scholarly journals Application of Innovative Aerospace Technologies for Pastoral Farming of Sheeps

2019 ◽  
Vol 9 (1) ◽  
pp. 4584-4587
Keyword(s):  
Nutritional Value ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Age Groups ◽  
Complex Objects ◽  
Prognostic Assessment ◽  
Remote Assessment ◽  
Uav Images ◽  
The Many ◽  
Comparison Of The Results

Development of an innovative system of livestock husbandry based on the use of digital aerospace technologies and telemetry is a new modern direction in the development of the livestock industry, designed to solve the many problems of restoring fertility of soil and pasture for animals. To develop a methodology for remote assessment of pasture fertility, we used the technological capabilities of using unmanned aerial vehicles and satellite service, which allows us to study the dynamics of the NDVI (Normalized Difference Vegetation Index) of various pasture plots in the Stavropol Territory of the Russian Federation. The analysis of literary sources shows that the main problems associated with the complex automation of forecasting processes for complex objects are not technical, but methodological in nature and are caused by the lack of a theoretical base that should form the basis for creating the corresponding model and algorithmic support. A comparison of the results of a prognostic assessment of the nutritional value of fodder plants obtained from unmanned aerial vehicle (UAV) images, space services with actual nutritional values obtained from studies of the zoochemical composition of feeds showed a high degree of correlation. The UAV capabilities were recognized as promising for assessing the ethological characteristics of the Manych merino sheep, which allows to optimize the acquisition of groups of animals consolidated by forage activity. The article considers the issues of assessing the nutritional value of pasture feed and the vegetation index when raising sheep of the Jalgin merino breed in the conditions of the steppe regions of the Stavropol Territory. The introduction of remote assessment methods in pastoral livestock can optimize the cultivation of various sex and age groups of sheep and reduce the time to achieve production parameters by 5-8%.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1025 ◽  
Author(s):  
Jung-il Shin ◽  
Won-woo Seo ◽  
Taejung Kim ◽  
Joowon Park ◽  
Choong-shik Woo
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Method ◽  
Burn Severity ◽  
Spectral Angle Mapper ◽  
Before And After ◽  
Uav Images ◽  
Uav Image

Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future.

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

scholarly journals Geospatial Effect on the Mining Operation in Joda/Barbil Area of Odisha, India

2021 ◽  
pp. 36-55
Author(s):  
Siba Prasad Mishra ◽  
Kamal Kumar Barik ◽  
Smruti Ranjan Panda
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Surface Air Temperature ◽  
Mining Operation ◽  
Landsat 8 ◽  
Mining Areas ◽  
Illegal Mining ◽  
Comparison Of The Results

The study aims to investigate the Geospatial effect on the extraction operation in Joda and Barbil mining areas of Keonjhar district, Odisha, India. Present work involves the topography, soil, climate, and stratigraphy investigation of the area. The acquisition of Landsat 8 TIRS (Thermal Infrared), Landsat 5 TM (Thematic Mapper), and CARTOSAT DEM data of temporal and spatial satellite images from various websites. ARC GIS and ERDAS IMAGINE 9.2 software used to find the land use and land cover images (accuracy average 90%). Normalized Difference Vegetation Index (NDVI), and Surface air Temperature (SAT) of Barbil area for 2003, 2007, 2017 and 2018 have been estimated. Comparison of the results have shown that, there is increase in built up, and mining areas whereas the agricultural land and vegetation cover are down scaled. There is constant average SAT rise of 1-2°C in all the land cover classification between 2007 and 2018. The NDVI values show conversion of sparse from dense vegetation in the area. Poor operational strategies in mines operation, like corruption, illegal mining, lack of accountability, overburden wastes/ trailing disposal, ecologic degradation, waterlogging in mine pits, and human rights violations are the root causes of environmental deterioration of the study area. It is pertinent to implement strictly, the Mines and Minerals (Development and Regulation) Amendment Act, India, 2021, regular GIS application to assess the mines volume of extraction, strict vigilance and fixation of accountability for losses of existing mines values, and afforestation/ reforestation of degraded/lost forests in Barbil area.

scholarly journals Comparação de imagens multiespectrais utilizando Satélites e VANT para a análise de mudanças estruturais em área de Floresta Seca

2021 ◽  
pp. 2510-2522
Author(s):  
Cloves Santos ◽  
Magna Moura ◽  
Josicleda Galvincio ◽  
Herica Carvalho ◽  
Rodrigo Miranda ◽  
...  
Keyword(s):  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Structural Characteristics ◽  
Landsat 8 ◽  
Green Vegetation ◽  
Detailed Assessment ◽  
Low Altitude ◽  
Uav Images ◽  
Sentinel 2

Remote sensing is a very important tool in the acquisition of information that allows the monitoring of structural characteristics and changes in vegetation in biomes, and with the use of spectral indices of vegetation, it is possible to analyze its dynamics over time. This study aims to analyze the structure of vegetation cover in an area of the Caatinga Biome, comparing multispectral images acquired by satellite with different resolutions and low altitude unmanned aerial vehicle (UAV) platforms with high resolution cameras. Automated flights were carried out in November and December 2019 over the study area and the images were processed to generate orthomosaics. Landsat-8 and Sentinel-2 satellite images were acquired free of charge for comparison purposes with the UAV. The vigor of green vegetation was analyzed through the calculation of the Normalized Difference Vegetation Index (NDVI) and verified through the correlation between high resolution and low altitude products with satellites. Both products from satellites proved to be effective and good indicators of vegetation vigor, with emphasis on Sentinel-2 images, which obtained a better correlation with aerial UAV images reaching (R = 0.7) compared to Landsat-8 (R = 0.6). Satellite products showed good indicators for monitoring the structural characteristics of the Caatinga, however, they are not indicated for assessments of areas with a greater predominance of soil, water or other targets, as they can affect the NDVI values and make a more detailed assessment impossible. of the areas.

scholarly journals Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1617
Author(s):  
Anastasiia Safonova ◽  
Emilio Guirado ◽  
Yuriy Maglinets ◽  
Domingo Alcaraz-Segura ◽  
Siham Tabik
Keyword(s):  
High Performance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Ground Truth ◽  
Olive Tree ◽  
Accurate Estimation ◽  
Tree Crown ◽  
Olive Groves ◽  
Uav Images

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

scholarly journals Comparative Study on NDVI with RVI for Estimated Area and Class Distribution

2019 ◽  
Vol 3 (2) ◽  
pp. 12-20
Author(s):  
I Made Yuliara ◽  
Ni Nyoman Ratini ◽  
I Gde Antha Kasmawan
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Broad Class ◽  
Vegetation Distribution ◽  
Vegetation Analysis ◽  
Rare Class ◽  
Dense Class ◽  
Comparison Of The Results ◽  
Distribution Class ◽  
Area Data

This study aims to determine the differences and comparison of the results of the estimated area, the distribution of clove vegetation using the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) and to choose a vegetation index that is more suitable for clove vegetation analysis in Buleleng district, Bali. The method used is to compare statistically descriptive area and distribution class produced by the NDVI and RVI models with area data from the Forestry and Plantation Service (FPS), Buleleng regency, Bali in 2014, amounting to 7622.32 ha. The estimated area of ??clove vegetation by the NDVI model was 7852.68 ha and the RVI model was 7669.44 ha. There is an estimated difference in the area of ??clove vegetation of 183.24 ha and a difference in the broad class category of 2453.85 ha for the Rare class (NDVI > RVI) category, for the Medium class of 1611.45 ha (RVI > NDVI), and for the Dense class of 659.16 ha (RVI > NDVI). Comparison of the area with FPS data obtained 97.07% for the NDVI model and 99.39% for the RVI model. This shows that the RVI model vegetation index is more suitable for use in the estimation of the area and class of clove vegetation distribution in Buleleng regency, Bali.

Riverscape classification by using machine learning in combination with satellite and UAV images

2020 ◽  
Author(s):  
Hitoshi Miyamoto ◽  
Takuya Sato ◽  
Akito Momose ◽  
Shuji Iwami
Keyword(s):  
Machine Learning ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Training Data ◽  
Low Resolution ◽  
Critical Areas ◽  
Aerial Vehicle ◽  
Uav Images

<p>This presentation examined a new method for classifying riverine land covers by using the machine learning technique applied to both the satellite and UAV (Unmanned Aerial Vehicle) images in a Kurobe River channel.  The method used Random Forests (RF) for the classification with RGBs and NDVIs (Normalized Difference Vegetation Index) of the images in combination.  In the process, the high-resolution UAV images made it possible to create accurate training data for the land cover classification of the low-resolution satellite images.  The results indicated that the combination of the high- and low-resolution images in the machine learning could effectively detect waters, gravel/sand beds, trees, and grasses from the satellite images with a certain degree of accuracy.  In contrast, the usage of only low-resolution satellite images failed to detect the vegetation difference between trees and grasses.  These results could actively support the effectiveness of the present machine learning method in the combination of satellite and UAV images to grasp the most critical areas in riparian vegetation management.</p>

scholarly journals Comparison of Sentinel-2 and UAV Multispectral Data for Use in Precision Agriculture: An Application from Northern Greece

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 35
Author(s):  
Nikolaos Bollas ◽  
Eleni Kokinou ◽  
Vassilios Polychronos
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Northern Greece ◽  
Multispectral Data ◽  
Aerial Vehicles ◽  
The Difference ◽  
Uav Images ◽  
Sentinel 2

The scope of this work is to compare Sentinel-2 and unmanned aerial vehicles (UAV) imagery from northern Greece for use in precision agriculture by implementing statistical analysis and 2D visualization. Surveys took place on five dates with a difference between the sensing dates for the two techniques ranging from 1 to 4 days. Using the acquired images, we initially computed the maps of the Normalized Difference Vegetation Index (NDVI), then the values of this index for fifteen points and four polygons (areas). The UAV images were not resampled, aiming to compare both techniques based on their initial standards, as they are used by the farmers. Similarities between the two techniques are depicted on the trend of the NDVI means for both satellite and UAV techniques, considering the points and the polygons. The differences are in the a) mean NDVI values of the points and b) range of the NDVI values of the polygons probably because of the difference in the spatial resolution of the two techniques. The correlation coefficient of the NDVI values, considering both points and polygons, ranges between 83.5% and 98.26%. In conclusion, both techniques provide important information in precision agriculture depending on the spatial extent, resolution, and cost, as well as the requirements of the survey.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

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