Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery

Accurate extraction of urban impervious surface data from high-resolution imagery remains a challenging task because of the spectral heterogeneity of complex urban land-cover types. Since the high-resolution imagery simultaneously provides plentiful spectral and spatial features, the accurate extraction of impervious surfaces depends on effective extraction and integration of spectral–spatial multifeatures. Different features have different importance for determining a certain class; traditional multifeature fusion methods that treat all features equally during classification cannot utilize the joint effect of multifeatures fully. A fusion method of distance metric learning (DML) and support vector machines is proposed to find the impervious and pervious subclasses from Chinese ZiYuan-3 (ZY-3) imagery. In the procedure of finding appropriate spectral and spatial feature combinations with DML, optimized distance metric was obtained adaptively by learning from the similarity side-information generated from labeled samples. Compared with the traditional vector stacking method that used each feature equally for multifeatures fusion, the approach achieves an overall accuracy of 91.6% (4.1% higher than the prior one) for a suburban dataset, and an accuracy of 92.7% (3.4% higher) for a downtown dataset, indicating the effectiveness of the method for accurately extracting urban impervious surface data from ZY-3 imagery.

Abstract. Delineation of the glacier is an important task for understanding response of glaciers to climate. In Himalayan region, most of the glaciers are covered with debris. Supraglacial debris works as an obstacle for automatic mapping of glacier using remote sensing data. Different methods have been used to reduce this difficulty based on pixel-based and object-based approaches using optical data, thermal data and DEM. Pixel-based glacier mapping is a traditional method for delineation of the glacier but the object-based method has emerged as a new approach in cryosphere application leading to its successful application in different applications. All pixel-based methods require some degree of manual correction because these can’t be delineated automatically, especially in shadow area and debris covered part of the glacier. In the majority of studies, the object-based method has provided higher accuracy to delineate the debris-covered glacier. Spatially high spatial resolution satellite data is best suited for object-based image classification. In future, a combination of pixel-based method and object-based method can be attempted for delineation of the debris-covered glacier along with its critical analysis for suitability. The present paper critically reviews pixel-based and object-based methods as well as provides a framework for combined pixel and object-based method for delineation of debris-covered glacier.

Panax notoginseng is grown mainly in Yunnan Province. Under the present high-density planting patterns for the plant, to solve the problems of a high rate of seed damage and the inability to use a traditional single air-blowing metering device, this paper designs a six-row air-blowing centralized precision seed-metering device for P. notoginseng to realize mechanized precision seeding of this species. This paper describes the working principle of the seed-metering device, and the main structural parameters are determined by combining theoretical calculations with simulation analysis. A mechanics model of the seed filling, cleaning and pressing processes of the seed-metering device was constructed. The seeds of P. notoginseng in Yunnan Province were selected as experimental subjects. An experimental study on the seed-metering performance of the seed-metering device was carried out using the quadratic rotation orthogonal combination test method. The outlet pressure of the air nozzle, forward velocity and cone angle of the hole were selected as test factors. Mathematical models of the grain spacing qualified index, miss index, multiple index and the coefficient of variation of the row displacement consistency were established to analyze the order of factors affecting indicators. Through parameter optimization, the optimum combination of parameters was determined as follows: the cone angle of the hole is 50°, the forward velocity is less than 0.73 m/s, and the outlet pressure of the air nozzle is 0.32-0.52 kPa. The qualified index of grain spacing is higher than 94%, the miss index is less than 3%, the multiple index is less than 5%, and the coefficient of variation of the row displacement consistency is less than 5%. The test results are essentially consistent with the optimization results. The metering device meets the requirements of precision seeding of P. notoginseng. This study provides a basis for the design of a six-row air-blowing centralized precision seed-metering device for P. notoginseng. Keywords: Panax notoginseng, air blowing, seed-metering device, optimization design, air distribution mechanism DOI: 10.25165/j.ijabe.20201302.5161 Citation: Lai Q H, Sun K, Yu Q X, Qin W. Design and experiment of a six-row air-blowing centralized precision seed-metering device for Panax notoginseng. Int J Agric & Biol Eng, 2020; 13(2): 111–122.

Nowadays, urban land use mapping has played a significant role in commerce, urban planning, tourism, as well as in environmental management. With the development of remote sensing technology, high spatial resolution satellite imagery has the capability to enable accurate classification of general urban land cover and land use types. In this study, we compared pixel-based and object-based methods for detailed land use classification, specifically, classification of commercial buildings, residential buildings and roads based on WorldView-2 imagery using support vector machine (SVM) approach. In this study we tested two classification scenarios: (1) object-based classification based on spectral, texture and geometric features in pan-sharpened multispectral image; (2) pixel-based classification based on texture features only in panchromatic image. Each scenario was trained and tested using two sets of stratified random sample (SRS) points: (1) training samples and test samples can be located within the same reference objects (SRS_1) and (2) test samples in separate reference objects from training points (SRS_2). Our results show that object-based method obtained higher overall accuracy than pixel-based method with SRS_1, but lower accuracy with SRS_2. Considering the exaggeration of accuracy with SRS_1 sample points, we concluded that for detailed land-use classification in our study, pixel-based method is advantageous over object-based method because of its higher accuracy (SRS_2) and that only panchromatic image is needed in this method.

The Ziyuan-3 (ZY-3) satellite was designed to satisfy 1:50000 scale mapping requirements. This study uses 556 images obtained by ZY-3, from June 1 to October 31, 2013, covering an area of 3 500 000 km 2 in midwestern China. A total of 900 check points measured by a global positioning system were also used to conduct the planar accuracy verification. The experimental results show that the ZY-3 nadir sensor calibration images achieved a planar root mean square error (RMSE) of 10.8 m without the use of ground control points (GCPs). In addition, the verification of vertical accuracy employed 12 ZY-3 stereo image pairs distributed over an area of 14 000 km 2 around Taiyuan in the Shanxi Province of China, and a Digital Elevation Model with 0.5-m vertical accuracy was used for reference and validation. The vertical accuracy of forward interaction and stereo-extracted Digital Surface Model (DSM) from the stereo images were both validated without GCPs. The experimental results demonstrate that the overall vertical RMSE of the forward intersection was 6.58 m; it was 5.21 and 7.07 m for flat and mountainous terrain, respectively. Moreover, the overall vertical RMSE of DSM was 5.56 m; it was 4.37 and 5.69 m for flat and mountainous terrain, respectively. It can be seen from the experimental results of planar and vertical accuracy verification that ZY-3 imagery is able to satisfy the requirements of 1:50000 topographic mapping in China without using GCPs.

Determination of arsenic speciation and the possible source of methylated arsenic in Panax Notoginseng

Water management, planting slope indicators, and economic benefit analysis for Panax notoginseng production decision under shaded and rain-shelter cultivation: A three-year sloping fields experiment

Chinese herbal medicine has played an important role in the treatment of the novel coronavirus patients. Machine learning-based remote-sensing techniques play a significant role in the quantitative resource inventory of materia medica resources, particularly to explore the monitoring abilities for sustainable utilization and biodiversity protection of the cultivated medicinal plant Panax notoginseng in the macrocosm. Until now, to the best knowledge, concrete planting patterns of Panax notoginseng are still poorly known. In this study, two popular supervised classifiers, i.e., support vector machine and artificial neural network, are employed to conduct mapping Panax notoginseng fields in Wenshan city, Yunnan province, China. It only targets a single class of interest, and there are 8,072 and 8,749 polygons extracted, whilst the planting areas of Panax notoginseng are estimated as 35.45 and 32.47 square kilometers achieved by SVM and ANN, respectively. Meanwhile, special concerns raised in terms of remote sensing mapping for modeling planting patterns and monitoring plant rotations of perennial Panax notoginseng cultivated under shade-net structures based on the experiments.

In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT), support vector machine (SVM), and random forest (RF)—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.

Panax notoginseng (Burkill) F.H.Chen (Sanqi) is a traditional Chinese medicinal plants, and has shown hemostasis, anti-oxidation, neuroprotection and anti-tumor activities (Wang et al. 2014). Wenshan (23°35'24.1" N, 104°19'48.2" E) in Yunnan Province of China, is the main producing area of P. notoginseng. This region has a subtropical monsoon climate, an altitude of around 1200m above sea level, and loam soil. In November 2023, serious symptoms of root rot disease were first observed on 2-year-old P. notoginseng in Wenshan. The P. notoginseng initially exhibits indications of stem browning and leaf yellowing, followed by plant wilting and damping-off, and eventual death. The incidence of diseased plants was up to 20% in a plantation of 4 hectares. Six diseased plants were randomly collected from different regions of a 1000 square meters planting area, and root samples were cut into small fragments and surface sterilized with 1% sodium hypochlorite for 1 min. The root fragments were placed on potato dextrose agar (PDA) and then incubated at 30℃ in the dark. Mycelium tips were observed and transferred onto new plates and finally 18 isolates were obtained from the diseased samples. The colonies of all 18 isolates were white with strong coconut odour, and looked identical in appearance. The growth rate of the isolates was about 2.5cm/d and the whole petri dish (8 cm in diameter) could be covered in 4 days. Conidia formed after another two-day incubation with light. Conidia were smooth, elliptical, 4.1 to 5.2 × 2.9 to 3.6 μm in size (n = 30). DNA was extracted from three representative isolates. The partial region of the rDNA internal transcribed spacer (ITS) and the translation elongation factor (tef1) were amplified with the primers ITS1/ITS4, TEF1/EF728 (Samuels et al. 2006). The blast results showed that the three isolates were identical to each other in their ITS (PP469588) and tef1 (PP544203) sequences and those sequences had 99% (ITS, 580/583bp) and 98% (tef1, 597/609 bp) similarity with those of Trichoderma gamsii (DQ841730 and DQ841722, respectively) (Jaklitsch et al. 2006). For pathogenicity tests, thirty 5-month-old healthy seedlings of P. notoginseng were used and inoculated by root-irrigation of conidial suspension (107 conidia/mL, 20mL per seedling), whereas ten seedlings were treated with distilled water as controls. All seedlings were incubated in pots and set in the test field in Wenshan area from April to June. First symptoms appeared 6 days after inoculation. All inoculated seedlings showed disease symptoms 18 days later after challenge, whereas no symptoms were observed on the controls. The pathogen was reisolated and confirmed to be T. gamsii through molecular identification. The pathogenicity test was repeated three times. T. gamsii was first reported in 2006 (Jaklitsch et al. 2006), and attracted attention mainly on its biocontrol effects (Matarese et al. 2012). It had also been isolated from healthy P. notoginseng as endophyte to produce many different secondary metabolites for potential medical use (Ding et al. 2012). To our knowledge, this is the first report of T. gamsii causing root rot disease on P. notoginseng. P. notoginseng is of great economic value in Wenshan area. In 2017, its production was valued at 16.2 billion Chinese Yuan, with an expanding cultivation (Liu et al. 2020). Thus, it is important to monitor and manage this pathogen on P. notoginseng.

At the beginning of 2012, a new optical satellite, called ZiYuan-3 (ZY-3), was launched from the Taiyuan Satellite Launch Centre. This article is aimed at the analysis and the assessment of the digital surface models (DSMs) and orthophotos from a ZY-3 triplet acquired over the city of Bolzano and the surrounding areas. The imagery processing chain was executed with four different software packages. In all cases the Rational Polynomial Functions (RPFs) orientation model was used, with rational polynomial coefficients (RPCs) supplied by the vendors. The accuracies of the extracted DSMs were compared with the public light detection and ranging (lidar) DSM. The DSM extracted with each software package was used to generate the corresponding orthophoto. The orthophoto accuracy was assessed against 1:5000/1:10000 maps. The results obtained show that ZY-3 imagery allow us to generate DSMs with an accuracy, in terms of root mean square error (RMSE), which can reach 1.5–4.2 m in the flat area, 3.9–6.9 in the urban area, and 5.7–8.2 m in the mountain area. These results are better than Shuttle Radar Topography Mission (SRTM). Regarding orthophoto generation, all software packages supply similar results with a significant bias of about 20 m, mainly caused by vendors’ RPC inaccuracy.

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Panax notoginseng is a well-known Chinese herb that is used worldwide. Studies on the factors affecting the quality of Panax notoginseng have mainly focused on the physical and chemical properties of the soil, with little reference to the differences in the microbial structure of different production areas. The goal of this work was to explore the diversity and structure of rhizosphere microbial communities of Panax notoginseng. To do this, Panax notoginseng rhizosphere soil samples were collected from ten production areas in China, and the 16SrRNA and internal transcribed spacer (ITS1) sequences were analyzed by Illumina high-throughput sequencing technology. The results revealed similar species composition of fungal and bacterial communities in the different producing areas, but significant variation in the abundances of some dominant flora. Redundancy analysis showed that environmental factors explained 41.3% of the fungal community and 45.7% of the bacterial community. Among all samples, the beneficial fungus Chaetomium was the most abundant with an average abundance of 19.65%. We detected significant enrichment of some root rot pathogens, including Ilyonectria, Fusarium, and Pseudomonas, in samples from Wenshan City and Yunnan Province. In summary, the results reveal differences in the structure of rhizosphere soil microbial community of Panax notoginseng in different production areas. The results of this study show that there are differences in the structure of rhizosphere soil microbial community of Panax notoginseng in different producing areas and some rhizosphere microbes may be related to rhizosphere rot. The results of this work should be beneficial to the agricultural development of Panax notoginseng and can provide a theoretical basis for the control of diseases and pests during cultivation of this important plant.

Panax notoginseng is second only to ginseng Panax notoginseng in the Chinese traditional medicine industry. It has extremely high economic value and medicinal value. However, due to the unsustainable planting characteristics of P. notoginseng. The original planting area of P. notoginseng has been exhausted, so the planting area of P. notoginseng has gradually expanded to other areas, and the planting area of P. notoginseng is also changing. Based on Sentinel-2 and Landsat 8 OLI remote sensing image data from 2018 to 2021, the study used Google Earth engine and Random Forest classification to analyze the spatial distribution information of P. notoginseng planting areas in Kaiyuan City, Honghe Prefecture. The results show that, based on Sentinel-2 data, Landsat 8 OLI data, and other multisource data, comprehensively considering the image spectral characteristics and habitat characteristics of P. notoginseng, and applying the Random Forest classification method. The spatial distribution information of P. notoginseng planting areas can be quickly extracted. After calculation, the transfer matrix indicates that the accuracy of the results is high and can meet the requirements of research and application. According to the analysis of the results, the cultivated area of P. notoginseng showed no significant change in 2018 and 2019. It shows an increasing trend during this period. In 2020, the cultivated area of P. notoginseng decreased slightly. While in 2021, the cultivated area of P. notoginseng increased sharply, and the cultivated area of P. notoginseng was more than twice that of the previous year. However, the price of P. notoginseng has shown a trend of decline year by year. By monitoring the planting area of P. notoginseng and analyzing the relationship between the planting area and the price of P. notoginseng, we can predict the future market price of P. notoginseng and plan the rational planting of P. notoginseng.

Remote sensing technology presents unique possibilities for monitoring agricultural systems, providing accurate information like crop type distribution, crop planting area, crop rotation, etc. Extracted from remote sensing imagery, previous efforts generally produce crop information based on pixel-based classification strategy without considering spatial context of objects. Further incorporation of object-based image analysis in crop type mapping could improve mapping accuracy and reduce disturbance caused by uncertainties caused by pixel-based methods. Here we aim to combine the advantages of pixel-based and object-based approaches for further improving crop type maps over Northeast China based on Sentinel-2 imagery, simple non-iterative clustering (SNIC), random forest classifier and Google Earth Engine platform. The results showed in the majority of cropland, object-based mapping results had higher accuracies and reduced obvious errors at parcel level. Overall accuracies improved by 0.5% and the Kappa coefficient improved by 9% in Sanjiang Plain. However, soybean and maize intercropping with small parcels could be ignored in object-based methods when clustering objects. Therefore, an integration of pixel and object-based approaches was adopted considering different landscapes and patch areas to generate an unprecedentedly accurate crop type map in Northeast China.