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As an important part of the ecosystem, green vegetation coverage is crucial to people’s sensory and mental health. Using reliable data sets to classify and identify the green vegetation cover on the land surface and explore its spatial distribution law can provide important reference for the work of regional ecosystem managers and urban planners. The optimization of effective screening methods for green vegetation coverage areas is an important requirement to measure the surface vegetation status. UAV aerial images feature high definition, large scale, small area and high up-to-dateness. However, at present, there are few studies based on the reliable UAV aerial image system to identify green vegetation cover and further explore its spatial changes. In this study, 701 residential neighborhoods in Beijing were taken as the research objects, and the green vegetation of 7,695 sample points was identified by UAV. The green vegetation coverage was measured, and the spatial distribution pattern of green vegetation in different land surface areas was quantitatively compared. The results show that the image processing method proposed in this paper can effectively detect the boundary of green vegetation cover area from UAV aerial images, the correlation of texture segmentation is good, and the segmentation performance is better than other methods. The distribution of green vegetation cover in the research target area is uneven, with 63.79% of the research area having relatively low (Level 2) and medium (Level 3) green vegetation coverage, which indicates that the green vegetation coverage area in the research area is insufficient to meet the needs of regional ecosystem development. The characteristics of green vegetation cover in 16 districts in the study area are different, showing different spatial distribution patterns; except Xicheng District, there are 211 points without landscape in the area covered by green vegetation in 15 districts. The results can provide support for urban land surface planning and management.

The significance of identifying apple orchard land and monitoring its spatial distribution patterns is increasing for precise yield prediction and agricultural sustainable development. This study strived to identify the optimal time phase to efficiently extract apple orchard land and monitor its spatial characteristics based on the random forest (RF) classification method and multitemporal Sentinel-2 images. Firstly, the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), and Difference Vegetation Index (DVI) between apple orchard land and other green vegetation (other orchards, forest and grassland) during the growing stage were calculated and compared to identify the optimal time phase for apple orchard land extraction; the RF classifier was then constructed using multifeature variables on Google Earth Engine to efficiently identify apple orchard land, and the support vector machine (SVM) classification results were used as a comparison; GIS spatial analysis, a slope calculation model, and Moran’s I and Getis-Ord GI* analysis were employed to further analyze the spatial patterns of the apple orchard land. The results found the following: (1) April, May, and October were the optimal time phases for apple orchard identification. (2) The RF-based method combining coefficients of indexes, the grayscale co-occurrence matrix, and 70% of the ground reference data can precisely classify apple orchards with an overall accuracy of 90% and a Kappa coefficient of 0.88, increasing by 9.2% and 11.4% compared to those using the SVM. (3) The total area of apple orchard land in the study area was 485.8 km2, which is 0.6% less than the government’s statistical results. More than half (55.7%) of the apple orchard land was distributed on the gentle slope (Grade II, 6–15°) and the flat slope (Grade I, 0–5°); SiKou, Songshan, and Shewopo contained more than 50% of the total orchard land area. (4) The distribution of apple orchard land has a positive spatial autocorrelation (0.309, p = 0.000). High–High cluster types occurred mainly in Sikou (60%), High–Low clusters in Songshan (40%), Low–High clusters in Sikou (47.5%), and Low–Low clusters in Taocun and Tingkou (37.4%). The distribution patterns of cold and hot spots converged with those of the Local Moran Index computation results. The findings of this study can provide theoretical and methodological references for orchard land identification and spatial analysis.

To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance.

[Objective] The land use of the Yellow River Basin(YRB) has a significant impact on the ecology and economy of the basin. It is extremely important to study the temporal and spatial change of land use in the YRB for the sustainable development of the basin. [Methods]Taking the YRB as the study area, the land use types in the basin were classified and evaluated based on remote sensing monitoring data of five years of land use. The temporal and spatial characteristics of land use in four time series units from 2000 to 2005, 2005 to 2010, 2010 to 2015, and 2015 to 2020 were analyzed by using geo-information Tupu method, and the internal factors of land use change were analyzed from the perspective of increase and decrease. [Results] The results show that from 2000 to 2020, the land type of the YRB is dominated by grassland, the growth trend of woodland is higher than that of grassland, the growth trend of built-up land is higher than that of water area, and the cultivated land and unused land show a decreasing trend; different time series units show similar land changes, with the strongest in 2000-2005. The Tupu units of "cultivated land →grassland", "grassland→cultivated land", "grassland→ unused land" and "unused land→ grassland" are the main changes in the four time series units ;In the rising trend Tupu, grassland is converted from a large number of cultivated land, and the increase of grassland has a tendency to occupy cultivated land. In the falling trend Tupu, a large number of grassland have been converted into cultivated land, and the increase of cultivated land is inclined to occupy grassland. All of them have the most obvious changes in Gansu, the southern part of Ningxia and the northern part of Shaanxi. From the perspective of spatial location, the two may be transformed into each other, and the distribution is relatively concentrated in the pilot areas of the policy of returning farmland to forests.[Conclusion] The analysis of temporal and spatial changes of land use in the YRB in the past 20 years provides a realistic case for the feedback role of land policy in society and environment, and lays a foundation for the harmonious development of human and ecology.

From the perspective of village classification, a set of methods for accurately measuring the potential of rural settlement consolidation are proposed. Taking Feicheng in Shandong Province as the research area, combined with the corresponding planning, a classification and evaluation system for rural residential areas was constructed to classify rural residential areas from the four levels of natural resources, economy and society, supporting facilities and livability. The theoretical and practical potential of one type of rural settlements (relocating and merger village) is mainly calculated, and the potential scale, potential level and potential spatial distribution of different regions are analyzed. (Level and spatial distribution are analyzed. The results showed that the 621 rural settlements can be divided into five types: 148 urban-rural integration villages (URIV), 41 historical and cultural villages (HCV), 56 cluster developing villages (CDV), 155 keeping and limiting villages (KLV) and 221 relocating and merger villages (RMV). According to calculations, the theoretical potential of relocating and merger village areas was 1971.47 hm2, accounting for 19.52% of the total scale of Feicheng residential areas; the comprehensive correction coefficient of each region was 0.48 - 0.70, and the revised actual potential is 1082.68 hm2, accounting for the theoretical potential 54.92% of the total. There were large spatial differences in the scale of potential in different regions. In general, the theoretical and practical potential of the central and southern regions was higher than that of the northern regions.

HighlightsMonitored the canopy chlorophyll content of apple trees using hyperspectral reflectance information.Constructed support vector machine combination regression model (C-SVR) based on five-fold cross validation and support vector machine regression approach.Compared estimation accuracy of ensemble learning models (C-SVR, RF), machine learning models (SVR, ANN), and PLSR models for apple canopy chlorophyll content.Abstract. Rapidly and effective monitoring of the canopy chlorophyll content (CCC) of apple trees is of great significance for crop stress monitoring in precision agriculture. This study attempted to use hyperspectral vegetation indices (VIs) to estimate the CCC of apple trees based on ensemble learning approach. In this study, vegetation indices combined by any two wavelengths from 400 to 1100 nm were constructed to calculate the correlation coefficient with the CCC in apple. We constructed a partial least squares regression model (PLSR), artificial neural network regression model (ANN), support vector machine regression (SVR), random forest regression (RF) model and support vector machine combination regression model (C-SVR) based on combinations of VIs to improve the estimation accuracy in apple CCC. The results showed that the correlation coefficients between NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), DVI (572,532), and apple CCC were all above 0.76. The CCC estimation model using the RF and C-SVR approach constructed by the NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), and DVI (572,532) achieved the better estimation results, and the R2V, RMSEV, and RPDV values of models were 0.76, 0.131(mg . g-1), 2.04 and 0.78, 0.127(mg . g-1), 2.12, respectively. Compared with the PLSR, ANN, and SVR model, the R2V and RPDV values of C-SVR model were increased by 4%, 1.2%, 3.8%, and 5.0%, 28.4%, 7.1%, respectively. The results show that using C-SVR approach to estimating the apple CCC can realize high accuracy of quantitative estimation. Ensemble learning approach is an effective method for monitoring the nutrient status of fruit trees based on hyperspectral technique. Keywords: Apple tree canopy, Chlorophyll content, Crop stress monitoring, Ensemble learning, Hyperspectral, Vegetation index.

Timely, nondestructive and effective determination of canopy nitrogen content provides an important reference value for real-time monitoring of total nitrogen status of apple trees. Different processing methods are used to mine hyperspectral information to estimate nitrogen content. However, the overprocessing or underprocessing of hyperspectral data leads to the underutilization of spectral information. The primary objective of this study was to establish a model for estimating the nitrogen content of apple tree canopy by red-edge parameters based on fractional differential. The Grunwald–Letnikov fractional difference algorithm was used to extract the red-edge parameters from the hyperspectral canopy data, so as to develop the support vector machine (SVM) and random forest (RF) models. The results showed that the correlation with nitrogen content can be enhanced by differential spectroscopy compared with the original spectrum. The spectral parameters such as red-edge peak area (Sr(α)) obtained by fractional differential and logarithmic transformation processing and the correlation coefficient with nitrogen content can reach 0.6 or greater. The R2 of SVM and RF models constructed with red-edge parameters reached 0.56 (RMSE was 1.51 for SVM) and 0.94 (RMSE was 0.84 for RF), respectively. The RPD greater than 2 indicates that both models could be used for nitrogen estimation, and the RF model has a better predictive effect (RPD was 2.17 for SVM, RPD was 2.43 for RF). It provides an effective method for real-time monitoring of apple canopy nitrogen status and provides theoretical and technical support for hyperspectral information mining and data processing.

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time-consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, Shandong Peninsula, China were measured by an ASD FieldSpec 3 portable object spectrometer. Raw spectral reflectance datewas transformed by four methods. These were: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA+FD), NWMA withstandard normal variate (NWMA+SNV), and NWMA with min-max standardization (NWMA+MS). These data were analyzed and correlated with SOM content. The evaluation model was built by support vector machine regression (SVM) using sensitive wavelengths. The results showed thatNWMA+FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA+FD method were 917 nm, 991 nm, 1007 nm, 1996 nm, and 2267 nm. The SVM model established by the above five sensitive wavelengths was significant (R c 2 = 0.875, RMSE c = 0.107g·kg −1 , R v 2 = 0.853, RMSE v = 0.097g·kg −1 ). The results indicated that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients.

To promote the use of imaging spectroscopy to assess the nutritional status of apple trees, the models to estimate the chlorophyll content of apple leaves were explored. Spectral data for apple leaves were collected with an imaging spectrometer and then preprocessed with the nine-point moving weighted average method. Correlation analyses were conducted between chlorophyll content and mathematically transformed spectral data. Wavelengths sensitive to chlorophyll content were selected on the basis of the highest correlation coefficients, and partial least squares (PLS), support vector machine (SVM), and random forest (RF) models to estimate chlorophyll content were established and tested. The wavelengths sensitive to chlorophyll content were 414, 424, 429, 439, and 577 nm. The best model was the SVM model with wavelength data subjected to a second order differential of the logarithm transformation log R414)”, (log R424)”, (log R429)”, (log R439)”, (log R577)” as the independent variables. For this model, the coefficient of determination V-R2 was 0.7372, the root mean square error V-RMSE was 0.4477, and the residual predictive deviation V-RPD was 1.8810. Among all the models, this SVM model had the highest V-R2 and V-RPD values and the lowest V-RMSE value.

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

Tree species recognition is important for remote–sensing mapping and dynamic monitoring of forest resource. However, the complex phenological cycle poses a challenge to remote–sensing recognition of deciduous tree species in mountainous areas, and the selection of temporal phase is particularly important to improve recognition accuracy. Multispectral images of Ziyuan–1 02C (ZY–1 02C) and Ziyuan–3 (ZY–3) at three phenological phases of spring, autumn and winter (12 May, 29 September and 7 December, recorded as T5–12, T9–29 and T12–7) are selected to optimize sensitive spectral indices. Support vector machine (SVM) and maximum likelihood model (MLE) are constructed to explore the optimum phase of recognizing on Quercus acutissima (O. acutissima ) and Robinia pseudoacacia (R. pseudoacacia) in Mount Tai. The results showed the average spectral reflection intensity of O. acutissima was higher than that of R. pseudoacacia Compared to other phenological periods, the most significant spectral differences between O. acutissima and R. pseudoacacia were found in the spring (12 May), which was identified as the optimum phenological phase. Band 4 is the most sensitive band in all the three phases for the tree species recognition. Moreover, the overall recognition accuracy of deciduous tree species on 12 May reached 89.25%, which was significantly higher than the other two phases. On 12 May, the recognition accuracies of SVM based on sensitive spectral indices of up to 93.59% for O. acutissima and 85.44% for R. pseudoacacia, were higher overall than that of the MLE. Sensitive spectral indices introduced were shown to significantly improve the recognition accuracy for tree species over a single sensitive band. The study is expected to facilitate the precise recognition and forestry management on Mount Tai.

We took distribution visualization of chlorophyll content in apple leaves to estimate the nutrient content and growth levels of apple leaves. 130 mature and non-destructive apple leaves were collected, and imaging spectroscopy data were collected by SOC710VP hyperspectral imager. The chlorophyll content of the leaves was determined on the spectral information of the leaves. After pre-processing, we took linear wavelength stepwise regression method to choose the sensitive wavelength of chlorophyll content. And then we established partial least squares, principal component analysis and stepwise regression model. Finally, the chlorophyll content distribution visualization was realized. The results showed that the sensitive wavelengths of the chlorophyll content were 712.50 nm, 509.95 nm, 561.22 nm, 840.62 nm, 696.67 nm and 987.91 nm. The R2, RMSE, RE of the optical chlorophyll content estimation model, and the principal component analysis regression model, were 0.800, 0.319 and 26.4%. The chlorophyll content of each pixel on the hyperspectral image of apple leaves was calculated by the best estimation model and we completed the visualization distribution of chlorophyll content, which provided a technical support for the rapid detection of nutrient distribution.

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data.

Bumald spirea (Spiarea bumalda Burv.) is an important ornamental tree widely grown in northern China. In August of 2006, spirea plants exhibiting symptoms of witches'-broom, stunting, yellowing, and shoot dieback were found at an incidence of 5 to 15% in Qingzhou City, Shandong Province, China. Total DNA was extracted separately from 0.1 g of phloem tissue from leaf midribs and stems of six symptomatic and six asymptomatic plants with a modified cetyltriethylammonium bromide (CTAB) method (3). Resulting DNA samples were analyzed for phytoplasma DNA by a nested PCR assay using phytoplasma universal 16S rDNA gene primer pairs R16mF2/R16mR1 and R16F2n/R16R2 (2). These primers amplified 1.5- and 1.2-kb products, respectively, from DNA of all symptomatic plants only. Restriction fragment length polymorphism (RFLP) analysis of the 1.2-kb 16S rDNA product using enzymes AluI, MseI, and HhaI indicated that all symptomatic plants contained a group 16SrI (aster yellows group) subgroup B (16SrI-B) phytoplasma strain (4). A 16S rDNA sequence derived from this strain (GenBank Accession No. EF176608) was most similar (99.8 and 99.6%) to those of severe aster yellows (GenBank Accession No. M86340) and Maryland aster yellows (GenBank Accession No. AF322644) phytoplasmas, respectively, thereby confirming strain identity based on RFLP analysis. A phytoplasma (Spiarea stunt phytoplasma, GenBank Accession No. AF190228), which belongs to X-disease group (16SrIII), was reported to infect spirea and probably be lethal to S. tomentosa in New York (1,4). The phytoplasma reported here shared low identity (90.8%) with Spiarea stunt phytoplasma, but also caused dieback of spirea shoots. The epidemiology and economic impact of this disease need further intensive investigation. To our knowledge, this is the first report of spirea witches'-broom disease and of its association with a subgroup 16SrI-B phytoplasma in China. References: (1) H. M. Griffiths et al. Can. J. Plant Pathol. 16:255, 1994. (2) D. E. Gundersen and I.-M. Lee. Phytopathol. Mediterr. 35:144, 1996. (3) Y. Qi et al. Biotechnol. Bull. 4:44, 2004. (4) The IRPCM Phytoplasma/Spiroplasma Working Team-Phytoplasma Taxonomy Group. Int. J. Syst. Bacteriol. 54:1243, 2004.

The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.

This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835; the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.

The best hyperspectral estimation model of soil total nitrogen (TN) was established, which provided the basis for rapid and accurate estimation of soil total nitrogen content, scientific and rational fertilization and soil informatization management. A total of 92 brown soil samples were collected from the orchard of Qixia County, Yantai City, Shandong Province. After drying and grinding, the hyperspectrum of the soil was measured in the laboratory using ASD FieldSpec3. The TN contents of brown soil were measured by Kjeldahl method. The sensitive wavelengths were selected by multiple linear stepwise regression method. The hyperspectral estimation model of TN was established by Random Forest (RF) and Support Vector Machines (SVM). The models were validated by independent samples. The best estimation model was obtained. The sensitive wavelengths were 956 nm, 995 nm, 1020 nm, 1410 nm, 1659 nm and 2020 nm. The coefficients of determination (R2) of the two estimation models were 0.8011 and 0.8283, the root mean square errors (RMSE) were 0.022 and 0.025, and relative errors (RE) were 0.1422 and 0.1639, respectively. Random Forest model and Support Vector Machines model are feasible in estimating TN contents, but the Support Vector Machines model is better.

Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees’ precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R 551 +R 716 , R 551 *R 716 , FDR 530 +FDR 703 , FDR 530 *FDR 703 ; R 543 +R 705 , R 543 *R 705 , FDR 699 +FDR 756 , FDR 699 *FDR 756 and R 545 +R 702 , R 545 *R 702 , FDR 695 +FDR 746 , FDR 695 *FDR 746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R 2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746; the validation R2 was 0.720; and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.

The aims of this study are to explore the effect of different scales in the high spectral data on the estimation of chlorophyll content of apple leaves, to find out the optimal differential window scale and to establish a model for estimating the chlorophyll content of apple leaves. Taking the apple leaves as the research object, the actual spectral reflectance of the leaves was determined by the ASD Field Spec 3 spectrometer and the chlorophyll contents of the leaves were measured in the laboratory. Firstly, the differential transformations from 1 to 30 window scales were done for actual spectral data respectively, and correlation analyses were done between apple leaf chlorophyll content and differential data, then two sensitive wavelengths were chosen under each window. Secondly, taking five consecutive differential windows as a group, the best differential window was selected in each group. Lastly, after the conversion of two sensitive wavelengths in six differential windows, relationship analyses between chlorophyll content of apple leaves and two sensitive wavelengths were done, then two new parameters with the largest correlation coefficient were chosen to establish estimation model. Results showed that with increasing differential window, the determination coefficient (R2) of estimation model decreased after an initial increase, the tipping point was at the 13th differential window scale. Testing the partial least squares (PLS) model and the stepwise regression (SR) model established under differential window scale of the 13th, the R2 of the SR model was higher than that of the PLS model. The RMSE and RE% of the SR model were lower than that of the PLS model, which showed that SR model was more suitable to estimate chlorophyll content.

The influence of the equidistant sampling method was explored in a hyperspectral model for the accurate prediction of the water content of apple tree canopy. The relationship between spectral reflectance and water content was explored using the sample partition methods of equidistant sampling and random sampling, and a stepwise regression model of the apple canopy water content was established. The results showed that the random sampling model was Y = 0.4797 − 721787.3883 × Z3 − 766567.1103 × Z5 − 771392.9030 × Z6; the equidistant sampling model was Y = 0.4613 − 480610.4213 × Z2 − 552189.0450 × Z5 − 1006181.8358 × Z6. After verification, the equidistant sampling method was verified to offer a superior prediction ability. The calibration set coefficient of determination of 0.6599 and validation set coefficient of determination of 0.8221 were higher than that of the random sampling model by 9.20% and 10.90%, respectively. The root mean square error (RMSE) of 0.0365 and relative error (RE) of 0.0626 were lower than that of the random sampling model by 17.23% and 17.09%, respectively. Dividing the calibration set and validation set by the equidistant sampling method can improve the prediction accuracy of the hyperspectral model of apple canopy water content.

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

The complete genomic sequence of a Chinese Potato virus X isolate FX21 (PVX-FX21) was determined from three overlapping cDNA clones. The genome of PVX-FX21 is 6435 nucleotides in length excluding the poly(A) tail and contains five open reading frames (ORFs). Its entire genomic sequence shares 95.2-96.3% identities with Asian and European isolates, and 77.3-77.8% with American isolates. Phylogenetic analysis of the complete genomic sequence reveals two groups: the Eurasian group and the American group. PVX-FX21 belongs to the Eurasian group and forms a separate sub-branch with three Asian isolates. Similar analyses of the coat protein genes of 37 PVX isolates also reveal two major groups. All PVX isolates from Asia are clustered to group I, whereas isolates from Europe and America are clustered to both groups. Nucleotide sequence diversity analyses show that there is no geographical differentiation between PVX isolates and that constraint on the ORF encoding RNA-dependent RNA polymerase is much higher than those on the other four ORFs.

In 2005, rose plants (Rosa rugosa cv. ‘Plena’) exhibiting typical phytoplasma disease symptoms of stunting, yellowing, witches’-broom and dieback were observed in Pingyin, Shandong Province, China. The disease, rose witches’-broom (RoWB), is progressively destructive and can be graft-transmitted. Polymerase chain reaction (PCR), sequencing of PCR products and electron microscopy were used to investigate the possible association of phytoplasma with RoWB. All results indicated that presence of phytoplasma in the symptomatic rose plants. Sequence alignment of 16S rRNA gene, tuf gene and rp gene confirmed that the phytoplasma associated with RoWB is the causal agent of Paulownia witches’-broom disease, which might be transmitted from the paulownia tree that is several meters away. To our knowledge, this is the first report of the molecular characterization of phytoplasma infecting rose in China.

Tomato chlorosis virus (ToCV), a phloem-limited and whitefly-transmitted crinivirus (family Closteroviridae) has been reported recently in China (Zhao, et al., 2015). In the summers of 2014 and 2016, leaves of cowpea (Vigna unguiculata) plants exhibiting interveinal chlorotic yellowing, dark green veins and curling were observed in greenhouses that were growing cowpeas and tomatoes in Liaocheng prefecture, Shandong Province, where natural infection of ToCV in tomato is widespread (Zhao et al., 2015). Tomato plants (cv.Oudun) with interveinal chlorotic yellowing symptoms and heavy whitefly (Bemisia tabaci) infestations were observed in the sampling sites. The disease incidence was around 87% for tomato plants and 5% for cowpea plants. To identify the pathogen, total RNA was extracted from leaves of six symptomatic cowpea and eight symptomatic tomato plants, with asymptomatic plants being used as controls. RT-PCR were carried out using primers ToCV-CP-F/ToCV-CP-R (5'-CCTCAAAGAGCTAAACTGGAC-3'/5'- CACATCACCGG...

The new-shoot-growing stage is an important period of apple tree nutrition distribution. The objective of this study is to provide technical support for apple tree nutrition diagnosis by constructing quantitative evaluation models between the apple leaf nitrogen content during the new-shoot-growing stage and characteristic spectral parameters. The correlation coefficients between the original spectral data and the nitrogen content were calculated. Then, the sensitive bands of the nitrogen content were selected using the theory of two-dimensional (2D) correlation spectroscopy. Finally, partial least squares regression (PLSR) and support vector machine (SVM) evaluation models were established using 2 parameters: Rx (maximum spectral reflectivity in the waveband) and Sx (total spectral reflectivity in the waveband). The results showed that the sensitive bands in the 2D correlation synchronous and asynchronous spectrograms were 537-560 nm and 708-719 nm. The PLSR model can be used to estimate the nitrogen content. Compared with PLSR, SVM provided better modeling and testing results, with a larger coefficient of determination (R2) and a smaller root-mean-square error (RMSE). The SVM model based on Sx was a good backup method. The calibration R2 of the model was 0.821, its RMSE was 0.710 g·kg-1, the validation R2 was 0.768, and its RMSE was 1.019 g·kg-1. The SVM model based on 2D correlation spectroscopy can be used to quantitatively estimate the nitrogen content in apple leaves.