Vegetation monitoring in rugged terrain with one novel topography — Adjusted vegetation index (TAVI)

2010 ◽  
Author(s):  
Hong Jiang ◽  
Xiao-qin Wang ◽  
Qin-min Wang
Keyword(s):  
Vegetation Index ◽  
Vegetation Monitoring ◽  
Rugged Terrain

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

2018 ◽  
Vol XLII-3 ◽  
pp. 1623-1627
Author(s):  
S. Talebi ◽  
J. Shi ◽  
T. Zhao
Keyword(s):  
Optical Depth ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
Scattering Effects ◽  
The Relationship

This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

scholarly journals Design of a Remote-Controlled Platform for Green Roof Plants Monitoring via Hyperspectral Sensors

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1368 ◽  
Author(s):  
Monica Moroni ◽  
Michele Porti ◽  
Patrizia Piro
Keyword(s):  
Green Infrastructure ◽  
Vegetation Index ◽  
Water Cycle ◽  
Green Roof ◽  
Hyperspectral Data ◽  
Climate Impacts ◽  
Spectral Signature ◽  
Health State ◽  
Vegetation Monitoring ◽  
Vegetation Health

The combination of an appropriate design and careful management of green infrastructures may contribute to mitigate flooding (stormwater quantity) and pollutant discharges (stormwater quality) into receiving water bodies and to coping with other extreme climate impacts (such as temperature regime) on a long-term basis and water cycle variability. The vegetation health state ensures the green infrastructure’s effectiveness. Due to their remarkable spatial and spectral resolution, hyperspectral sensing devices appear to be the most suited for green infrastructure vegetation monitoring according to the peculiar spectral features that vegetation exhibits. In particular, vegetation health-state detection is feasible due to the modifications the typical vegetation spectral signature undergoes when abnormalities are present. This paper presents a ground spectroscopy monitoring survey of the green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. The spectroradiometer, placed on a fixed stand, was used to identify stress conditions of vegetation located in areas where drought could affect the plant health state. Broadband vegetation indices were employed for this purpose. For the test case presented, data acquired agreed well with direct observations on the ground. The analyses carried out showed the remarkable performances of the broadband indices Red Difference Vegetation Index (Red DVI), Simple Ratio (SR) and Triangular Vegetation Index (TVI) in highlighting the vegetation health state and encouraged the design of a remote-controlled platform for monitoring purposes.

scholarly journals Evaluation and Normalization of Topographic Effects on Vegetation Indices

2020 ◽  
Vol 12 (14) ◽  
pp. 2290
Author(s):  
Rui Chen ◽  
Gaofei Yin ◽  
Guoxiang Liu ◽  
Jing Li ◽  
Aleixandre Verger
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Topographic Effects ◽  
Terrestrial Vegetation ◽  
Mountainous Areas ◽  
Near Infrared Reflectance ◽  
Illumination Condition ◽  
Vegetation Monitoring ◽  
Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

scholarly journals A coupled impact of different management and soil moisture on yield of winter wheat (Triticum aestivum L.) in dry conditions at locality Mezőföld, Hungary

2021 ◽  
Vol 69 (1) ◽  
pp. 76-86
Author(s):  
Gabor Milics
Keyword(s):  
Triticum Aestivum ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Nutrient Management ◽  
Sensor Technology ◽  
Soil Conditions ◽  
Variable Rate ◽  
Moisture Index ◽  
Vegetation Monitoring

AbstractVariable rate technology (VRT) in nutrient management has been developed in order to apply crop inputs according to the required amount of fertilizers. Meteorological conditions rarely differ within one field; however, differences in soil conditions responding to precipitation or evaporation results within field variations. These variations in soil properties such as moisture content, evapotranspiration ability, etc. requires site-specific treatments for the produced crops. There is an ongoing debate among experts on how to define management zones as well as how to define the required amount of fertilizers for phosphorus and nitrogen replenishment for winter wheat (Triticum aestivum L.) production. For management zone delineation, vegetation based or soil based data collection is applied, where various sensor technology or remote sensing is in help for the farmers. The objective of the study reported in this paper was to investigate the effect of soil moisture data derived from Sentinel-2 satellite images moisture index and variable rate phosphorus and nitrogen fertilizer by means of variable rate application (VRA) in winter wheat in Mezőföld, Hungary. Satellite based moisture index variance at the time of sowing has been derived, calculated and later used for data comparison. Data for selected points showed strong correlation (R2 = 0.8056; n = 6) between moisture index and yield, however generally for the whole field correlation does not appear. Vegetation monitoring has been carried out by means of NDVI data calculation. On the field level, as indicated earlier neither moisture index values at sowing nor vegetation index data was sufficient to determine yield. Winter wheat production based on VRA treatment resulted significant increase in harvested crop: 5.07 t/h in 2013 compared to 8.9 t/ha in 2018. Uniformly managed (control) areas provided similar yield as VRA treated areas (8.82 and 8.9 t/ha, respectively); however, the input fertilizer was reduced by 108 kg/ha N and increased by 37 kg/ha P.

scholarly journals First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields

2019 ◽  
Vol 11 (23) ◽  
pp. 2769 ◽  
Author(s):  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Jean-Pierre Wigneron ◽  
Mehrez Zribi ◽  
Clément Albergel ◽  
...  
Keyword(s):  
Water Content ◽  
Optical Depth ◽  
Water Loss ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Vegetation Monitoring ◽  
Growth Cycles ◽  
Sar Data

Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km × 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (R2 ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.

Heuristic Approach to Temporal Assignments of Spatial Grid Points for Vegetation Monitoring

2019 ◽  
Vol 10 (3) ◽  
pp. 1-19
Author(s):  
Virginia M. Miori ◽  
Nicolle Clements ◽  
Brian W. Segulin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heuristic Approach ◽  
Spatial Problem ◽  
African Region ◽  
Vegetation Monitoring ◽  
Testing Procedures ◽  
Reward Function ◽  
Grid Points ◽  
False Discoveries

In this research, vegetation trends are studied to give valuable information toward effective land use in the East African region, based on the normalized difference vegetation index (NDVI). Previously, testing procedures controlling the rate of false discoveries were used to detect areas with significant changes based on square regions of land. This article improves the assignment of grid points (pixels) to regions by formulating the spatial problem as a multidimensional temporal assignment problem. Lagrangian relaxation is applied to the problem allowing reformulation as a dynamic programming problem. A recursive heuristic approach with a penalty/reward function for pixel reassignment is proposed. This combined methodology not only controls an overall measure of combined directional false discoveries and nondirectional false discoveries, but make them as powerful as possible by adequately capturing spatial dependency present in the data. A larger number of regions are detected, while maintaining control of the mdFDR under certain assumptions.

scholarly journals Remote Sensing Monitoring and Evaluation of Vegetation Restoration in Grassland Mining Areas—A Case Study of the Shengli Mining Area in Xilinhot City, China

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 743
Author(s):  
Jiawei Hui ◽  
Zhongke Bai ◽  
Baoying Ye ◽  
Zihao Wang
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Monitoring And Evaluation ◽  
Mining Area ◽  
Vegetation Restoration ◽  
Coal Production ◽  
Vegetation Monitoring ◽  
Mining Areas ◽  
Restoration Work

Coal production will cause serious damage to regional vegetation, especially in ecologically fragile grasslands. It is the consensus of all major countries to conduct vegetation restoration and management monitoring in areas damaged by coal production. This paper compares the adaptability of different data sources and different vegetation indices to grassland mining areas and proposes a normalized environmental vegetation index (NEVI) suitable for vegetation monitoring in grassland mining areas. Based on the Landsat and Sentinel data from 2005 to 2019, this paper uses NEVI to monitor the vegetation destruction and restoration of the Shengli mining area. The main result is that the vegetation restoration work in the Shengli mining area started in 2007 and was gradually carried out in subsequent years. The restoration effect of vegetation is significantly better in the east than in the west. The NEVI of the vegetation in the east can reach, or exceed, the level of natural vegetation in the same period. The restoration of vegetation degradation in some areas requires strengthening of management and maintenance measures.

scholarly journals Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1699
Author(s):  
Lingxiao Gu ◽  
Yanmin Shuai ◽  
Congying Shao ◽  
Donghui Xie ◽  
Qingling Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Relative Difference ◽  
Thematic Mapper ◽  
Absolute Difference ◽  
Optical Remote Sensing ◽  
Vegetation Monitoring ◽  
Angle Effect ◽  
Overlapped Region ◽  
Remote Sensing Indices

Optical remote sensing indices play an important role in vegetation information extraction and have been widely serving ecology, agriculture and forestry, urban monitoring, and other communities. Remote sensing indices are constructed from individual bands depending on special characteristics to enhance the typical spectral features for the identification or distinction of surface land covers. With the development of quantitative remote sensing, there is a rapidly increasing requirement for accurate data processing and modeling. It is well known that the geometry-induced variation observed on surface reflectance is not ignorable, but the situation of uncertainty thereby introduced into these indices still needs further detailed understanding. We adopted the ground multi-angle hyperspectrum, spectral response function (SRF) of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), Operational Land Imager (OLI), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multi-Spectral Instrument (MSI) optical sensors and simulated their sensor-like spectral reflectance; then, we investigated the potential angle effect uncertainty on optical indices that have been frequently involved in vegetation monitoring and examined the forward/backward effect over both the ground-based level and the actual Landsat TM/ETM+ overlapped region. Our results on the discussed indices and sensors show the following: (1) Identifiable angle effects exist with a more elevated influence than that introduced by band difference among sensors; (2) The absolute difference of forward and backward direction can reach up to −0.03 to 0.1 within bands of the TM/ETM+ overlapped region; (3) The investigation at ground level indicates that there are different variations of angle effect transmitted to each remote sensing index. Regarding cases of crop canopy at various growth phases, most of the discussed indices have more than a 20% relative difference in nadir value except Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), at less than 10%, and Normalized Burn Ratio (NBR) at less than 16%. For the case of wax maturity stage, the relative difference in nadir value of Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), Char Soil Index (CSI), NBR, Normalized Difference Moisture Index (NDMI), and SWIR2/NIR exceeded 50%, among which the values for NBR and NDMI reached up to 115.8% and 206.7%, respectively; (4) Various schemes of index construction imply different developments of angle effect uncertainty. The “difference” indices can partially suppress the directional influence, while the “ratio” indices show high potential to amplify the angle effect. This study reveals that the angle-induced uncertainty of these indices is greater than that induced by the spectrum mismatch among sensors, especially under the senescence period. In addition, based on this work, indices with a suppressed potential of angle effect are recommended for vegetation monitoring or information retrieval to avoid unexpected effects.

scholarly journals Normalized Difference Vegetation Index in the Integration of Conservation Education

2018 ◽  
Vol 3 (1) ◽  
pp. 47 ◽  
Author(s):  
Ali Rahmat ◽  
Mustofa Abi Hamid ◽  
Muhammad Khoiru Zaki ◽  
Abdul Mutolib
Keyword(s):  
Developing Countries ◽  
Satellite Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Degradation ◽  
Conservation Education ◽  
Global Environment ◽  
Vegetation Monitoring ◽  
June July ◽  
Status Data

Forest plays an important role to support a global environment. Currently, forest degradation occurs in developing countries. Therefore, the excellent strategies to against the forest degradation must be found. One of the best solutions is understanding the information of vegetation condition. Here, the objective of this paper was to apply a method as the assessment of vegetation monitoring using satellite data in the integration of conservation education forest at great forest Wan Abdul Rachman in Lampung Province, Indonesia. In this study, normalized difference vegetation index (NDVI) was used, completed with satellite data (namely MODIS). This technique helps in monitoring vegetation status. Data NDVI from MODIS satellite data showed that forest area decrease very small from 2000-2017. The data was obtained for June, July, and the end of September.

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