The Evaluation of Efficiency of Color Metrics in Monitoring Robiuia PseudoacaciaPhenology based on RGB Images

The widely application of digital camera，especially the appearance of time\|lapse camera，inspired the monitoring vegetation seasonal dynamic using time\|series RGB imagery.Extract critical temporal stage of vegetation dynamic is the most useful aspect.Color index including Green Excess Index(ExG)，Green Chromatic Coordinate(Gcc)，Green Red Vegetation Index(GRVI)，and Hue based on HSL(Hue) are the most widely used metrics.However，their efficiency for specific plants may differ.In this study，the efficiency of four color indices mentioned above were tested taking RGB imagery of Robiuia Pseudoacacia as data source.The critical timing point of plant growth and senescence reflected by greenness of leaves were used to pick out the most efficiency color index.The results showed that the best SOS(Start of the Seasonality)，EOS(End of the Seasonality) can be extracted using ExG following the     

基于大疆精灵4无人机多光谱影像的西沙植被监测

西沙群岛位于热带,常年多云,在光学卫星数据获取时易受天气影响导致缺失,使得地表动态监测困难。为解决这一问题,探讨无人机低空平台对西沙群岛植被的监测能力,选取大疆精灵4多光谱无人机,通过5个多光谱波段提取4项植被指数,包括归一化差值植被指数（NDVI）、叶绿素指数（GCI）、绿色归一化植被指数（GNDVI）以及归一化绿红差值指数（NGRDI）,评估了2020年5月西沙群岛北岛的植被生长状况,并结合关键气象参数以及Worldview2卫星光学影像对比分析了2020年5月和2018年5月北岛植被生长变化及其潜在归因。研究结果表明：2020年5月北岛平均NDVI、GCI、GNDVI和NGRDI别为0.30、0.84、0.26和0.05,反映出植被覆盖度较低,可能存在枯黄现象,与地面监测结果一致;2020年人工管理植被区和自然生长植被区各项指数差异由2018年的-23%—15%增加到15%—40%,表明2020年自然生长植被长势显著差于人工管理植被,反映出较强的环境胁迫;气象数据显示2020年4月—5月该地区日平均温度较常年同期升高、累计降水量减少、平均风速增大同时增加了土壤水分亏缺,可能是引起...     

利用AMSR-E遥感数据反演地表温度——以2008年广东省春季寒害为例

基于2008年1月25日至2008年2月5日期间的AMSR-E/Aqua L2A微波亮度温度数据,以广东省为研究对象,依据微波极化差异指数（MPDI）、归一化植被指数（NDVI）和比率植被指数（RVI）等3种植被指数,将广东省地表植被覆盖情况分为裸地、草地、灌木林、针叶林和阔叶林等5种类型,利用逐步回归分析方法,建立了基于不同植被覆盖类型的微波亮度温度与地面气象温度多元回归模型。同步地面气象温度数据验证表明,本文建立的基于地表植被覆盖分类的多波段地表温度反演模型,地表温度反演精度基本可达到3.0℃,其中有大约86%的地区地表温度反演精度可以控制在2.5℃以内,为广东省作物寒害预测提供了微波遥感技术支持。     

Impact of multi-angular CHRIS/PROBA data on their empirical relationships with tropical forest biomass

Images from the Compact High Resolution Imaging Spectrometer (CHRIS), on board the space platform Project for On Board Autonomy (PROBA), were acquired in the Brazilian Amazon region in 62 bands (410–1050 nm) at different view angles. They were evaluated for angular variations in Bidirectional Reflectance Factor (BRF), selected vegetation and anisotropic indices, and their relationship with the Above Ground Biomass (AGB) of some forest successional stages using an empirical approach. Results showed that correlations between AGB and reflectance were influenced by the vegetation anisotropy, which was stronger in the visible than in the near-infrared. The anisotropy increased from the forward (?36°) to the backward (+55°) scattering direction, was greater in the blue and green bands and decreased towards the near-infrared. As a result of this behaviour, several narrow and traditional vegetation indices showed correlation coefficients (R) that varied with view angle. The backscattering–forward scattering contrast, represented by anisotropic indices such as the Normalized Difference Anisotropic Index (NADX) and the Hot spot–Dark spot Normalized Difference Vegetation Index (NDVI_TD), presented only a small improvement of the relationships with AGB when compared with the performance of the other traditional vegetation indices at nadir viewing.     

Estimations of Chlorophyll Content from Vegetation Indicesby Using PROSPECT and DART

With the aid of a well known leaf optical model PROSPECT and a canopy scale model DART (Discrete Anisotropic Radiative Transfer),sensitivities between chlorophyll content and six different vegetation indices were investigated by simulating eucalyptus,one of a dominant fast growing tree in China,as an example.Vegetation indices used here include Normalized Difference Vegetation Index (NDVI),Structure Insensitive Pigment Index (SIPI),Colouration Index (COI),Simple Ratio Index (SR),Cater Index (CAI),and Red edge Position Linear Interpolation (REP_Li).Results indicate that at the leaf scale,COI and SIPI are sensitive to the LCC (Leaf Chlorophyll Content)as the Chlorophyll Content changes.Meanwhile,no obvious saturation phenomenon is observed for these two indices compared to other indices.Further investigations show that all these vegetation indices are incapable of estimating LCC at the canopy scale,due to significant influences from LAI(Leaf Area Index).Nevertheless,it suggests that SIPI and COI can be applied to estimate the CCC (Canopy Chlorophyll Content).     

Spectral response of benthic diatoms with different sediment backgrounds

Remote sensing of terrestrial vegetation uses a wide range of vegetation indices (VIs) to monitor plant characteristics, but these indices can be very sensitive to canopy background reflectance. This study investigated background influences on VIs applied to intertidal microphytobenthos, using a synthetic spectral library constituted by a spectral combination of three contrasting types of sediment (sand, fine sand, and mud) and reflectance spectra of benthic diatom monospecific cultures obtained in controlled conditions. The spectral database exhibited, for the same biomass range (3-182 mg chlorophyll a m^− 2), marked differences in albedo and spectral contrast linked to sediment variability in water content, grain size, and organic matter content. Several VIs were evaluated, from ratios using visible and near infrared wavelengths, to hyperspectral indices (derivative analysis, continuum removal). Among the ratios, the Normalized Difference Vegetation Index (NDVI) appeared less sensitive to background effects than VIs with soil corrections such as the Perpendicular Vegetation Index (PVI), the Soil-Adjusted Vegetation Index (SAVI), the Modified second Soil-Adjusted Vegetation Index (MSAVI2) or the Transformed Soil-Adjusted Vegetation Index (TSAVI). The lower efficacy of soil-corrected VIs may be explained by the structural differences and optical behavior of soil vs. canopies compared to sediment vs. microphytobenthos biofilms. The background effects were minimized using Modified Gaussian Model indices at 632 nm and 675 nm, and the second derivative at 632 nm, while poor results were obtained with the red-edge inflection point (REIP) and the second derivative at 675 nm. The least sensitive index was the Phytobenthos Index which is very similar to the NDVI, but uses a red wavelength at 632 nm instead of 675 nm, to account for the absorption by chlorophyll c. The modified NDVI₇₀₅, where the 705 nm wavelength replaces the red band, showed moderate background sensitivity. Moreover, the NDVI₇₀₅ and the Phytobenthos Index have the additional relevant property of being less sensitive to the index saturation response with increasing biomass. Unfortunately, these VIs cannot be applied to broad-band multispectral satellite images, and require sensors with a hyperspectral resolution. Nevertheless, this study showed that the background influence was not a limitation to applying the ubiquitous NDVI to map intertidal microphytobenthos using multispectral satellite images.     

一种基于植被指数的遥感影像决策树分类方法

以江苏省徐州市为研究区,采用2000年ETM+多光谱影像作为遥感信息源,选择影像的光谱特征和归一化植被指数（NDVI）、绿度植被指数（GVI）、比值植被指数（RVI）等10种植被指数作为分类特征,基于See5决策树学习软件构建分类决策树,实现了研究区景观格局的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像的分类,植被指数参与到决策树分类中能够提高分类的总体精度。     

Comparison between vegetation change analysis in Kenya based on AVHRR and SeaWiFS images

An assessment of the suitability of the Advanced Very High Resolution Radiometer (AVHRR) vegetation index to estimate land degradation in semi‐arid areas has been carried out, comparing its behaviour with that of vegetation indices based on Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) images. Notwithstanding the importance of the classic Normalized Difference Vegetation Index (NDVI) indicator, based on red–NIR channels, several studies have identified some limitations related to its use, such as its dependence on the atmospheric profile, saturation problems, non‐linearity in biophysical coupling with Leaf Area Index (LAI) and canopy background contamination. The relatively recent Enhanced Vegetation Index (EVI) overcomes these limits, using the information related to the blue channel and a soil adjustment factor. SeaWiFS data allow the computation of both vegetation indices. On the other hand, the NDVI based on AVHRR can be computed back in time to the 1980s, allowing a sufficient time span to obtain information on the desertification trend of the considered region (northern Kenya). In conclusion, taking advantage of both datasets, the accuracy of a change detection technique based on the classic NDVI has been confirmed as suitable for revealing any desertification trend.     

Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices

In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types.     

Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data

A simulated canopy reflectance dataset for a total of six channels in visible, near-infrared (NIR) and shortwave-infrared (SWIR) region, corresponding to Landsat Thematic Mapper (TM) was generated using the PROSAIL (PROSPECT+SAIL) model and a range of Leaf Area Index (LAI), soil backgrounds, leaf chlorophyll, leaf inclination and viewing geometry inputs. This dataset was used to develop and evaluate approaches for LAI estimation, namely, standard two-band nonlinear empirical vegetation index (VI)–LAI formulation (using Normalized Difference Vegetation Index/simple ratio (NDVI/SR)) and a multi-band principal component inversion (PCI) approach. The analysis indicated that the multi-band PCI approach had a smaller rms error (RMSE=0.380) than the NDVI and SR approaches (RMSE=2.28, 0.88), for an independently generated test dataset.