Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

[Feasibility of monitoring karst standing conditions with vegetation spectra].

Karst regions are typically ecological fragile zones constrained by geological setting, which resulted in high heterogeneity of vegetation standing conditions. The karst vegetation was featured with stone, dry and high calcium carbonate content growth conditions. Based on vegetation spectral analysis and canonical correspondence analysis (CCA), the present study aimed to examine the feasibility of using vegetation spectra to monitor the heterogeneous karst standing conditions. The results showed that there were significant differences between karst vegetation and non-karst vegetation within the spectral range of 1 300-2 500 nm reflectance and 400 - 680 nm first-derivative spectra. It was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst regions. Ordination diagrams of CCA could distinguish the differences of karst vegetation and non-karst vegetation. Our study demonstrates that vegetation spectra are highly related to karst standing conditions and it is feasible to monitor karst standing conditions with vegetation spectral features.

[Monitoring spatio-temporal spectral characteristics of leaves of karst plant during dehydration using a field imaging spectrometer system].

As the supplement of spaceborne and airborne imaging spectrometer system, field Imaging spectrometer system spans a very broad range of applications. Imaging spectrometer system of this new kind could provide vital information especially for which spaceborne or airborne remote sensing could not be competent, such as proximal detection of plant population, individual plant or plant organs for site-specific management in precision agriculture. A new self-developed imaging spectrometer system was utilized to monitor spatio-temporal dynamics of spectral changes of plant leaves in response to dehydration. lThe phenomenon of blue shift of red edge of plant leaves was successfully detected and visualized in the form of image series. The patterns of photochemical reflectance index (PRI) of leaves during dehydration were compared and confirmed by fluorescence parameter quantum yield. Our results show that FISS has good spectral and radiometric properties and could be used in quantitative researches and precise information mapping.

Indication of the expression of transgene in rice plant based on hyperspectral remote sensing technique II--growth monitoring of samples in the contrast experiment.

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works, the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively, fulfilling the growth monitoring of the transgenic samples. The spectral parameters (spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts, and thus could be applied as indicators of biophysical or biochemical processes changes of plant. By ASD portable field spectroradiometer with high-density probe, fine foliar spectra of 8 groups were obtained. By analyzing spectral angle and continuum removal, the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis. By investigating spectral indices of the samples, the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content. In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated. The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches. By this technique, quantitative and qualitative results of sample spectra could be provided as prior knowledge, as certain orientation, for laboratory professional advanced transgenic breeding study.

[Study on the relation between fraction cover and mixed spectra in karst environment].

Karst rocky desertification is one of the most serious eco-environmental problems of land degradation in karst regions, southwest China. The fractional cover of vegetation and exposed bedrock are the main land surface symptoms and essential assessing indicators of karst rocky desertification. To assess the extent of rocky desertification in complex karst environments, the information of multiple land cover types fraction is needed. Based on in situ spectral reflectance data, this study proposed several spectral indices and explored the relationship between spectral features of main land cover types and their responding fractional cover. The results showed that spectral indices have much higher correlation coefficients with fractional cover than does spectral reflectance. Vegetation indices have good linear relation with fractional cover of photosynthetic vegetation (PV). The proposed spectral indices have high correlation coefficients with fractional cover of non-photosynthetic vegetation (NPV) and bare soil, with R2 0.70 and 0.73, respectively. Lower correlation coefficients (R2 = 0.55) with the factional cover of exposed bedrock, were observed. The absorption depth of four forms of the proposed indices has the highest correlation coefficient with the fractional cover of NPV, bare soil, and exposed rock. This study indicates that hyperspectral remote sensing has the potential for the extraction of karst rocky desertification information.

[Responses of ecosystem service values to landscape pattern change in typical Karst area of northwest Guangxi, China].

Based on 3S technique, this paper examined the responses of ecosystem service values (ESVs) to the landscape pattern change in the typical Karst area of northwest Guangxi, China in 1985, 1990, 2000 and 2005. The ESVs in the study area had close relations to landscape area, fragmentation degree, complexity of patch shape, areas of critical type, patch connectivity, and patch richness. It was linearly positively correlated with landscape area and had curvilinear positive correlations with patch index (LPI), contagion index (CONTAG), aggregative index (AI), effective mesh (MESH), proportion of like adjacencies (PLADJ), and tended to be increased with increasing patch area and patch connectivity of critical landscape type. The ESVs had curvilinear negative correlations with division index (DIVISION), split index (SPLIT), and patch richness (PRD), and decreased with increasing patch fragmentation and shrinking patch size. Therefore, it would be important to protect the critical landscape types such as woodland, shrub, and grassland, and to increase the patch size and connectivity to 'avoid further fragmentation. Moreover, it would be necessary to reduce the frequency and severity of disturbances to ensure the ESVs growth and the sustainable development of the study area.

[Detection of the expression of transgene in rice plant based on hyperspectral remote sensing technique].

The present study aims to identify the expression of transgene in given rice plant samples in certain conditions. To avoid external noise caused by temperature change and water-loss, field spectrum was collected with ASD field spectrometer in natural state. The study calculated the mean spectrum of samples as main data set analyzed which were controlled by inner clustering coefficient to ensure data quality. By mean spectrum, the noise from random distinctions in few individual cultivators, which could not be expressed in the class stably, could be weakened even with filtering. With the help of parameters, such as red edge and green peak, this study gave qualitative spectral differences between transgenic samples and their parents. The results show that the transgenes in rice plant were expressed and influenced the samples. Moreover, it was found that the parameters of area are more suitable for describing the differences/changes of the samples, while PRI and SR-PRI are more sensitive to indicate them. Most of the above results could be found on the continuum-removal spectrum curve of samples. These conclusive results demonstrate that hyperspectral remote sensing technique has good prospects and application potential in transgene expression detection and monitoring, especially in plant breeding process.

[Relationships between soil and environment in peak-cluster depression areas of Karst region based on canonical correspondence analysis].

Karst region is a typical ecological fragile zone, with small environmental and anti-interference capability. Many environment factors, such as different land use, elevation and topographic relief, have significant influence on spatial variability of soil properties, resulting in abnormal fragments of landscape and severe rocky desertification. The method of canonical correspondence analysis (CCA) was employed to reveal the relationships between soil and environment in peak-cluster depression areas of karst region, using the samples from field survey in Guzhou. Results show that spatial variability of TN, AN, SOC, AP, AK and C/N are greater than TP, TK, and pH, with the similar trend among TN, AN and SOC. Furthermore, CCA shows that SOC, TN and AN have high correlation with bare rock rate and grade, AK and vegetation type, together with the significant correlation between C/N and soil depth, while the environment has little effect on TK, TP and pH. Environmental factors that affect soil properties are listed as the following order: land use > vegetation type > bare rock rate > aspect > soil depth > elevation. Except TK and pH, soil properties are significantly affected by different land-use types.