Fractional vegetation coverage (FVC) is an important ecological parameter reflecting the growth of regional plants. Existing FVC estimation is often based on vegetation indices, especially the Normalized Difference Vegetation Index (NDVI). However, NDVI can be oversaturated and easily affected by problems such as ‘shadow’ in images, which leads to a decrease in the precision of FVC estimation. In this study, the Normalized Shaded Vegetation Index (NSVI) was used to comprehensively compare the estimation ability of FVC with NDVI, and the differences in FVC estimation ability between NSVI and NDVI were explored. Based on two dimensions of ‘bright’ and ‘shadow’ hierarchies and FVC ranks, four evaluation systems of signal-to-noise ratio (SNR), index range, statistical model and dimidiate pixel model were selected from Sentinel-2A MSI, Landsat-8 OLI and Resource-1-02D (ZY1-02D) images and covered a variety of topographic landscapes. The results showed that: (1) the ability of NSVI to resist soil background is slightly smaller than NDVI; (2) the range of NSVI and NDVI in hyperspectral images is slightly larger than multispectral images, and the ability of NSVI to detect vegetation information in medium-high-rank and high-rank FVC areas has obvious advantages; (3) in the same kind of regression model, the goodness of fit of NSVI, NDVI and FVC was slightly higher in bright areas than in shaded areas, and the goodness of fit of the five regression models obtained from NSVI showed a significant advantage in bright areas compared with NDVI, with the best fit of the cubic curve model; (4) the estimation accuracy of the dimidiate pixel model based on NSVI and NDVI is slightly higher in bright areas than in shaded areas, and the estimation ability of NSVI is slightly better than that of NDVI in bright areas with medium-high-rank and high-rank FVC. NSVI and NDVI have their own advantages in SNR, index range, statistical model and dimidiate pixel model, so it is suggested that when remote sensing estimation of FVC is carried out, NSVI should be preferred in medium-high-rank and high-rank FVC areas, and NDVI should be preferred in low-rank FVC areas in shaded areas.

Chlorophyll is an important physiological parameter reflecting the health status of green vegetation. The change mechanism of chlorophyll and leaf spectrum under pest stress is complex. It is of great significance to analyze the relationship between chlorophyll and leaf spectrum in depth for pest detection. Taking Shunchang County, Nanping City, Fujian Province as the experimental area, the leaf SPAD and leaf spectrum of Phyllostachys pubescens under different damage scenarios were measured. Pearson correlation method was used to screen the leaf spectrum characteristic indexes, and multiple linear regression, ridge regression, random forest and XGBoost estimation models of leaf SPAD were established. By comparing the screening results of spectral characteristics and the estimation effect of the model, the relationship between chlorophyll and leaf spectral characteristics of Phyllostachys pubescens under the stress of Pantana phyllostachysae was analyzed. The results showed that: (1) SPAD of Phyllostachys pubescens leaves showed a downward trend with the increase of insect pests; (2) Compared with the undamaged state, the spectral characteristics of Phyllostachys pubescens leaves changed obviously under the stress of Pantana phyllostachysae, and the "green peak" and "red valley" tended to disappear, the slope of "red edge" decreased, and the reflectance of near infrared wavelength decreased. (3) The best spectral characteristics of leaf SPAD based on full sample fitting are VOG(2), R-515/R-570, CIred, PRI and NDVI705, and the best estimation model is multiple linear regression model (R-2=0.7537, RMSE=3.0150). (4) SPAD of Phyllostachys pubescens leaves was fitted based on samples with different damage degrees. The optimal spectral characteristic indexes were health: CIred, VOG(2), ARVI, R-515/R-570, DVI; mild hazard: RENDVI, RERVI and REDVI; moderate hazard: RENDVI, RERVI and REDVI; severe hazard: VOG(2), CIred, NDVI705; off year: PRI, NDVI705, VOG(1), CIred. The best estimation model is the multiple linear regression model, and the model accuracy is healthy (R-2 = 0.8823; RMSE=1.6388); mild hazard(R-2=0.1802; RMSE=3.3354); moderate hazard(R-2 = 0.3604; RMSE=3.8867); severe hazard (R-2=0.4677; RMSE=2.6018); off year (R-2=0.7324; RMSE=2.3754). It was found that with the increase of the damage grade, the spectral characteristic index of Phyllostachys pubescens leaves changed, and the estimation accuracy of the relational model showed a trend of sharp decline at first and then slowly rising. The model had better estimation effect on SPAD of healthy and young leaves, but poor estimation effect on SPAD of light-medium-severe damaged leaves. When the relationship between SPAD and spectral characteristics of Phyllostachys pubescens leaves tends to be disordered, it indicates that the harm of Pantana phyllostachysae may occur.

The detection of insect pests of Phyllostachys edulis plays a vital role in the growth of bamboo and the development of the bamboo industry. Based on the relationship between the hyperspectral canopy spectrum information and the pest degree of Phyllostachys edulis, the characteristic wavelengths, indices, and spectral parameters closely related to the pests in the canopy spectrum were extracted, and Fisher's discriminant analysis method was used to establish Phyllostachys edulis Pest degree detection model. Here are the wavelengths at 400 similar to 508, 586 similar to 693, 724 similar to 900 nm of the original spectrum, and the envelope curve to remove the characteristic wavelengths between 400 similar to 756 nm of the spectrum, 9 of canopy spectrum vegetation indices and 7 characteristic spectral parameters of the canopy are used as independent variables of the Fisher discriminant function to construct the discriminant function. Collected 300 groups of Phyllostachys pubescens leaf pest sample data, and randomly divided them into 210 modeling sets and 90 verification sets. According to the detection accuracy, Kappa coefficient and determination coefficient R-2 as the test standards, the effect of the established discriminant function is evaluated and compared. The results show that the inspection accuracy of the Fisher discriminant function established by the original spectrum, de-envelope spectrum, canopy index, and spectral parameters as independent variables are 84. 4%, 81. 1%, 79. 7%, 78. 7%, respectively. The inspection accuracy of Kappa coefficient is 0. 79, 0. 74, 0. 74, 0. 76, and R-2 is 0.89, 0.88, 0.88, 0.85, respectively. It can be seen that the function established by the Fisher discriminant analysis model has a good ability to detect the degree of pests of the Phyllostachys edulis, and the discriminant function established based on the original spectrum of the canopy has the best detection effect. The discriminant function established based on the original spectrum of the canopy of the hyperspectral data was used to detect the pest degree of Phyllostachys edulis in Yangmen and Tulong Village in Wufang Village, Dagan Town, Shunchang County, Fujian Province. The test result is that the bamboo forests in the two sample areas of Shanghu are mainly healthy, and the pest degree of the two sample areas of Yangmen is mainly moderate and severe. Therefore, based on UAV hyperspectral remote sensing, it is feasible for large-area detection of Phyllostachys edulis pests. The method and results can provide a reference for the exploration of pest detection and contribute theoretical support for pest detection based on canopy remote sensing.

Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the constructed model decreased by 5%. About half of the off-year leaf samples were misclassified into the damaged group. The identification of off-year leaves is a challenge for monitoring PPC damage using hyperspectral data. These results can provide practical guidance for monitoring PPC using remote sensing methods.

开展高光谱遥感影像的阴影检测研究有助于去除阴影,并进一步发挥其高光谱分辨率优势.以多角度高光谱影像PROBA/CHRIS为数据源,尝试从增大明亮区植被、阴影区植被、水体区3种典型地物间光谱的差异入手,利用连续投影算法(successive projection algorithm,SPA)选取特征波段,并分析典型地物在CHRIS影像原始波段及归一化差值植被指数上的光谱特征,由此构建该影像的归一化阴影植被指数(normalized shaded vegetation index,NS-VI).基于步长法设置合理阈值,对影像予以分类,并从分类精度及光谱差异增强效果两个角度评价NSVI对CHRIS影像阴影的检测能力.结果表明:B9和B15可作为构建CHRIS影像NSVI的特征波段;基于NSVI阈值法对CHRIS多角度影像予以分类,各角度影像3种地物的分类精度均在94％以上,总Kappa均大于0.89,0°影像的分类效果最佳;经掩模获取分类后3种地物的子影像,子影像光谱均值有差异,但考虑标准差后则发现其光谱重叠现象较为明显,表明NSVI可增强典型地物间的光谱差异,提高了光谱混淆像元间的可分性.通过进一步比较NS-VI与归一化阴影指数和阴影指数的阴影检测效果,亦证明了NSVI的阴影检测能力,说明所构建的NSVI能够应用于PROBA/CHRIS高光谱影像的阴影检测,可为该影像的阴影去除及阴影信息修复等工作提供重要支持.

Understanding the spectral characteristics of moso bamboo leaves damaged by Pantana phyllostachysae Chao can provide theoretical guidance for developing applicable and effective technologies to monitor the ecological safety of the bamboo forest. Compared with the traditional multispectral data, hyperspectral remote sensing can sense the subtle changes of host spectrum among different severity of Pantana phyllostachysae Chao. However, the related researches were still rare, and the spectral change mechanism of the host needs to be further summarized. Therefore, this study analyzed the spectral differences among healthy, damaged and off-yearmoso bamboo leaves based on 552 field measured spectrums. The characteristic variables that can act as indicators of leaves health status were selected. Finally, the model for monitoring the damage of leaves caused by Pantana phyllostachysae Chao was established using the XGBoost algorithm. The results show that: (1) with the increase of pest damage, the reflectance of damaged leaves gradually appeared "green low and red high" in visible-band, while the reflectance noticeably decreased in near-infrared band, and the reflectance of damaged leaves in shortwave infrared band was significantly higher than that of healthy leaves, especially in the two typical water vapor absorption bands (1 450 and 1 940 nm); (2) the reflectance of off-year leaves in visible and near infrared bands was significantly higher than healthy and damaged leaves; (3) the spectral characteristics of indentation-only leaves only slightly changed in comparison with healthy leaves, while the red band reflectance of leaves with red-brown disease spots increased to some extent, andthe leaves with gray-white disease spots completely lost the basic spectral characteristics of vegetation; (4) according to the feature importance score determined by the XGBoost algorithm, the contribution of each characteristic variable was PRI>FDVI576, 717 > NPCI>DSWI> VOG 1> RVSI> NDWI; (5) the overall average accuracy of the model to detect the damage by the pest was 74. 39%, and the accuracy for healthy, mild damaged, severe damaged, off-year, and moderate damaged leaves was 94. 55%, 74. 93%, 84. 12%, 71. 10%, and 33. 48% respectively.

刚竹毒蛾是竹的最主要食叶性害虫,严重影响竹林健康与竹业生产,掌握其空间分异规律对开展该虫害的监测与防控工作具有重要意义.以福建省延平区为研究区,基于刚竹毒蛾危害分布与地形数据,采用空间叠置分析等方法,统计不同地形条件下虫害分布占比,依此探究地形因子对该虫害的影响.结果表明,随着海拔、坡度的升高,虫害分布占比呈现先升后降特征,海拔500～800 m、坡度10°～24°为主要危害区域;东、东北、东南3个坡向的虫害分布占比最高,合计达57.09％;虫害与坡位的关系表现为"高集中"特征,主要分布于山脊,占比高达71.63％;其次为山谷,占比21.94％.该文证实地形因子对刚竹毒蛾危害具有重要影响,据此建议对海拔500～800 m,坡度10°～24°,东向、东北向、东南向及山脊、山谷区域的虫害予以重点监测与防控.

Islands face increasingly prominent environmental problems with rapid urbanization. Hence, timely and objective monitoring and evaluation of island ecology is of great significance. This study took the Pingtan Comprehensive Experimental Zone (PZ) in the east sea of Fujian Province of China as the research object. Based on remote sensing technology, four Landsat images from 2007 to 2017 and the remote sensing ecological index (RSEI) were used to explore the ecological status and space–time change. The results showed that from 2007 to 2011, the average RSEI decreased from 0.519 to 0.506, indicating that the ecological quality generally showed a slight downward trend, mainly due to large-scale development brought by the construction; by 2014, although the ecology of the original area improved, the overall ecology was still declining with 0.502 mean RSEI mainly because of large-scale reclamation projects; by 2017, the average RSEI rebounded to 0.523, which was attributed to the fact that ecological construction and protection were emphasized in the construction of PZ, especially in reclamation areas. In conclusion, the increase of large area bare soil will lead to the decline of regional ecology, but the implementation of scientific ecological planning is conducive to ecological restoration and construction.

As the most important pigment involved in photosynthesis of plant, the chlorophyll is an important indicator for monitoring bamboo pests. This study aims to establish the hyperspectral estimation model for the chlorophyll content of bamboo leaves under pests stress by wavelength screening of different spectral data sets, and provide a theoretical basis for monitoring the pests of bamboo by hyperspectral remote sensing. The test was carried out in Shunchang County, the bamboo production base in Fujian Province. The ASD FieldSpec 3 spectrometer was used to collect 102 bamboo leaves spectra of different pest levels, and the chlorophyll content of the corresponding leaves was determined by SPAD-502 chlorophyll meter. By comparing the spectral characteristics of bamboo leaves with different pest levels, the mechanism of estimating chlorophyll content with hyperspectral data was explored. The original spectrum (OS) of the bamboo leaves was subjected to continuum removal (CR) first derivative (FD), and continuum removal-first derivative (CR-FD), and the correlation between different spectral data and chlorophyll content was analyzed. The characteristic wavelengths of the four spectra were extracted by the successive projection algorithm (SPA). Four spectral datasets were divided by sample set partitioning based on joint x-y distances method (SPXY) and random method. Combined with multiple stepwise regression (MSR), the chlorophyll content estimation model of bamboo leaves was established, and the effects of spectral transformation and sample partitioning on estimating chlorophyll content were analyzed. The results showed that there were significant differences in the spectral reflectance of bamboo leaves with different pest levels. The main manifestations were the gradual disappearance of the "green peak" and "red valley" in the visible light range, the "red edge" was levelled and the near-infrared wavelength reflectance was reduced. The spectral transformation could effectively improve the correlation between the spectrum and chlorophyll content, and the correlation coefficient between the CR-FD spectrum and chlorophyll content at 724 nm was the largest. The characteristic wavelengths of different spectral data sets extracted by the successive projection algorithm were concentrated in the green band, red band, and "red edge", and the multiple selected wavelengths were located in bands (600 similar to 750 nm) that highly correlated with chlorophyll content. The MSR model based on SPXY sample partitioning method could significantly improve the estimation accuracy of chlorophyll content compared with the random sample partitioning method, in which R-2 and RPD increased by 0. 1 and 0. 5, and RMSE decreased by 0. 7 on average. The multiple stepwise regression model established by CR-FD spectrum characteristic wavelengths combined with SPXY sample partitioning method had the highest accuracy for estimating chlorophyll content of bamboo leaves, and the R-2, RMSE, RPD were 0. 835, 2. 604 and 2. 364 respectively, which could accurately estimate the chlorophyll content of bamboo leaves under pests stress.