Sensor system for acquisition of vegetation indexes

ABSTRACT Vegetation indexes are important indicators of the health and yield of agricultural crops. Among the sensors used to evaluate vegetation indexes, proximal sensors can be used for real-time decision-making. Thus, the objective of this study was to develop a proximal sensor system based on phototransistors to acquire and store the following vegetation indexes: normalized difference vegetation index, simple ratio, wide dynamic range vegetation index, soil-adjusted vegetation index, and optimized soil-adjusted vegetation index. The sensor system was developed using an analog circuit to acquire reflectance data from red and near-infrared bands. The sensor system was calibrated according to the results of a spectroradiometer, using Zoysia japonica grass as the target. An algorithm that calculates and stores vegetation indexes in a file was developed. The Pearson correlation between the vegetation indexes obtained with the sensor system and the spectroradiometer was evaluated. The vegetation indexes presented a Pearson correlation higher than 0.92 to the estimated values by the spectroradiometer. Under the evaluation conditions, the proposed sensor system could be used to determine all vegetation indexes evaluated.

Download Full-text

Vegetation index cartography as a methodology complement to the terroir zoning for its use in precision viticulture

OENO One ◽

10.20870/oeno-one.2017.51.4.1589 ◽

2017 ◽

Vol 51 (3) ◽

pp. 289 ◽

Cited By ~ 3

Author(s):

Alvaro Martínez ◽

Vicente D Gomez-Miguel

Keyword(s):

Environmental Factors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Alluvial Soil ◽

Soil Features ◽

Vegetation Indexes ◽

Soil Elevation ◽

Precision Viticulture ◽

Simple Ratio ◽

Terrain Features

Aim: Precision Viticulture (PV) is a form of vineyard management based on tools that offer winegrowers georeferenced information of each vineyard, mainly sector mapping (sub-areas) differentiated by characteristics capable of influencing vineyard usage. This provides knowledge of the variations in these sectors and PV treats each one of them in an independent and optimised manner. This allows, amongst many other possibilities, to monitor fruit ripening with the objective of performing site-specific harvest based on the characteristics of each given sector. Local variations in soil features and natural environmental factors, such as climate, lithology, geomorphology and soil, determine the units that drive or limit PV.Methods and results: In this paper, multispectral images are used. These have been obtained between veraison and harvest in three different years in order to calculate four vegetation indexes (VI) that have been used since the end of the last century to delimit homogenous sectors in vineyards: the Normalized Difference Vegetation Index (NDVI), the Improved Soil Adjusted Vegetation Index (MSAVI), the Simple Ratio Index (SR) and the Modified Simple Ratio Index (MSR). Mapping of these VI has allowed to relate their distribution with natural environmental factors with the objective of valuing their use in the discrimination of homogenous sectors as a complement and/or alternative to traditional methodologies to terroir zoning. Results show that, in the area studied, the vineyards planted in alluvial soil and conglomerated zones, over dominant fine-loamy, mixed, mesic, Calcixerollic Xerochrept soil series, at elevations between 519 and 604 m, oriented east and on slopes less than 5º present higher values for all four indexes throughout the three years of study.Conclusions: It is precisely these environmental elements (lithology, soil, elevation, orientation and slope) and many soil features that must be relatively uniform in order to make an efficient use of the studied VI.Significance and impact of the study: The study addresses the use of VI as a companion tool to viticultural zoning, which has not been much explored at such scale level. In addition, the results obtained may lead to changes in the use of VI, which are usually used without taking into account soil and/or terrain features.

Download Full-text

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-141-2017 ◽

2017 ◽

Vol 24 (2) ◽

pp. 141-155 ◽

Cited By ~ 6

Author(s):

Carmelo Alonso ◽

Ana M. Tarquis ◽

Ignacio Zúñiga ◽

Rosa M. Benito

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Fractal Analysis ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Root Zone ◽

Geographic Location ◽

Image Resolution ◽

Vegetation Indexes

Abstract. Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Download Full-text

Desert camouflage and what wildlife see

NIR news ◽

10.1177/0960336017738177 ◽

2017 ◽

Vol 28 (8) ◽

pp. 4-10

Author(s):

Wendy W Kuhne ◽

Martine C Duff ◽

Katie Salvaggio ◽

Nancy V Halverson ◽

Ronald Staggs

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Visual Observation ◽

Infrared Measurements ◽

Blue Wavelength ◽

Blue Range ◽

Simple Ratio ◽

Camouflage Patterns

Desert, desert-scrub, savanna and sandy beach, and lakeshore environments can be particularly tricky in terms of camouflage selection due to their low vegetative density. Therefore many companies focus on the development of paint color schemes that match the vegetation and the desert soils/sands. However another factor in the consideration of which camouflage to purchase may lie in what the animal can see. White-tailed deer and similar large mammals have been shown to have three classes of photo pigments that are sensitive to the range of blue to yellow-green during day light hours and blue to blue-green at night. Six commercially-available camouflage patterns were investigated to determine if the reflectance characteristics measured in the laboratory and under field conditions were elevated in the blue range and perhaps more likely to be seen by wildlife. The camouflage patterns were evaluated against standard vegetation indices including normalized difference vegetation index, soil adjusted vegetation index, enhanced vegetation index, and simple ratio. Only two of the patterns (S4 and S5) possessed a reflectance more like vegetation. Patterns S4, S6, S3, and S2 all showed only slight elevations in the blue wavelength range which could only have been detected by near infrared measurements instead of visual observation by the human eye.

Download Full-text

Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Remote Sensing ◽

10.3390/rs13050956 ◽

2021 ◽

Vol 13 (5) ◽

pp. 956

Author(s):

Florian Mouret ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Denis Kouamé ◽

Guillaume Rieu ◽

...

Keyword(s):

Outlier Detection ◽

Experimental Validation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multispectral Images ◽

Vegetation Indexes ◽

Crop Development ◽

Different Types ◽

Selection Of ◽

Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

Download Full-text

A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030129 ◽

2021 ◽

Vol 10 (3) ◽

pp. 129

Author(s):

Vincent Nzabarinda ◽

Anming Bao ◽

Wenqiang Xu ◽

Solange Uwamahoro ◽

Madeleine Udahogora ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Forest Type ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Plant Health ◽

Tree Cover ◽

Climatic Zones ◽

Dramatic Decline ◽

Precipitation Changes

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

Download Full-text

Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

Mathematical Problems in Engineering ◽

10.1155/2017/5209173 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Siqin Tong ◽

Yuhai Bao ◽

Rigele Te ◽

Qiyun Ma ◽

Si Ha ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pearson Correlation ◽

Growing Season ◽

Vegetation Coverage ◽

Semiarid Region ◽

Linear Regression Method ◽

Upward Trend ◽

Drought Effect ◽

Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

Download Full-text

Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽

10.1614/ws-05-54.2.346 ◽

2006 ◽

Vol 54 (02) ◽

pp. 346-353 ◽

Cited By ~ 41

Author(s):

Francisca López-Granados ◽

Montse Jurado-Expósito ◽

Jose M. Peña-Barragán ◽

Luis García-Torres

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Research ◽

Aerial Images ◽

Wild Oat ◽

Late Season ◽

High Resolution Satellite Imagery ◽

Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

Download Full-text

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Remote Sensing ◽

10.3390/rs10081293 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1293 ◽

Cited By ~ 26

Author(s):

Yunpeng Luo ◽

Tarek S. El-Madany ◽

Gianluca Filippa ◽

Xuanlong Ma ◽

Bernhard Ahrens ◽

...

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Plant Phenology ◽

Mediterranean Ecosystem ◽

Near Infrared Reflectance ◽

Seasonally Dry ◽

Growing Season Length ◽

Dry Down

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

Download Full-text

Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040257 ◽

2020 ◽

Vol 9 (4) ◽

pp. 257 ◽

Cited By ~ 3

Author(s):

Kiwon Lee ◽

Kwangseob Kim ◽

Sun-Gu Lee ◽

Yongseung Kim

Keyword(s):

Open Source ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Atmospheric Correction ◽

Urban Forestry ◽

Google Earth ◽

Landsat 8 ◽

Surface Reflectance ◽

Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

Download Full-text

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

Remote Sensing ◽

10.3390/rs11060724 ◽

2019 ◽

Vol 11 (6) ◽

pp. 724 ◽

Cited By ~ 13

Author(s):

Simon Measho ◽

Baozhang Chen ◽

Yongyut Trisurat ◽

Petri Pellikka ◽

Lifeng Guo ◽

...

Keyword(s):

Climate Variability ◽

Vegetation Dynamics ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Negative Impact ◽

Pearson Correlation ◽

Temporal Analysis ◽

Semiarid Region ◽

Ecological Zones ◽

Spatio Temporal

There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

Download Full-text