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1.
Ying Yong Sheng Tai Xue Bao ; 31(1): 35-44, 2020 Jan.
Artigo em Chinês | MEDLINE | ID: mdl-31957378

RESUMO

To verify the accuracy of MODIS-NDVI data products in deserts and provide guidance for scientific management of desert grasslands in the context of climate change, we examined the responses of fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) to hydrothermal gradient in arid desert areas using unmanned aerial vehicle (UAV) remote sensing. In Alxa desert region of Inner Mongolia, GreenSeeker handheld spectrometer was used to obtain NDVI (NDVIR) of 100 sampling points. NDVI was extracted by MODIS-NDVI data products (NDVIM), and the accuracy of NDVIM was verified by NDVIR. FVC of each sampling point was obtained through unmanned aerial vehicle remote sensing (FVCU), which was used to examine the FVC that was retrieved by the pixel binary model (FVCM). In addition, combining meteorological data, we examined the responses of FVC and NDVI to hydrothermal gradient based on UAV remote sensing method. The results showed that MODIS-NDVI data products reflected the real NDVI in Alxa area with an accuracy of 84.2%, but NDVIM were 15.7% higher than the actual values. FVCM reflected the vegetation coverage of Alxa region with an accuracy of 83.1%, which were 14.8% lower than the real value. Effects of meteorological factors on NDVI was different, depending on the different acquisition methods. NDVI was affected not only by temperature and precipitation, but also by ground temperature, evaporation and the interaction between evaporation and ground temperature. Because of the different degree of atmospheric influence, NDVIM was more affected by ground temperature, evaporation and precipitation than NDVIR, while NDVIR was more affected by temperature than NDVIM. To examine the changes of vegetation coverage across hydrothermal gradient in desert area, we should consider not only precipitation and temperature, but also the interaction between evaporation, ground temperature and meteorological factors. The interaction between temperature and rainfall, evaporation and ground temperature, and between temperature and evaporation had greater impacts on FVCU.


Assuntos
Conceitos Meteorológicos , Tecnologia de Sensoriamento Remoto , China , Mudança Climática , Temperatura Ambiente
2.
Ying Yong Sheng Tai Xue Bao ; 31(1): 219-229, 2020 Jan.
Artigo em Chinês | MEDLINE | ID: mdl-31957399

RESUMO

Using Landsat 5/TM and Landsat 8/OLI images in 2000 and 2017, based on remote sensing ecological index (RSEI) model, combined with meteorological observation data and socio-economic data in Nanjing from 2000 to 2017, we analyzed and evaluated the ecological environment changes and the characteristic ecological areas in Nanjing. The results showed that the average RSEI of Nanjing decreased from 0.626 to 0.618 during 2000-2017. The RSEI values could be divided into five grades: bad, poor, fair, good and excellent. The proportion of area above good grade decreased from 61.0% to 57.1%, while that below poor grade increased slightly. Compared with 2000, the proportion of areas with improved ecological environment quality was 34.5%, 34.7% area had deteriorated, and 30.8% area remained unchanged in 2017. Among them, the ecological quality of main urban area had significantly improved, and the area with improved ecological quality exceeded that of deterioration. The ecological quality of new urban area and suburbs had deteriorated. The area with poor ecological environment exceeded the area of improvement. Among the three ecological protection areas, the ecological quality of Zijin Mountain was significantly better than that of Laoshan Mountain and Jiangxinzhou. The urbanization rate was negatively correlated with RSEI, with a correlation coefficient of -0.91. The urbanization process would have negative impacts on the ecological environment in general. However, strict protection and management measures could maintain the good ecological environment even improve it.


Assuntos
Ecossistema , Urbanização , China , Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto
3.
Water Res ; 171: 115403, 2020 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-31901508

RESUMO

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R2 value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m3). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m3, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m3. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.


Assuntos
Clorofila A , Tecnologia de Sensoriamento Remoto , Teorema de Bayes , Canadá , Clorofila , Monitoramento Ambiental , Incerteza
4.
Integr Zool ; 15(1): 79-86, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31305022

RESUMO

Following significant developments in technology, alternative devices have been applied in fieldwork for animal and plant surveys. Thermal-image acquisition cameras installed on unmanned aerial vehicles (UAVs) have been used in animal surveys in the wilderness. This article demonstrates an example of how UAVs can be used in high mountainous regions, presenting a case study on the Sichuan snub-nosed monkey with a detection rate of 65.19% for positive individual identification. It also presents a model that can prospectively predict population size for a given animal species, which is based on combined initial work using UAVs and traditional surveys on the ground. A great potential advantage of UAVs is significantly shortening survey procedures, particularly for areas with high mountains and plateaus, such as the Himalayas, the Qinghai-Tibet Plateau, Hengduan Mountains, the Yunnan-Gui Plateau and Qinling Mountains in China, where carrying out a traditional survey is extremely difficult, so that species and population surveys, particularly for critically endangered animals, are largely absent. This lack of data has impacted the management of endangered animals as well as the formulation and amendment of conservation strategies.


Assuntos
Distribuição Animal , Colobinae/fisiologia , Tecnologia de Sensoriamento Remoto/métodos , Aeronaves , Animais , China , Conservação dos Recursos Naturais , Ecossistema , Densidade Demográfica , Tecnologia de Sensoriamento Remoto/instrumentação
5.
Sci Total Environ ; 698: 134074, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31505359

RESUMO

This study aims to investigate the combined use of two types of remote sensing data - ALS derived and digital aerial photogrammetry data (based on imagery collected by airborne UAV sensors) - along with intensive field measurements for extracting and predicting tree and stand parameters in even-aged mixed forests. The study is located in South West Romania and analyzes data collected from mixed-species plots. The main tree species within each plot are Norway spruce (Picea abies L. Karst.) and Beech (Fagus sylvatica L.). The ALS data were used to extract the digital terrain model (DTM), digital surface model (DSM) and normalized canopy height model (CHM). Object-Based Image Analysis (OBIA) classification was performed to automatically detect and separate the main tree species. A local filtering algorithm with a canopy-height based variable window size was applied to identify the position, height and crown diameter of the main tree species within each plot. The filter was separately applied for each of the plots and for the areas covered with Norway spruce and beech trees, respectively (i.e. as resulted from OBIA classification). The dbh was predicted based on ALS data by statistical Monte Carlo simulations and a linear regression model that relates field dbh for each tree species with their corresponding ALS-derived tree height and crown diameter. The overall RMSE for each of the tree species within all the plots was 5.8 cm for the Norway spruce trees, respectively 5.9 cm for the beech trees. The results indicate a higher individual tree detection rate and subsequently a more precise estimation of dendrometric parameters for Norway spruce compared to beech trees located in spruce-beech even-aged mixed stands. Further investigations are required, particularly in the case of choosing the best method for individual tree detection of beech trees located in temperate even-aged mixed stands.


Assuntos
Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto , Árvores , Fagus , Florestas , Lasers , Luz , Picea , Romênia
6.
Water Res ; 168: 115162, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31629230

RESUMO

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (SPOC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ13CPOC) values. The SPOC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the SPOC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.


Assuntos
Lagos , Tecnologia de Sensoriamento Remoto , Carbono , Ecossistema , Monitoramento Ambiental
7.
Sci Total Environ ; 701: 134769, 2020 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-31739237

RESUMO

Over the past decade, various aspects of China's fragile karst environments, including net primary productivity (NPP), have been changed or threatened by shale gas development. This industry is still developing, so it is important to understand what drives environmental changes, particularly in NPP, when shale gas pads are constructed in sensitive areas. Few previous studies have addressed this issue, so we quantified how the NPP changed, and what drove the changes, when a large shale gas area was developed at the end of 2012 in a mountainous karst area in Sichuan Province. We calculated the trend in the normalized difference vegetation index (NDVI) from 2012 to 2017 and used the Carnegie-Ames-Stanford Approach (CASA) model to calculate the changes in NPP at different distances from the pads using remote sensing images for July 2012 and July 2017 and field survey data from July 2017. We then identified the factors that drove the changes with Geodetector. The results showed that the NDVI increased across 64.2% of the shale gas development area from 2012 to 2017 because of climate change, and only showed a significant decrease across 0.3% of the area, mainly because of the shale gas development. The NPP decreased by 110.1 t because of the shale gas development in July 2017, or by about 0.35% of the total NPP. Of this, 93.8 t were associated with the pad construction areas, and 16.3 t were associated with the area around the pads. The changes in NPP around the shale gas pads were mainly confined to within 150 m during the construction phase and 90 m once the construction was completed. The NPP at different distances from the pads during the construction period was related to the distance from the pad, slope, and land use. Once completed, the NPP mainly varied with distance, land use, and the distance from the pad to rural settlements. The NPP was most strongly influenced by the distance from the pad and the area of the pad. We suggest that, when planning the construction of shale gas pads, the pads should be sited on gently sloping areas, the number of wells on each pad should be optimized, land use type changes outside the pad should be limited, and the land beyond the pads should be reclaimed in a timely manner to allow the NPP to recover.


Assuntos
Monitoramento Ambiental , Campos de Petróleo e Gás , Plantas , Tecnologia de Sensoriamento Remoto , China , Gás Natural
8.
Chemosphere ; 239: 124678, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31494323

RESUMO

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Tecnologia de Sensoriamento Remoto/métodos , Aerossóis/análise , China , Meteorologia , Modelos Estatísticos , Rios , Estações do Ano , Astronave
9.
Environ Monit Assess ; 192(1): 2, 2019 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-31792634

RESUMO

Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district.


Assuntos
Monitoramento Ambiental/métodos , Sistemas de Informação Geográfica , Tecnologia de Sensoriamento Remoto , Paquistão , Plantas , Estações do Ano , Temperatura Ambiente , Urbanização
10.
Ying Yong Sheng Tai Xue Bao ; 30(12): 4059-4070, 2019 Dec.
Artigo em Chinês | MEDLINE | ID: mdl-31840450

RESUMO

It's important to master tree species composition and distribution in forests for the study of forest ecosystems. To promote the application of domestic Gaofen data in the classification of tree species and to explore the effects of different combining images, classification features and classifier on tree species classification results, three kinds of single temporal data and four kinds of multi-temporal data were constructed. Based on three GF-2 images, according to the multi-scale segmentation, C5.0 feature optimization as well as two classifiers including support vector machine (SVM) and random forest (RF), we finished the object-based classification of eight tree species of different temporal and feature dimensions respectively, and finally achieved good results with overall accuracy between 63.5% and 83.5% and the Kappa coefficient between 0.57 and 0.81. The results showed that the choice of temporal stage would affect the classification results. The results based on multi-temporal were generally better than that on single temporal stage. There were obvious precision differences between different image combinations of multi-temporal as well as different single temporal stage. It is notable that feature optimization played a positive role in the improvement of classification accuracy. SVM was relatively stable across different temporal and feature dimensions, which should be given priority when single temporal and classification features are difficult to distinguish tree species directly, while it should be noted that SVM was easy to overfit. RF was not easy to overfit, but it was more dependent on the quality of classification features and would get good results under favorable image combination.


Assuntos
Tecnologia de Sensoriamento Remoto , Árvores , Ecossistema , Máquina de Vetores de Suporte
11.
Environ Monit Assess ; 192(1): 15, 2019 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-31811511

RESUMO

Land-use/land-cover change is the most vulnerable factor in any developing urban environment. Increased infrastructure and population density tend to alter the land features which in turn will have an impact on climate change and will increase the impervious layer. Study of trends in land-use/land-cover change is required for analyzing the possible ways of managing the natural system. In this study, the spatial and temporal changes of the urban and peri-urban landscape of the Chennai Metropolitan Area (CMA), Tamil Nadu, India, were analyzed using satellite images. Imageries from Landsat 5 (TM) and Landsat 8 (OLI/TIRS) sensors were taken for the years 1988, 1997, 2006, and 2017. Ensembles of remote sensing spectral indices (NDVI, MNDWI, NDBI, and NDBaI) were calculated for the land-use/land-cover classification. The confusion matrix was used for assessing the accuracy for the year 2017. The overall accuracy of the LULC classification obtained was 91.76% with the kappa coefficient of 0.84. The results show that during the period of February 1988 to February 2017, the agriculture/fallow land, barren/semi-barren, vegetation, and water bodies/wetlands have decreased by 53.62%, 1.45%, 58.99%, and 30.59%, respectively. This decrease has contributed to an increase of 173.83% in built-up area. About 26,881 ha of agriculture/fallow land, 10,482 ha of vegetation land, and 2454 ha of water bodies/wetlands were converted to built-up and other land-use over the period. This essentially meant that CMA has changed from predominantly an agricultural area (42.21%) in 1988 to built-up area (48.72%) in 2017.


Assuntos
Monitoramento Ambiental/métodos , Tecnologia de Sensoriamento Remoto , Urbanização , Agricultura , Monitoramento Ambiental/instrumentação , Índia , Imagens de Satélites , Áreas Alagadas
12.
Biosci. j. (Online) ; 35(6): 1847-1854, nov./dec. 2019. ilus, tab
Artigo em Inglês | LILACS | ID: biblio-1049144

RESUMO

Crop harvest scheduling and profits and losses predications require strategies that estimate crop yield. This work aimed to investigate the contribution of phenological variables using path analysis and remote sensing techniques on cotton boll yield and to generate a model using decision trees that help predict cotton boll yield. The sampling field was installed in Chapadão do Céu, in an area of 90 ha. The following phenological variables were evaluated at 30 sample points: plant height at 26, 39, 51, 68, 82, 107, 128, and 185 days after emergence (DAE); number of floral buds at 68, 81, 107, 128, and 185 DAE; number of bolls at 185 DAE; Rededge vegetation index at 23, 35, 53, 91, and 168 DAE; and cotton boll yield. The main variables that can be used to predict cotton boll yield are the number of floral buds (at 107 days after emergence) and the Rededge vegetation index (at 53 and 91 days after emergence). To obtain higher cotton boll yields, the Rededge vegetation index must be greater than 39 at 53 days after emergence, and the plant must present at least 14 floral buds at 107 days after emergence.


O escalonamento de colheitas e a previsão de ganhos e perdas requerem estratégias que estimam a produtividade das culturas. Este trabalho teve como objetivo investigar a contribuição de variáveis fenológicas utilizando técnicas de análise de trilha e sensoriamento remoto sobre a produtividade de algodão em caroço e gerar um modelo utilizando árvores de decisão que ajudam a prever esta variável. O campo de amostragem foi instalado em Chapadão do Céu, em uma área de 90 ha. As seguintes variáveis fenológicas foram avaliadas em 30 pontos amostrais: altura das plantas aos 26, 39, 51, 68, 82, 107, 128 e 185 dias após a emergência (DAE); número de gemas florais aos 68, 81, 107, 128 e 185 DAE; número de cápsulas a 185 DAE; Índice de vegetação Rededge em 23, 35, 53, 91 e 168 DAE; e produção de algodão em caroço. As principais variáveis que podem ser utilizadas para prever a produção de caroço de algodão são o número de gemas florais (aos 107 dias após a emergência) e o índice de vegetação de Rededge (aos 53 e 91 dias após a emergência). Para obter maiores produtividades de algodão, o índice de vegetação de Rededge deve ser superior a 39 aos 53 dias após a emergência e a planta deve apresentar pelo menos 14 gemas florais aos 107 dias após a emergência.


Assuntos
Sementes , Gossypium , Tecnologia de Sensoriamento Remoto , Pradaria
13.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4078-4081, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872679

RESUMO

In order to solve the problem of manual area measurement,the traditional methods of medicinal planting area statistics are difficult to meet the needs of rapid area survey application. This paper uses the UAV remote sensing method with the advantages of unmanned,automatic,high efficiency,high score and short production cycle to monitor the shape of Callicarpa nudiflora. A solution for aerial photography,image data acquisition and data processing of drones were designed for characteristics and planting conditions. After data processing and statistical analysis,detailed information on the location and area of the C. nudiflora in the target area was obtained. Then the accuracy comparison analysis was carried out with the measured results of the C. nudiflora. The results show that the UAV is feasible for the monitoring of C. nudiflora,and has a good application prospect in the monitoring of Chinese herbal medicine planting.


Assuntos
Callicarpa , Plantas Medicinais , Tecnologia de Sensoriamento Remoto , Fotografação
14.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4090-4094, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872681

RESUMO

The dried roots of Panax ginseng are used as medicines. In this paper,multi-time satellite sensing image data are used for image registration by radiometric correction,atmospheric pressure correction,the data of different years were compared. The multiscale segmentation of the sensing image was successively carried out by using object-oriented method. Combining with the characteristics of the sensing image participated in the field survey,the objective was to understand the speckles of the environmental parameters distribution map of Changbai county in 2017 and 2018. The parameter area of Changbai county was calculated by using GIS spatial analysis tools. The union,erase and intersect tools of " analysis to OLS" overlay in " Arc Toolbox" were used to analyze the parametric area of Changbai county from 2017 to 2018. The results showed that the parameter area of Changbai county in 2017 was 27 400 mu( 1 mu≈667 m2),and the parameter area in 2018 was 13 900 mu. The parameter area of the new park in Changbai County in 2018 was 12 500 mu,and the harvested area in 2017 was 27 000 mu. Through the analysis and study of the regional change of the park participating in the training area,it has significance for guiding the park participating in the actual production planning and layout in Changbai county in the next step.


Assuntos
Panax , Tecnologia de Sensoriamento Remoto , Jardins
15.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4095-4100, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872682

RESUMO

The study is aimed to effectively obtain the planting area of traditional Chinese medicine resources. The herbs used as the material for traditional Chinese medicine are mostly planted in natural environment suitable mountainous areas. The UAV low altitude remote sensing data were used as the samples and the GF-2 remote sensing images were applied for the data source to extract the planting area of Salvia miltiorrhiza and Artemisia argyi in Luoning county combined with field investigation. Remote sensing satellite data of standard processing obtain specific remote sensing data coverage. The UAV data were pre-processed to visually interpret the species and distribution of traditional Chinese medicine resources in the sample quadrat. Support vector machine( SVM) was used to classify and estimate the area of traditional Chinese medicine resources in Luoning county,confusion matrix was used to determine the accuracy of spatial distribution of traditional Chinese medicine resources. The result showed that the application of UAV of low altitude remote sensing technology and remote sensing image of satellite in the extraction of S. miltiorrhiza and other varieties planting area was feasible,it also provides a scientific reference for poverty alleviation policies of the traditional Chinese medicine Industry in local areas.Meanwhile,research on remote sensing classification of Chinese medicinal materials based on multi-source and multi-phase high-resolution remote sensing images is actively carried out to explore more effective methods for information extraction of Chinese medicinal materials.


Assuntos
Altitude , Medicamentos de Ervas Chinesas , Medicina Tradicional Chinesa , Tecnologia de Sensoriamento Remoto , Recursos Naturais , Máquina de Vetores de Suporte
16.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4101-4106, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872683

RESUMO

In order to comprehensively monitor the dynamic change of Paeonia lactiflora planting area,the investigation of P. lactiflora planting area in Dangshan was carried out. It can provide reference for the planting detection of P. lactiflora in Huaibei Plain.Based on remote sensing technology,this paper extracts the planting area of P. lactiflora in Dangshan in 2018 by using the minimum distance method,maximum likelihood method,parallel hexahedron method and Mahalanobis distance method,using the remote sensing image of ZY-3 Satellite as the data source,and makes a comparative analysis with the results. The results show that the maximum likelihood method is better than the other three methods. This method can provide reference for remote sensing monitoring of P. lactiflora planting area in China.


Assuntos
Paeonia , Tecnologia de Sensoriamento Remoto , China
17.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4107-4110, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872684

RESUMO

Moutan Cortex is one kind of famous medicinal materials. The dry root bark of Paeonia ostii which is a genuine medicinal material produced in Tongling,Anhui province,and later was introduced to Heze,Shandong province and Bozhou,Anhui province.Dangshan county is located at the northern end of Anhui province and adjacent to Shandong province. Its medicinal seedlings were came from Heze,Shandong province. At present,there is a lack of scientific investigation on the planting area of P. ostii in north China plain. On the basis of field investigation and remote sensing technology,through the data source provided by the remote sensing image of " Resources 3"( ZY-3),combined with the biological characteristics of P. ostii,the planting area of P. ostii in Dangshan county was extracted by field investigation and supervisory classification. The supervise classification method with the highest interpretation accuracy so far,the overall accuracy was 97. 81%,Kappa coefficient 0. 96. The results showed that the remote sensing classification method based on the maximum likelihood classification could extract P. ostii plots in the study area effectively. This study provides a scientific basis for the protection and rational utilization of traditional Chinese medicine resources,the development policy of traditional Chinese medicine industry and the long-term development plan in Dangshan county,and provides technical support for the poverty alleviation of traditional Chinese medicine industry in Dangshan county. It provides scientific reference for the application of remote sensing technology to investigate the planting area of P. ostii in in north China plain.


Assuntos
Medicina Tradicional Chinesa , Paeonia , Tecnologia de Sensoriamento Remoto , China
18.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4121-4124, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872687

RESUMO

Due to the large amount of Codonopsis pilosula planted in Weiyuan county,and the arable land area,the local medicinal materials office uses a large amount of manpower,financial resources and material resources to estimate its area every year. In order to extract the information of local Chinese medicinal materials more quickly and simply,we try to apply remote sensing technology to the extraction of Chinese medicinal materials. This paper will use Weiyuan county of Gansu province as the research area,and use the domestic ZY-3 Satellite multi-spectral remote sensing image as the data source to find out the spectral characteristics of the party's participation in other remote sensing images. The visual interpretation method was used to extract the planting area of the C. pilosula in Weiyuan county. The estimated value of the planting area of C. pilosula using satellite remote sensing technology was 75 965 mu( 1 mu≈667 m2),which was basically consistent with the field survey data of the local medicinal materials office. After the accuracy verification,it was found that the precision of C. pilosula planted by visual interpretation was more than 70%. It is concluded that the satellite remote sensing technology can be used to extract the information of C. pilosula and it can provide the relevant information of the planting area of Chinese medicinal materials quickly and accurately.


Assuntos
Codonopsis , Plantas Medicinais , Tecnologia de Sensoriamento Remoto , China
19.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4125-4128, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872688

RESUMO

Due to the large amount of nutrients required during the cultivation of Angelica sinensis and in order to prevent the occurrence of pests and diseases,and the annual reduction of the planting area of Angelica and the balance of supply and demand of A. sinensis,the A. sinensis plantation adopts the rotation mode. This paper takes Wuyuan county of Gansu province as the research scope and use GF-1 Satellite data as the data source,using remote sensing technology combined with field survey results,to explore the effective method of visual interpretation for the extraction of A. sinensis planting area. A sample was selected to generate a spectrum according to different feature types. The different characteristics of A. sinensis and other features were analyzed and distinguished in remote sensing images,so that the A. sinensis planting plots were extracted and verified in remote sensing images. The results showed that the accuracy verification value of the visual interpretation method was 95. 85%. It is determined that the visual interpretation method can effectively extract the A. sinensis planting plots within the research scope and realize the comprehensive grasp of the spatial distribution information of A. sinensis.


Assuntos
Angelica sinensis , Plantas Medicinais , Tecnologia de Sensoriamento Remoto , China
20.
Zhongguo Zhong Yao Za Zhi ; 44(19): 4129-4133, 2019 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-31872689

RESUMO

Traditional Chinese medicine is planted in mountainous areas with suitable natural conditions. The planting area is complex in terrain,and the planting plots are mostly irregularly shaped. It is difficult to accurately calculate the planting area by traditional survey methods. The method of extracting Chinese herbal medicine planting area combined with remote sensing and GIS technology is of great significance for the rational development and utilization of traditional Chinese medicine resources. Taking Bletilla striata planting in Ningshan county of Shaanxi province as an example,the extraction method of planting area of traditional Chinese medicine in county was studied. High-resolution ZY-3 and GF-1 multi-spectral multi-temporal remote sensing images were used as data sources. Through field sampling,samples such as B. striata,cultivated land,forest land,water body,artificial surface,alpine meadow,etc. are collected. The spectral features,texture features and shape features of remotely identifiable objects in different planting areas and cultivated land,vegetable sheds were analyzed,confusing ground objects were eliminated and interpretation marks were establish. The method of visual interpretation is used to realize the extraction of B. striata planting areas,and the B. striata planting area are calculated by combining GIS technology. The results showed that the method of visual interpretation,using high-resolution ZY-3 and GF-1 multi-spectral multi-temporal remote sensing image data extracted the planting area of 403.05 mu. It can effectively extract the B. striata planting area in research region.


Assuntos
Medicina Tradicional Chinesa , Orchidaceae , Tecnologia de Sensoriamento Remoto , Florestas
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