响更为敏感,原因是更强的红光天空漫反射削弱了红光的地形影响。地形强烈影响非波段比值型植被指数(如RSR和MNDVI等),导致阳坡的植被指数相对偏小,阴坡的植被指数相对偏大,这种地形效应随坡度增大而显著增大。因此,利用非波段比值型植被指数反演山区植被参数时必须做严格的地形校正。与之相反,波段比值型植被指数(如SR和NDVI等)可以很大程度上消除地形影响,但是在大坡度情况下,地形影响仍然不能被忽略,而且此时SR比NDVI的地形效应更大。C地形校正效果好于余弦校正效果,特别是大坡度情况下更为明显。     

Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVI^THM), especially for agriculture and forest ecosystems. To apply NDVI^THM, knowledge of the proportion of vegetation cover (P_V) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the P_V on the estimation of LSE and LST when using NDVI^THM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The P_V was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate P_V and to compute LSE and LST. The results showed that the prediction accuracy of P_V improved using an artificial neural network (R²_CV = 0.64, RMSE_CV = 0.05) over classic vegetation indices (R²_CV = 0.42, RMSE_CV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated P_V affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.     

无人机遥感载荷验证场精密测量研究

无人机遥感载荷验证场精密测量研究,为我国首个航空遥感定标场的建立、世界上首个无人机遥感定标场的建立提供了基础测绘数据保障.本文详细介绍了无人机遥感载荷验证场(南方场)精密测量的具体实施过程,以及本项目的主要成果.为今后无人机遥感验证工作提供借鉴.     

植被结构及太阳／观测角度对NDVI的影响

在文献［１］中作者建立了计算多组分植被方向反射系数（ＢＲＦ）的综合解析模型。本文采用该模型研究植被空间结构对常用的归一化植被指数（ＮＤＶＩ）的影响，文中讨论了ＮＤＶＩ与叶（或植被其它组分）角分布（ＬＡＤ）、植被组分（如叶片）的特征尺度和它们在空间的散布方式，以及非叶器官面积在总面积中所占比例间的依赖关系，同时给出了ＮＤＶＩ随太阳／观测角度的变化情况。结果表明即使在叶面积指数（ＬＡＩ）固定不变时，冠层结构及植被组分光学性质的空间非均匀性对ＮＤＶＩ的大小及角分布也有十分显著的影响。通常ＮＤＷ随角度的变化是很大的，如果植被不同组分的光学性质差异很大，且事先不知道它们的空间散布方式时，那么利用ＤＮＶＩ就无法准确地估算出ＬＡＩ。但是对于组分随机分布的植被，利用远离“热点”区域的光谱资料可以使冠层其它结构参数的影响减至最小。     

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACT

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.     

兰州市南北两山植被覆盖度动态变化遥感监测

本文基于植被指数(NDVI)和植被覆盖度像元分解模型,建立了兰州市南北两山植被覆盖度遥感定量模型,在此基础上研究了兰州市南北两山1991年和2006年两个时期的植被覆盖度动态变化,结果表明:1991-2006年兰州市南北两山绿化工程区植被覆盖度总体呈上升趋势,低植被覆盖度面积减小,中高和高植被覆盖度面积增加,其中七里河工程区植被覆盖度变化最为明显,绿化效果较好,安宁工程区绿化效果相对较差,结果可为兰州市南北两山绿化工程提供科学依据。     

Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels

Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L.) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R² of 0.74 and 0.94, respectively. Results showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780–800 nm) and either green (540–560 nm) or red-edge (730–750 nm) spectral bands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively.     

无人机红外传感器改制及数据获取研究

针对云雾复杂气候条件下无人机可见光影像难以获取的问题,文章在分析了地质灾害应急遥感监测伴随的复杂气候条件的基础上,根据目前无人机传感器的构造原理,通过对各种红外滤片的分析和比较,研究基于红外滤片元器件的无人机传感器红外改制技术和红外影像数据增强方法,对航摄传感器进行红外改装。实验结果表明,无人机红外传感器在多云雾条件下能够获取高精度清晰影像,有效解决云雾条件下地质灾害应急数据获取难题,为地质灾害应急决策提供参考。     

Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R² > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.     

混合光谱分解模型提取水体叶绿素含量的研究

遥感监测水体叶绿素含量是水质遥感研究的难点之一。本文通过研究不同叶绿素含量水体反射率的光谱特征，确定了提取叶绿素a含量的最佳特征波段，建立了太湖水体叶绿素a的混合光谱模型。研究发现，混合光谱模型提取的水体叶绿素a百分比浓度与同步采样分析的叶绿素a浓度之间有较好的线性相关性。并基于TM和HEPERION图像利用此模型生成了叶绿索a浓度分布图。研究结果表明，混合光谱分解模型可以作为遥感监测水体叶绿素a含量的定量模型。     

Using high-resolution,multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot

Remotely sensed multispectral imagery, soils and graminoid samples from an abandoned cattle feedlot and adjacent wetlands were used to characterize plant vigour and soil nutrient distribution and evaluate the relationship between soil properties and vegetation reflectance. The feedlot lies on a sandy beach ridge, which likely mitigates the mobility of soil phosphorus. Soil phosphorus remains concentrated directly beneath the feedlot pens, where vegetation indices are low. In contrast, nitrate is transported through preferential pathways into the wetlands, where vegetation indices and plant vigour are high. Although spectral vegetation indices did not show any significant relationship with plant tissue nutrient concentration, the indices showed statistically significant relationships to some soil properties. Results of this study indicate that the abundance of nutrients in the soil does not necessarily enhance plant growth. This can limit the extent that remotely sensed vegetation indices can be used to evaluate soil nutrients concentrations.     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

高光谱数据的降维及Tabu搜索算法的应用

在高光谱影像的分类过程中,如何有效地降低特征空间的维数,又能保证原始数据所包含的丰富地物信息是一项十分重要而繁琐的工作。深入分析了这种降维的必要性,并针对当前常用的降维方法存在的问题,提出了运用Tabu搜索算法获取对分类最为有利的特征波段的思想。考虑到高光谱数据的特点,指出了算法运行中应该注意的若干关键参数设置问题。实验表明,Tabu搜索算法在求解质量和执行效率方面都有着良好的表现,可以用于高光谱数据的降维处理。