Analysis of the MODIS-Based Vegetation Phenology Using the HANTS Algorithm

Choi, Chul-Hyun; Jung, Sung-Gwan

doi:10.11108/kagis.2014.17.3.020

Cited by 4 publications

(3 citation statements)

References 40 publications

(7 reference statements)

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“…The Harmonic Analyses of NDVI Time-Series (HANTS) algorithm was proposed for the extraction of the characteristics of the vegetation dynamics. The HANTS algorithm has been extensively used in rangeland LSP investigations successfully [111,[124][125][126]. The amplitude and harmonic components of the algorithm makes HANTS attractive for LSP studies [125].…”

Section: Lsp Software Packages For Data Processingmentioning

confidence: 99%

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

et al. 2021

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Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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Section: Lsp Software Packages For Data Processingmentioning

confidence: 99%

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

et al. 2021

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“…Based on such data, a basis for objective judgment can be prepared. A photogrammetric technique using drone images to generate high-quality 3D spatial information may facilitate the prediction and mitigation of environmental impacts based on high resolution images (Dong & Cho, 1992;Korea Environment Institute, 2019;Song, 2021;Kang & Lee, 2022, Kim, 2018Park, 2021;Ko et al, 2021;Cho et al, 2020). Also, if there is an EIASS platform that handles domestic environmental impact assessment, the US EPA platform, Germany's EIA platform, and Japan's EADAS platform are in operation.…”

Section: Introductionmentioning

confidence: 99%

Digitalization for the environmental impact assessment of railway projects using drones and lidar

KIM,

Park,

Hwang

et al. 2024

Preprint

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Environmental impact assessments of Korea's railway projects are currently presented qualitatively and follow guidance in the Eco-friendly Railway Construction Guidelines (2015). Quantitative prediction and evaluation are limited due to limitations conducting literature research or making uniform measurements. Recent developments in the quantification of assessment data emphasize the collection of environmental with Fourth Industrial Revolution technologies such as drones, lidar, big data, and artificial intelligence (AI). In this study, we reviewed the limitations of existing evaluation methods for six key evaluation items for the assessment of railroad projects (air quality, water quality, ecology, topography, noise and vibration, and scenic value). For the first time in Korea, we developed a digitalization plan that will secure quantitative data through drones and lidar for environmental impact evaluations of railroad projects. Based on our results, a preemptive response system can be established for the digitalization of environmental impact assessment for the railway business. In the future, it will be necessary to apply the technologies used in this study to collect continuous time-series data based on the methods established in this study, and to implement specific prediction models.

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“…In addition, accelerated plant phenology, due to raised annual temperature and precipitation caused by climate change, has been reported (Ministry of Environment 2018). Therefore, several studies on climate change and plant phenological responses have been published in Korea (Kim and You 2010b;Han et al 2012;Lee et al 2012;Shin and You 2012;Choi and Jung 2014;Park et al 2014a;Park et al 2014b).…”

Section: Introductionmentioning

confidence: 99%

Studies on the changes in phenological, growth and physiological responses of Silene capitata Kom., an endangered plant in Korea, under climate change treatment

et al. 2019

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Background: This research aims to study the effect of climate change on the phenology, growth, and physiological traits of Silene capitata Kom., a Korean endangered species II. This study increased CO 2 concentration in a closed glass greenhouse, with the daily mean temperature and CO 2 concentration respectively being 4.61°C and 93.63 ppm higher than the outside temperature (ambient conditions, control). The seeds of S. capitata were sown in control and treatment environments in March 2013 while seedlings were transplanted into individual pots in May 2013. To research phenological changes, the first day of the flowering and ripening of the plants transplanted in 2013 and first day of leafing in 2014 were observed. The growth and physiological responses of mature leaves were also studied in 2013. Results: There was no difference in the first day of flowering, but the first day of ripening was earlier in the treatment group than the control group. There was no difference in the number of rosette leaves between the two groups, but leaf area was wider in the treatment group than the control group. Transpiration rate and stomatal conductance were higher in the treatment group than the control group, chlorophyll content decreased, and photosynthetic rate and water use efficiency were the same for both groups. As a result of simple regression analysis among the transpiration rate, stomatal conductance, photosynthetic rate, and water use efficiency, stomatal conductance increased when transpiration rate increased. Stomatal conductance increased with photosynthetic rate in the control unlike in the treatment group. The photosynthetic rate and water use efficiency increased with transpiration rate in the control group unlike in the treatment group. Furthermore, water use efficiency increased as photosynthetic rate increased in both groups. Conclusion: Due to high CO 2 concentration, the photosynthetic rate was no longer controlled by the stomata, which appeared to suppress the excessive production of photosynthetic products by reducing chlorophyll content. It is believed that the phenological responses of S. capitata under climate change conditions will advance and that stable growth will be difficult in regions lacking moisture due to the high transpiration rate.

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Analysis of the MODIS-Based Vegetation Phenology Using the HANTS Algorithm

Cited by 4 publications

References 40 publications

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Digitalization for the environmental impact assessment of railway projects using drones and lidar

Studies on the changes in phenological, growth and physiological responses of Silene capitata Kom., an endangered plant in Korea, under climate change treatment

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