• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.384-401
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• 2016

The vegetation assessment indicator has been developed recently as a biological part of the integrated assessment system for river environment to improve the efficiency of river restoration projects. This study carried out to test the vegetation assessment indicator and to reset its grade criteria on experimental streams. We classified and mapped vegetation communities at the level of physiognomic-floristic composition by each assessment unit. A total of 204 sampling quadrats were set up on the 68 assessment units at 5 experimental streams. By analyzing the vegetation data collected, we examined the appropriate numbers of sampling quadrats, the criteria of vegetation index score, classification of vegetation community, and grade criteria for vegetation assessment. The developed vegetation assessment indicator composed with the vegetation complexity index (VCI), the vegetation diversity index (VDI), and the vegetation naturalness index (VNI) was proved to reflect the current conditions of the streams sufficiently. The contribution of vegetation naturalness index to grading by vegetation assessment indicator was larger, but three indexes were closely correlated to each other. Also there was more clearer discrimination of grading with the application of adjusted criteria of vegetation assessment indicator and the standardized classification of vegetation community, but the stream segment type did not influence the vegetation assessment grade significantly.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.292-303
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• 2021

Studies that relate the structure of tropical regrowth vegetation to soil properties are generally lacking in the literature. This study proposes three indices for assessing the structural regeneration of secondary forests. They are: (1) the tree diameter class, (2) the plant life form and (3) the woody/herbaceous plants ratio indices. They were applied to assess the regeneration status of forest regrowth vegetation (aged 1-10 years), derived savanna regrowth vegetation in south western Nigeria, and to secondary forests in different stages of succession in Columbia and Venezuela, Bolivia, Mexico in South and Central America and semi-arid savanna in Ethiopia and seasonal deciduous forest successional stages in India. In all the cases, the indices increased with increasing age of regrowth vegetation and hence, with increasing structural complexity of regenerating vegetation. The tree diameter class index increased from 32.1% in a 9-year secondary forest to 69.0% in an 80-year-old secondary forest in Columbia and Venezuela and from 0.4% in a 1-year fallow to 20.9% in 10-year regrowth vegetation in southwestern Nigeria. In semi-arid savanna in northern Ethiopia, the woody/herbaceous plants ratio index increased from 18.1% in a 5-year protected grazing enclosure to 75.1% in 15-year protected enclosure, relative to the status of 20-year enclosure. The indices generally had correlations of 0.6-0.90 with species richness and Simpson's/Margalef's species diversity, implying that they are appropriate measures of ecosystem development over time. The proposed indices also had strong and positive correlations with soil organic carbon and nutrients. They are therefore, significant indicators of fertility status.

• Journal of Korea Water Resources Association
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• v.52 no.spc2
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• pp.765-776
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• 2019

The purpose of this study is to provide the legal and institutional guidelines and standards that can be used in the whole river restoration project and to analyze and evaluate the performance of the river project. We constructed an assessment system of four biological taxa that can represent the river environments, namely, evaluation indexes and standards of vegetation and birds, benthic invertebrates and fishes. Specifically, the assessment indicator and criteria of biological characteristics are summarized, so that in case of vegetation community, vegetation diversity, vegetation complexity, and vegetation naturalness can be quantitatively assessed through the combination of three indices. Based on the scientific basis of the advanced techniques, benthic invertebrates, fishes, and birds were proposed to quantitatively evaluate assessment grades according to the classification of biological data. In order to evaluate biological characteristics, which are a part of river environmental naturalness, we proposed a comprehensive biological index and evaluation grade applying the weight of these four biological taxa, and it clearly reflects the characteristics of river environment in test bed.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.3-11
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• 2016

Sixty four percent of Korean territory consists of forest which is fragile for forest fire. However, it is difficult to detect the disaster-induced damages due to topographic complexity in mountainous areas and harsh weather conditions. For this reason, satellite imaging systems have been widely utilized to detect the damage caused by forest fire. In particular, ground vegetation condition can be estimated from multi-spectral satellite images and change detection technique has been used to detect forest fire damages. However, since Korea has clear four seasons, simple change detection technique has limitation. In this regard, this study applied the NDVI(normalized difference vegetation index) annual cycle modeling technique on time-series of Landsat images from 1991 to 2007 to analyze influence of forest fire of Yangyang-gun, Gangwon-do in 2005 on vegetation condition. The encouraging result was obtained when comparing the areas where forest fire occurs with non-damaged areas. The mean value of NDVI was decreased by 0.07 before and after the forest fire. On the other hand, annual variability of NDVI had been increasing and peak value of NDVI was stationary after the forest fire. It is interpreted that understory vegetation was seriously damaged from the forest fire occurred in 2005.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.238-245
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• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.