scholarly journals ULTRA-HIGH SPATIAL RESOLUTION UAV-BASED IMAGERY TO PREDICT BIOMASS IN TEMPERATE GRASSLANDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 443-447 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
J. Menne ◽  
M. L. Gnyp ◽  
G. Bareth
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Low Cost ◽  
Rapid Assessment ◽  
Vegetation Indices ◽  
Multispectral Data ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Cost Efficient

<p><strong>Abstract.</strong> Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R<sup>2</sup> of 0.57 and 0.63 respectively, while the PPR performs better with an R<sup>2</sup> of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.</p>

scholarly journals A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

2018 ◽  
Vol 37 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Khalid Mahmood ◽  
Zia Ul-Haq ◽  
Fiza Faizi ◽  
Syeda A. Batol
Keyword(s):  
Hazard Assessment ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
Zone Size ◽  
Vegetation Health ◽  
Spatio Temporal ◽  
Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

Evaluation of a Low-cost Camera for Agricultural Applications

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdon Francisco Aureliano Netto ◽  
Rodrigo Nogueira Martins ◽  
Guilherme Silverio Aquino De Souza ◽  
Fernando Ferreira Lima Dos Santos ◽  
Jorge Tadeu Fim Rosas
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Paspalum Notatum ◽  
Crop Canopy ◽  
N Application ◽  
Satisfactory Accuracy ◽  
Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

scholarly journals Digital aerial images land cover classification based on vegetation indices

2012 ◽  
Vol 31 (3) ◽  
pp. 5-23
Author(s):  
Maciej Dzieszko ◽  
Piotr Dzieszko ◽  
Sławomir Królewicz
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Aerial Images ◽  
Geographical Information ◽  
Wide Availability

Abstract . Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.

scholarly journals Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 80
Author(s):  
Milton Valencia-Ortiz ◽  
Worasit Sangjan ◽  
Michael Gomez Selvaraj ◽  
Rebecca J. McGee ◽  
Sindhuja Sankaran
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
Vegetation Indices ◽  
Crop Breeding ◽  
Multispectral Data ◽  
Solar Reflectance ◽  
Aerial Vehicle ◽  
Simple Ratio ◽  
Relative Differences

Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.

scholarly journals UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

2020 ◽  
Vol 2 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Luis Fernando Sánchez-Sastre ◽  
Mª Auxiliadora Casterad ◽  
Mónica Guillén ◽  
Norlan Miguel Ruiz-Potosme ◽  
Nuno M. S. Alte da Veiga ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Digital Cameras ◽  
Site Specific ◽  
Aerial Vehicles ◽  
Ndvi Index ◽  
Weed Cover

Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.

scholarly journals Spatio-temporal pattern of urban forest vegetation density, Medan Baru city, Indonesia

2021 ◽  
Vol 918 (1) ◽  
pp. 012021
Author(s):  
Samsuri ◽  
C A B Ginting ◽  
A Zaitunah ◽  
A Susilowati
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Forest ◽  
Vegetation Indices ◽  
Stand Density ◽  
Development Planning ◽  
Vegetation Density ◽  
Dense Vegetation ◽  
Spatio Temporal

Abstract The growth of urban areas and the population generally requires the guarantee of a healthy and comfortable environment. The expansion of physical developments and urban areas, year after year, can no longer support human existence. In Indonesia, the city should have at least 10% of its surface area committed to private urban forest and 20% for public urban forest. Jakarta is Indonesia’s largest city, and has only 9.98% urban forest coverage. Medan Baru city is facing the same issue, as it continues to grow year after year. The population requires a comfortable environment, which includes safe drinking water and clean, fresh air. As a result, vegetation is an important component of Medan Baru sub-district that offers numerous benefits. It is necessary to conduct research on the analysis of vegetation density in the Medan Baru, using vegetation indices such as Normalized Difference Vegetation Index (NDVI). The research aimed to analyze vegetation density change and mapping the vegetation density of Medan Baru city. The research found the largest area was relatively dense vegetation, about 262.00 hectares (47.87%). The research also found a decrease in urban forest quality, indicated by an increase in the sparse density class of 41.90 hectares and a decrease in the relative-dense vegetation class with 51.65 hectares. This reduction of vegetation density will reduce the urban forest quality by influencing urban forest capability in absorbing carbon dioxide and alleviating the oxygen productivity volume. Areas with lower stand density must be considered in future urban development planning. Moreover, decrease in vegetation density and urban forest area should be a primary consideration in Medan urban forest management.

scholarly journals A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan

2020 ◽  
Vol 12 (3) ◽  
pp. 580
Author(s):  
Muhammad Usman ◽  
Janet E. Nichol
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Variability ◽  
Vegetation Indices ◽  
Biomass Productivity ◽  
Temporal Analysis ◽  
Annual Rainfall ◽  
Spatial Estimates ◽  
Spatio Temporal ◽  
Rainfall Estimates

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

scholarly journals ASSESSING CROP MONITORING POTENTIAL OF SENTINEL-2 IN A SPATIO-TEMPORAL SCALE

2018 ◽  
Vol XLII-5 ◽  
pp. 227-231 ◽  
Author(s):  
P. Ghosh ◽  
D. Mandal ◽  
A. Bhattacharya ◽  
M. K. Nanda ◽  
S. Bera
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Crop Monitoring ◽  
Red Edge ◽  
Plant Area Index ◽  
Spatio Temporal ◽  
Sentinel 2

<p><strong>Abstract.</strong> Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in ‘red-edge’ position (i.e., 705<span class="thinspace"></span>nm, 740<span class="thinspace"></span>nm, 783<span class="thinspace"></span>nm) in Sentinel-2 with 10&amp;ndash;20<span class="thinspace"></span>m spatial resolution images with five days revisit period have unfolded opportunity for meticulous crop monitoring. In the present study, the potential of Sentinel-2 have been appraised for monitoring phenological stages of potato over Bardhaman district in the state of West Bengal, India. Due to the competency of Vegetation indices (VI) to evaluate the status of crop growth; we have used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Index45 (NDI45) for crop monitoring. Time series analysis of the VIs exhibited increasing trend as the crop started approaching maturity and attained a maximum value during the tuber development stage and started decreasing as the crop advances to senescence. Inter-field variability of VIs highlighted the need of crop monitoring at high spatial resolution. Among the three vegetation indices, the GNDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.636), NDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.620) had the highest correlation with biomass and Plant Area Index (PAI), respectively. NDI45 had comparatively a lower correlation (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.572 and 0.585 for PAI and biomass, respectively) with both parameters as compared to other two indices. It is interesting to note that the use of Sentinel-2 Green band (B3) instead of the Red band (B4) in GNDVI resulted in 2.5% increase of correlation with biomass. However, the improvement in correlations between NDI45 with crop biophysical parameters is not apparent in this particular study with the inclusion of the Vegetation Red Edge band (B5) in VI. Nevertheless, the strong correlation of VIs with biomass and PAI asserted proficiency of Sentinel-2 for crop monitoring and potential for crop biophysical parameter retrieval with optimum accuracy.</p>

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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