USING UAVs/DRONES AND VEGETATION INDICES IN THE VISIBLE SPECTRUM TO MONITORING AGRICULTURAL LANDS

Unmanned Aerial Vehicles UAVs or Drones have made great progress in the field of aerial surveys to study vegetation and farmland. The research focuses on developing smart systems for managing agricultural fields, thus facilitating decision-making, increasing agricultural productivity, improving profitability and protecting the environment. The paper highlights the ability of drones to distinguish agricultural land intended for cultivation and classified as deserted or cultivated or in the germination stage. For the first time in the Nineveh governorate, a Phantom 4 DJI UAV images were used, in addition to using the spatialized Pix4Dfielde program to process these images. Four types of the standard agricultural indices that rely on the visible spectrum have been used (Visible Atmospherically Resistant Index (VARI), Triangular Greenness Index (TGI), Synthetic Normalized Differences Vegetation Index (S-NDVI) and Visible Difference Vegetation Index (VDVI)) to test UAVs images and to categorize different types of agricultural land. The results showed that when using the S-NDVI and VDVI indicators, the values 0.16 and 0.14 appeared respectively in certain areas, which indicates the presence and integrity of vegetation cover, unlike other regions, whose indicators showed 0.010 and -0.004, respectively, which indicate that the plant has a bad condition or its absence at all. All results finding in this research reflect and confirm the validity of using UAVs images for agricultural field management and development.

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Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane

Agronomía Colombiana ◽

10.15446/agron.colomb.v35n1.60852 ◽

2017 ◽

Vol 35 (1) ◽

pp. 82-91

Author(s):

Cesar Edwin García ◽

David Montero ◽

Hector Alberto Chica

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

High Spatial Resolution ◽

Early Stage ◽

Vegetation Indices ◽

Visible Spectrum ◽

Crop Productivity ◽

Spectral Information ◽

Nitrogen Effect ◽

Sugarcane Yield

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

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USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

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

10.5194/isprsarchives-xli-b8-845-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 845-850 ◽

Cited By ~ 2

Author(s):

H. Bendini ◽

I. D. Sanches ◽

T. S. Körting ◽

L. M. G. Fonseca ◽

A. J. B. Luiz ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Agricultural Land Use ◽

Landsat 8 ◽

Land Use Classification ◽

Crop Mapping ◽

Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

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Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

Jurnal Presipitasi Media Komunikasi dan Pengembangan Teknik Lingkungan ◽

10.14710/presipitasi.v17i3.194-204 ◽

2020 ◽

Vol 17 (3) ◽

pp. 194-204

Author(s):

Indarto Indarto ◽

Rufiani Nadzirah ◽

Hadrian Reksa Belagama

Keyword(s):

Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Vegetation Density ◽

Residential Areas ◽

Primary Input ◽

Normalised Difference Vegetation Index ◽

Sentinel 2A ◽

Marginal Agricultural Land ◽

Potential Use

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values = 66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

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Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

Remote Sensing ◽

10.3390/rs12182970 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2970

Author(s):

Anna C. Talucci ◽

Elena Forbath ◽

Heather Kropp ◽

Heather D. Alexander ◽

Jennie DeMarco ◽

...

Keyword(s):

Boreal Forests ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Visible Spectrum ◽

Basal Area ◽

Vegetation Recovery ◽

Climate Conditions ◽

Northeastern Siberia ◽

Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

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Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

Remote Sensing ◽

10.3390/rs12071207 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1207 ◽

Cited By ~ 1

Author(s):

Jian Zhang ◽

Chufeng Wang ◽

Chenghai Yang ◽

Tianjin Xie ◽

Zhao Jiang ◽

...

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Crop Growth ◽

Growth Monitoring ◽

Multispectral Images ◽

Area Index ◽

Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

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Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

Remote Sensing ◽

10.3390/rs13040568 ◽

2021 ◽

Vol 13 (4) ◽

pp. 568

Author(s):

David Andrés Rivas-Tabares ◽

Antonio Saa-Requejo ◽

Juan José Martín-Sotoca ◽

Ana María Tarquis

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Precipitation Pattern ◽

Scaling Properties ◽

Vegetation Growth ◽

Factors Affecting ◽

Series Analysis ◽

Climate Interactions

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

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USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

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

10.5194/isprs-archives-xli-b8-845-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 845-850

Author(s):

H. Bendini ◽

I. D. Sanches ◽

T. S. Körting ◽

L. M. G. Fonseca ◽

A. J. B. Luiz ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Agricultural Land Use ◽

Landsat 8 ◽

Land Use Classification ◽

Crop Mapping ◽

Phenological Metrics

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Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

Plants ◽

10.3390/plants10122726 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2726

Author(s):

Yaping Xu ◽

Vivek Shrestha ◽

Cristiano Piasecki ◽

Benjamin Wolfe ◽

Lance Hamilton ◽

...

Keyword(s):

Remote Sensing ◽

Data Collection ◽

High Throughput ◽

Panicum Virgatum ◽

Vegetation Index ◽

Lignin Content ◽

Vegetation Indices ◽

Rust Disease ◽

Bioenergy Feedstock ◽

First Time

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

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TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w4-279-2017 ◽

2017 ◽

Vol IV-4/W4 ◽

pp. 279-283 ◽

Cited By ~ 3

Author(s):

A. Karakacan Kuzucu ◽

F. Bektas Balcik

Keyword(s):

Land Use ◽

Support Vector Machine ◽

Maximum Likelihood ◽

Rural Areas ◽

Vegetation Index ◽

Vegetation Indices ◽

Support Vector ◽

Kappa Statistics ◽

Suitability Analysis ◽

Agricultural Field

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

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COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

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

10.5194/isprs-archives-xlii-3-1381-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1381-1387 ◽

Cited By ~ 1

Author(s):

M. Piragnolo ◽

G. Lusiani ◽

F. Pirotti

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Monitoring And Control ◽

Water Index ◽

Normalized Difference Water Index ◽

Remote Sensing Techniques ◽

Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

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