Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index

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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Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽

10.3390/f12010076 ◽

2021 ◽

Vol 12 (1) ◽

pp. 76

Author(s):

Yahui Guo ◽

Jing Zeng ◽

Wenxiang Wu ◽

Shunqiang Hu ◽

Guangxu Liu ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Linear Trend ◽

Spectral Characteristics ◽

Growth Conditions ◽

Temporal Changes ◽

Vegetation Coverage ◽

Dynamic Changes ◽

Spatial And Temporal Changes ◽

Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

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ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-223-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 223-227 ◽

Cited By ~ 2

Author(s):

Yi-Ta Hsieh ◽

Shou-Tsung Wu ◽

Chaur-Tzuhn Chen ◽

Jan-Chang Chen

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Vegetation Index ◽

Spectral Characteristics ◽

Aerial Images ◽

Aerial Image ◽

Optical Remote Sensing ◽

Shadow Area ◽

Vegetation Indexes ◽

Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

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Remote Sensing Landslide Recognition Based on Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2019/8389368 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yu Wang ◽

Xiaofei Wang ◽

Junfan Jian

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Large Scale ◽

Vegetation Index ◽

Near Infrared ◽

Spatial Information ◽

Spectral Characteristics ◽

Remote Sensing Images ◽

Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

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Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia

Modeling Earth Systems and Environment ◽

10.1007/s40808-020-00829-3 ◽

2020 ◽

Vol 6 (4) ◽

pp. 2487-2493 ◽

Cited By ~ 2

Author(s):

Hazem T. Abd El-Hamid ◽

Guan Hong

Keyword(s):

Remote Sensing ◽

Spectral Data ◽

Vegetation Index ◽

Spectral Characteristics ◽

Salt Content ◽

Soil Salinization ◽

Northern Region ◽

Semiarid Region ◽

First Order ◽

Remote Sensing Monitoring

Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.

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The effect of upper cortex absence on spectral reflectance indices in Antarctic lichens during thallus dehydration

Czech Polar Reports ◽

10.5817/cpr2018-1-8 ◽

2018 ◽

Vol 8 (1) ◽

pp. 107-118 ◽

Cited By ~ 1

Author(s):

Alla Orekhova ◽

Michaela Marečková ◽

Jana Hazdrová ◽

Miloš Barták

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Characteristics ◽

Hydration Status ◽

Nostoc Commune ◽

Response Curves ◽

Relative Water ◽

James Ross Island ◽

Species Specific ◽

Reflectance Indices

In maritime Antarctica, lichens and mosses represent dominant autotrophs forming community structure of vegetation oases. In our study, we selected 4 most common lichen species (Xanthoria elegans, Rhizoplaca melanophthalma, Leptogium puberulum, Physconia muscigena) and monospecific colony of Nostoc commune typical for James Ross Island (Antarctica) for detailed physiological experiments. We investigated their spectral characteristics in response to hydration status of their thalli. In samples desiccating from fully wet (RWC, relative water content of 100%) to dry state (RWC = 0), photochemical reflectance index (PRI), and normalized difference vegetation index (NDVI) were evaluated for control thalli and those with removed upper cortex. In this way, the effect of presence/absence of the upper cortex on PRI, NDVI was studied. PRI showed either no change or species-specific an increase/decrease with dehydration. Removal of the upper cortex caused both PRI decrease (N. commune, P. muscigena) and increase (R. melanophthalma, L. puberulum). Removal of the upper cortex led to increase in NDVI in all species, typically within the RWC range of 20-100%. Species-specific differences of hydration-response curves of PRI and NDVI are discussed as well as the role of the absence of the upper cortex in the evaluation of spectral characteristics in desiccating lichens.

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Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

Czech Polar Reports ◽

10.5817/cpr2018-2-21 ◽

2018 ◽

Vol 8 (2) ◽

pp. 249-259 ◽

Cited By ~ 2

Author(s):

Miloš Barták ◽

Kumud Bandhu Mishra ◽

Michaela Marečková

Keyword(s):

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Spectral Characteristics ◽

Structural Features ◽

Coefficient Of Determination ◽

Curvilinear Relationship ◽

Structural Deterioration ◽

Reflectance Indices

Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

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STUDY ON REMOTE SENSING IMAGE CHARACTERISTICS OF ECOLOGICAL LAND: CASE STUDY OF ORIGINAL ECOLOGICAL LAND IN THE YELLOW RIVER DELTA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-35-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 35-38

Author(s):

G. Q. An

Keyword(s):

Remote Sensing ◽

Land Use ◽

Vegetation Index ◽

Yellow River ◽

Spectral Characteristics ◽

Texture Features ◽

Yellow River Delta ◽

River Delta ◽

The Yellow River ◽

The Yellow River Delta

Takes the Yellow River Delta as an example, this paper studies the characteristics of remote sensing imagery with dominant ecological functional land use types, compares the advantages and disadvantages of different image in interpreting ecological land use, and uses research results to analyse the changing trend of ecological land in the study area in the past 30 years. The main methods include multi-period, different sensor images and different seasonal spectral curves, vegetation index, GIS and data analysis methods. The results show that the main ecological land in the Yellow River Delta included coastal beaches, saline-alkaline lands, and water bodies. These lands have relatively distinct spectral and texture features. The spectral features along the beach show characteristics of absorption in the green band and reflection in the red band. This feature is less affected by the acquisition year, season, and sensor type. Saline-alkali land due to the influence of some saline-alkaline-tolerant plants such as alkali tent, Tamarix and other vegetation, the spectral characteristics have a certain seasonal changes, winter and spring NDVI index is less than the summer and autumn vegetation index. The spectral characteristics of a water body generally decrease rapidly with increasing wavelength, and the reflectance in the red band increases with increasing sediment concentration. In conclusion, according to the spectral characteristics and image texture features of the ecological land in the Yellow River Delta, the accuracy of image interpretation of such ecological land can be improved.

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Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

Journal of Forest Science ◽

10.17221/28/2020-jfs ◽

2020 ◽

Vol 66 (No. 8) ◽

pp. 339-348

Author(s):

Viktoriia Lovynska ◽

Yuriy Buchavyi ◽

Petro Lakyda ◽

Svitlana Sytnyk ◽

Yuriy Gritzan ◽

...

Keyword(s):

Aboveground Biomass ◽

Vegetation Index ◽

Spectral Characteristics ◽

Free Access ◽

Biophysical Parameters ◽

Normalised Difference Vegetation Index ◽

Pine Stands ◽

Predicted Values ◽

Vegetative Indices ◽

Sentinel 2

The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetation index (NDVI) and transformed vegetation index (TVI) vegetative indices and the fraction of absorbed photosynthetic active radiation (FARAP) and fraction of vegetation cover (FCOVER) biophysical parameters. To generate the models of determining the forest aboveground biomass (AGB), we used both the single- and two-factor models, the most optimum of which were those containing the NDVI predictor separately and in combination with the FCOVER predictor. The predicted values of the total AGB for the mentioned models equalled 32.5 to 236.3 and 39.9 to 253.4 t·ha<sup>–1</sup>. We performed mapping of the AGB of pine stands of the Northern Steppe using multi-spectral Sentinel-2 images, particularly the spectral characteristics of their derivatives (vegetative indices, biophysical parameters). This study demonstrated promising results for conducting an AGB-mapping of pine woods in the studied region using free-access resources.

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Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

Remote Sensing ◽

10.3390/rs10101528 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1528 ◽

Cited By ~ 13

Author(s):

Liang Han ◽

Guijun Yang ◽

Haikuan Feng ◽

Chengquan Zhou ◽

Hao Yang ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Characteristics ◽

Canopy Structure ◽

Canopy Cover ◽

Structural Features ◽

Discrimination Ability ◽

Maize Varieties ◽

Aerial Vehicle

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

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