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Articoli di riviste sul tema "Vegetation monitoring"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Khan, Asim, Warda Asim, Anwaar Ulhaq, and Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data." PLOS ONE 17, no. 7 (2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.

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Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen scien

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Maxwald, Melanie, Markus Immitzer, Hans Peter Rauch, and Federico Preti. "Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data." Fire 5, no. 6 (2022): 211. http://dx.doi.org/10.3390/fire5060211.

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In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation’s development in different fire severity classes after a wildfire in September 2019. Random forest

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H. Shahad, Shaimаа, and Mutаsim I. Malik. "Monitoring Vegetation Area in Wasit/ Iraq using Normalized Difference Vegetation Index (NDVI)." IAR Journal of Engineering and Technology 6, no. 1 (2025): 1–4. https://doi.org/10.47310/iarjet.2025.v06i01.006.

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In the current work, the Landsat data were applied to monitor vegetation in the governorate of Wasit in Iraq country as the region of interest during the period between 2012 and 2024. NDVI was calculated and analyzed by using Landsat Satellite Image in GIS environment which was used to get binary images through analysis. Vegetation changes in this area were estimated and explored with the help of the Normalized Difference Vegetation Index (NDVI) as a vegetation index. The findings display minor increment in the vegetation rates. The binary image extraction method was applied to estimate the ar

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4

Økland, T. "Vegetational and ecological monitoring of boreal forests in Norway. I. Rausjømarka in Akershus county, SE Norway." Sommerfeltia 10, no. 1 (1990): 1–56. http://dx.doi.org/10.2478/som-1990-0001.

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Abstract Vegetational and ecological monitoring of boreal forests in Norway was initiated in 1988, as a part of the programme “Countrywide monitoring of forest health” at Norwegian Institute of Land Invetory (NIJOS). Ten reference areas for monitoring will be established and analysed within five years; two new areas each year. Each of the monitoring areas is planned to be reanalysed every fifth year. In each monitoring area 10 macro sample plots, 50 m2 each, are selected. Within each macro sample plot 5 meso sample plots, 1 m2 each, are randomly placed and the vegetation is analysed by using f

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HONDA, Yoshiaki, Shunji MURAI, and Kikuuo KATOOU. "Global Monitoring of Vegetation." Journal of the Japan society of photogrammetry and remote sensing 31, no. 1 (1992): 4–14. http://dx.doi.org/10.4287/jsprs.31.4.

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6

Pádua, Luís, Pedro Marques, Jonáš Hruška, et al. "Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery." Remote Sensing 10, no. 12 (2018): 1907. http://dx.doi.org/10.3390/rs10121907.

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This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to comp

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7

Halip, Rowena Mat, Nik Norasma Che’Ya, Rhushalshafira Rosle, Mohd Razi Ismail, Zulkarami Berahim, and Mohamad Husni Omar. "Enhancing Rice Crop Monitoring Through UAV Imagery And GIS Analysis." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012014. https://doi.org/10.1088/1755-1315/1412/1/012014.

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Abstract Unmanned aerial vehicles (UAVs) are becoming increasingly important in many industries, and agriculture is no exception. This study aimed to monitor rice fields by using UAV-based imagery to map rice fields thoroughly and using on-the-ground Soil Plant Analysis Development (SPAD) data to evaluate crop health. A series of UAV-captured photos were analyzed as part of the inquiry, and the results included the creation of reclassification maps and Normalized Difference Vegetation Index (NDVI), which were reinforced by GIS analysis. The results show that during the 11-day post-sowing phase

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8

Dhakal, Rabin, Abhishek Ghimire, Sanjay Nepal, and Kapalik Khanal. "PocketQube development for earth exploration and vegetation monitoring." Journal of Innovations in Engineering Education 5, no. 1 (2022): 77–83. http://dx.doi.org/10.3126/jiee.v5i1.43925.

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A PocketQube is being popular these days due to the enhancement in technologies for space research and earth observation. It is extremely vital to analyze the condition of vegetation on the earth surface because deforestation, forest fire and smuggling of precious plants have been increasing dramatically. The camera module in the payload captures the image from the space which helps in analyzing the vegetative condition of the forest on the earth’s surface. The provided image data is huge in order to maintain the quality of the images it captures. Hence, the received image data is further divi

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9

Shukla, Sonali. "Vegetation Monitoring System-A Review." International Journal for Research in Applied Science and Engineering Technology 6, no. 2 (2018): 258–63. http://dx.doi.org/10.22214/ijraset.2018.2040.

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10

Mohler, Robert R. J., Gordon L. Wells, Cecil R. Hallurn, and Michael H. Trenchard. "Monitoring vegetation of drought environments." BioScience 36, no. 7 (1986): 478–83. http://dx.doi.org/10.2307/1310346.

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11

Gobron, N., A. Belward, B. Pinty, and W. Knorr. "Monitoring biosphere vegetation 1998-2009." Geophysical Research Letters 37, no. 15 (2010): n/a. http://dx.doi.org/10.1029/2010gl043870.

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12

Retalis, A. "Modern Approaches in Vegetation Monitoring." Photogrammetric Record 21, no. 114 (2006): 182. http://dx.doi.org/10.1111/j.1477-9730.2006.00375_3.x.

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13

Zhang, Xiaoyang, Mark A. Friedl, Crystal B. Schaaf, et al. "Monitoring vegetation phenology using MODIS." Remote Sensing of Environment 84, no. 3 (2003): 471–75. http://dx.doi.org/10.1016/s0034-4257(02)00135-9.

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14

Wildi, O., E. Feldmeyer-Christe, S. Ghosh, and N. E. Zimmermann. "Comments on vegetation monitoring approaches." Community Ecology 5, no. 1 (2004): 1–5. http://dx.doi.org/10.1556/comec.5.2004.1.1.

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15

Li, Hongjin. "Monitoring of vegetation changes based on RS monitoring in Changbai Mountain Nature Reserve." Applied and Computational Engineering 85, no. 1 (2024): 165–71. http://dx.doi.org/10.54254/2755-2721/85/20240903.

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High vegetation cover helps maintain the ecosystem's stability and reduces the risk of soil erosion and natural disasters. Vegetation restoration is essential for the ecological value of Changbai Mountain Nature Reserve. In this study, the interannual changes in vegetation cover in Changbai Mountain Nature Reserve, China, from 2013 to 2022 were studied in depth based on Landsat remote sensing data and land use transfer matrix. The study includes interannual dynamic changes in vegetation types, quantitative analysis of interannual changes in surface vegetation types, and the land use transfer m

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16

Ruzikulova, Oykhumor. "Analysis of vegetation changes in land area of Syrdarya region using GIS technology and remote sensing data." E3S Web of Conferences 401 (2023): 04008. http://dx.doi.org/10.1051/e3sconf/202340104008.

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This article presents a map of vegetative changes in the Syrdarya region based on remote sensing data. Landsat 8 and Landsat 9 satellite images were used for analysis during the vegetation active period. The study examines the vegetation state of the selected area from 2000 to 2022 and analyzes the changes. The Normalized Difference Vegetation Index (NDVI) was calculated using ArcGIS 10.6 software and documented sequentially. The number of color-coded pixels on the map indicating the health and unhealthiness of the crops and the areas they occupy was determined through NDVI analysis. The study

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17

Jiang, Yanmin, Haijing Shi, Zhongming Wen, et al. "Monitoring of Flash Drought on the Loess Plateau and Its Impact on Vegetation Ecosystems." Forests 15, no. 8 (2024): 1455. http://dx.doi.org/10.3390/f15081455.

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Flash drought (FD) has attracted much attention due to its severe stress on vegetation ecosystems. Yet to date, the impacts of FD on vegetation ecosystems in different regions have not been fully evaluated and explored, especially for ecologically fragile areas. In this study, we identified the FD events in the Loess Plateau from 2000 to 2023 based on the attenuation rate in soil moisture percentile over time. The evolution process of FD, the driving roles of meteorological conditions and the responses of different vegetation types to FD were explored by vegetation indicators such as solar-ind

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18

Lin, Wen-Pin. "Monitoring and Protection of Forest Ecological Tourism Resources by Dynamic Monitoring System." Ecological Chemistry and Engineering S 26, no. 1 (2019): 189–97. http://dx.doi.org/10.1515/eces-2019-0018.

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Abstract Forest can adjust climate and provide resources for the development of the society and tourism as well as promote the progress of human civilization, which is of great significance to the survival and development of human beings. With the industrial development and the improvement of people’s living standard, the development strength on forest resources is becoming higher than ever before. As forest resources are important resources which can maintain the ecological balance of the earth, its monitoring and protection is necessary. Hence, remote sensing technology has been developed fo

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19

Arushanov, M. L., and H. U. Umerrov. "VEGETATION COVER MONITORING BASED ON NOAA/AVHRR DATA." Deutsche internationale Zeitschrift für zeitgenössische Wissenschaft 105 (June 6, 2025): 8–13. https://doi.org/10.5281/zenodo.15609337.

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Data on vegetation cover over vast territories cannot be obtained based on traditional in-kind measurements due to the lack of a specialized observation network. Point measurements of vegetation parameters at different times do not allow tracking its dynamics, where the “from the particular to the general” method is not applicable. Under these conditions, the only source of information on the state of vegetation cover is space imagery data, which makes it possible to simultaneously monitor the state of vegetation cover over large areas and periodically repeat these observation

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20

Eastwood, J. A., M. G. Yates, A. G. Thomson, and R. M. Fuller. "The reliability of vegetation indices for monitoring saltmarsh vegetation cover." International Journal of Remote Sensing 18, no. 18 (1997): 3901–7. http://dx.doi.org/10.1080/014311697216739.

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21

Durpaire, J. P., T. Gentet, T. Phulpin, and M. Arnaud. "Spot-4 vegetation instrument: Vegetation monitoring on a global scale." Acta Astronautica 35, no. 7 (1995): 453–59. http://dx.doi.org/10.1016/0094-5765(94)00279-u.

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22

Pei, Fengsong, Changjiang Wu, Xiaoping Liu, et al. "Monitoring the vegetation activity in China using vegetation health indices." Agricultural and Forest Meteorology 248 (January 2018): 215–27. http://dx.doi.org/10.1016/j.agrformet.2017.10.001.

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23

Zribi, Mehrez, Erwan Motte, Pascal Fanise, and Walid Zouaoui. "Low-Cost GPS Receivers for the Monitoring of Sunflower Cover Dynamics." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6941739.

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The aim of this research is to analyze the potential use of Global Navigation Satellite System (GNSS) signals for the monitoring of in situ vegetation characteristics. An instrument, based on the use of a pair of low-cost receivers and antennas, providing continuous measurements of all the available Global Positioning System (GPS) satellite signals is proposed for the determination of signal attenuation caused by a sunflower cover. Experimental campaigns with this instrument, combined with ground truth measurements of the vegetation, were performed over a nonirrigated sunflower test field for

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24

Gouveia, C., R. M. Trigo, and C. C. DaCamara. "Drought and vegetation stress monitoring in Portugal using satellite data." Natural Hazards and Earth System Sciences 9, no. 1 (2009): 185–95. http://dx.doi.org/10.5194/nhess-9-185-2009.

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Abstract. Remote sensed information on vegetation and soil moisture, namely the Normalised Difference Vegetation Index (NDVI) and the Soil Water Index (SWI), is employed to monitor the spatial extent, severity and persistence of drought episodes over Continental Portugal, from 1999 to 2006. The severity of a given drought episode is assessed by evaluating the cumulative impact over time of drought conditions on vegetation. Special attention is given to the drought episodes that have occurred in the last decade, i.e., 1999, 2002 and particularly the major event of 2005. During both the 1999 and

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25

Nuijten, Rik J. G., Nicholas C. Coops, Catherine Watson, and Dustin Theberge. "Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry." Remote Sensing 13, no. 10 (2021): 1942. http://dx.doi.org/10.3390/rs13101942.

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Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classe

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26

Priya, M. V., R. Kalpana, S. Pazhanivelan, et al. "Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu." Journal of Applied and Natural Science 15, no. 3 (2023): 1170–77. http://dx.doi.org/10.31018/jans.v15i3.4803.

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Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. T

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27

Kurbanov, R. K., and N. I. Zakharova. "Application of Vegetation Indexes to Assess the Condition of Crops." Agricultural Machinery and Technologies 14, no. 4 (2020): 4–11. http://dx.doi.org/10.22314/2073-7599-2020-14-4-4-11.

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Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the st

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28

Smith, Tyler, Jeremy Lundholm, and Len Simser. "Wetland Vegetation Monitoring in Cootes Paradise." Ecological Restoration 19, no. 3 (2001): 145–54. http://dx.doi.org/10.3368/er.19.3.145.

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Alvino, Francisco C. G., Catariny C. Aleman, Roberto Filgueiras, Daniel Althoff, and Fernando F. da Cunha. "VEGETATION INDICES FOR IRRIGATED CORN MONITORING." Engenharia Agrícola 40, no. 3 (2020): 322–33. http://dx.doi.org/10.1590/1809-4430-eng.agric.v40n3p322-333/2020.

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MIDGLEY, GUY. "Monitoring vegetation: a science in flux?" Journal of Biogeography 29, no. 7 (2002): 971–72. http://dx.doi.org/10.1046/j.1365-2699.2002.00689.x.

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HOLMES, M. G. "Monitoring vegetation in the future: radar." Botanical Journal of the Linnean Society 108, no. 2 (1992): 93–109. http://dx.doi.org/10.1111/j.1095-8339.1992.tb01634.x.

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Svanberg, S. "Fluorescence lidar monitoring of vegetation status." Physica Scripta T58 (January 1, 1995): 79–85. http://dx.doi.org/10.1088/0031-8949/1995/t58/009.

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Barman, Pritam Kumar, Shivani Rawat, Avni Kumari, and Afaq Majid Wani. "Monitoring the Vegetation Condition of Gorumara National Park Using NDVI and NDMI Indices." International Journal of Bio-resource and Stress Management 15, Feb, 2 (2024): 01–07. http://dx.doi.org/10.23910/1.2024.5052.

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The present study was conducted from November, 2022 to June, 2023 aims to analyze and detect changes in vegetation using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) in Gorumara National Park, Jalpaiguri district, West Bengal, India. To calculate NDVI and NDMI values, Landsat 8 level-1 images acquired between 2016 and 2021. Different band combinations of the remote sensing data are analyzed to classify the vegetation condition and cover. For this study, the 4 (Red), 5 (NIR), and 6 (SWIR) multi-spectral band combinations are used separately.

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Xu, Nianxu, Jia Tian, Qingjiu Tian, Kaijian Xu, and Shaofei Tang. "Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index." Remote Sensing 11, no. 10 (2019): 1192. http://dx.doi.org/10.3390/rs11101192.

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Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegeta

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Hu, Xueqian, Li Li, Jianxi Huang, et al. "Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends." Science of The Total Environment 945 (October 2024): 173974. http://dx.doi.org/10.1016/j.scitotenv.2024.173974.

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Shi, Mengxi, Shuhan Xing, He Bai, Dawei Xu, and Lei Shi. "The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology." Plants 13, no. 22 (2024): 3164. http://dx.doi.org/10.3390/plants13223164.

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This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precisio

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Zhang, Lifu, Na Qiao, Muhammad Hasan Ali Baig, et al. "Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD." Remote Sensing Letters 10, no. 7 (2019): 629–38. http://dx.doi.org/10.1080/2150704x.2019.1597298.

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Li, Huaimin, Weipan Lin, Fangrong Pang, et al. "Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis." Sensors 20, no. 10 (2020): 2894. http://dx.doi.org/10.3390/s20102894.

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An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies

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Atanasov, Asparuh, Radko Mihaylov, Svilen Stoyanov, Desislava Mihaylova, and Peter Benov. "Drone-based Monitoring of Sunflower Crops." ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 6, no. 1 (2022): 1–9. http://dx.doi.org/10.29114/ajtuv.vol6.iss1.258.

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Remote monitoring and utilization of digital technologies is essential for the application of the precision farming approach, which contributes significantly to the improved quality of agricultural products. The paper compares the data for six vegetation indices when observing the sunflower vegetation in South Dobrudzha in 2021. Images with RGB and digital NIR camera were obtained via a remotely piloted quadcopter. The flight plan specifies speed 8 m/s, altitude 100 m and shooting overlapping images of 80%. Six vegetation indices: NDVI, EVI2, SAVI, CVI, MGVRI and MPRI were calculated from the

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Gharbi, M. A., and H. N. Mukhlif. "Using of Temporal Variability for Monitoring Change of Vegetation via Remote Sensing in Anbar Province." IOP Conference Series: Earth and Environmental Science 904, no. 1 (2021): 012038. http://dx.doi.org/10.1088/1755-1315/904/1/012038.

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Abstract The use of spectral indicators and evidence based on remote sensing and the results of space data is one of the most important means of detecting changes in vegetation and land cover. Three space scenes were selected to assess vegetation changes in Anbar governorate, consisting of 33 satellite shots in different dates for the years 1999, 2009 and 2019, captured by the Landsat 5 and Landsat 8 for the two sensors TM and OLI. Spectral data were used to calculate NDVI normal vegetative variation index values. The results showed that the area of barren land continued to increase by 24,809.

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ZHANG, Yajie, Weihang XU, and Yuxin JIANG. "Remote Sensing Monitoring of Vegetation Change in Yungang Area for Ecological Restoration." Acta Interdisciplinary Science 2, no. 1 (2025): 1–9. https://doi.org/10.48014/ais.20241004001.

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Coal mining will change the land nutrient conditions and affect the growth of surface vegetation. In view of the lack of analysis and research on the spatio-temporal changes of vegetation coverage in Yungang District, Shanxi Province, in the hinterland of Datong coalfield, deeply explored the vegetation index information from remote sensing data and conducted statistical analysis of vegetation time series. Based on landsat8 oli images from 2019 to 2022, the normalized difference vegetation index (NDVI) , vegetation coverage and greenness change rate were extracted, and the long-term vegetation

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Dong, Shi Wei, Dan Feng Sun, and Hong Li. "Vegetation Fraction Change Monitoring in Beijing by Remote Sensing." Advanced Materials Research 955-959 (June 2014): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.859.

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Vegetation fraction was a most important index to score the vegetation coverage on the land surface. Improved dimidiate pixel model was applied to calculate and analyze the vegetation fraction change from September 2000 to September 2013 in Beijing, China. The results showed that vegetation coverage of Beijing in 2013 year was much better than 2000 year. The area of low and middle-low coverage of Beijing in 2013 decreased 379 km2 and 591 km2 respectively, and the area of high and middle coverage increased 885 km2 and 85 km2 respectively. The research provided a necessary reference for the rela

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Almalki, Raid, Mehdi Khaki, Patricia M. Saco, and Jose F. Rodriguez. "Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review." Remote Sensing 14, no. 20 (2022): 5143. http://dx.doi.org/10.3390/rs14205143.

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Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce

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Zhang, Aiwu, Shaoxing Hu, Xizhen Zhang, et al. "A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging." Agriculture 11, no. 12 (2021): 1262. http://dx.doi.org/10.3390/agriculture11121262.

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Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a re

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Ahn, Yu Bin, Ji Hyun Yoo, Yu Gun Chun, and Myeong Seong Lee. "Analysis of Changes in Vegetation Index Through Long-term Monitoring of Petroglyphs of Cheonjeon-ri, Ulju." Journal of Conservation Science 37, no. 6 (2021): 659–69. http://dx.doi.org/10.12654/jcs.2021.37.6.05.

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In this study, vegetation index, the vegetation index calculated based on hyperspectral images was used to monitor Petroglyphs of Cheonjeon-ri, Ulju from 2014 to 2020. To select suitable the vegetation index for monitoring, indoor analysis was performed, and considering the sensitivity to biocontamination, Normalized Difference Vegetation Index (NDVI) and Triangular Vegetation Index (TVI) were selected. As a result of monitoring using the selected vegetation index, NDVI increased from 2014 to 2018 and then decreased in 2020, after preservation treatment. On the other hand, TVI was difficult to

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He, Dong, Xianglin Huang, Qingjiu Tian, and Zhichao Zhang. "Changes in Vegetation Growth Dynamics and Relations with Climate in Inner Mongolia under More Strict Multiple Pre-Processing (2000–2018)." Sustainability 12, no. 6 (2020): 2534. http://dx.doi.org/10.3390/su12062534.

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Inner Mongolia Autonomous Region (IMAR) is related to China’s ecological security and the improvement of ecological environment; thus, the vegetation’s response to climate changes in IMAR has become an important part of current global change research. As existing achievements have certain deficiencies in data preprocessing, technical methods and research scales, we correct the incomplete data pre-processing and low verification accuracy; use grey relational analysis (GRA) to study the response of Enhanced Vegetation Index (EVI) in the growing season to climate factors on the pixel scale; explo

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Steyer, Gregory D., Brady R. Couvillion, and John A. Barras. "Monitoring Vegetation Response to Episodic Disturbance Events by using Multitemporal Vegetation Indices." Journal of Coastal Research 63 (April 2013): 118–30. http://dx.doi.org/10.2112/si63-011.1.

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48

Wang, Mengjia, Lei Fan, Frédéric Frappart, et al. "An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales." Remote Sensing of Environment 263 (September 2021): 112556. http://dx.doi.org/10.1016/j.rse.2021.112556.

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Du, Jinyang, Jiancheng Shi, Qiang Liu, and Lingmei Jiang. "Refinement of Microwave Vegetation Index Using Fourier Analysis for Monitoring Vegetation Dynamics." IEEE Geoscience and Remote Sensing Letters 10, no. 5 (2013): 1205–8. http://dx.doi.org/10.1109/lgrs.2012.2236297.

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50

Alexandridis, T. K., N. Oikonomakis, I. Z. Gitas, K. M. Eskridge, and N. G. Silleos. "The performance of vegetation indices for operational monitoring of CORINE vegetation types." International Journal of Remote Sensing 35, no. 9 (2014): 3268–85. http://dx.doi.org/10.1080/01431161.2014.902548.

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