scholarly journals UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

2021 ◽  
Vol 13 (11) ◽  
pp. 2139
Author(s):  
Ana I. de Castro ◽  
Yeyin Shi ◽  
Joe Mari Maja ◽  
Jose M. Peña
Keyword(s):  
Vegetation Indices ◽  
Canopy Cover ◽  
Nutrient Content ◽  
Environmental Benefits ◽  
Efficient Production ◽  
Vegetation Monitoring ◽  
Flight Operations ◽  
General Goal ◽  
Forestry Resources ◽  
Plant Crop

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits.

scholarly journals QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 952
Author(s):  
Lia Duarte ◽  
Ana Cláudia Teodoro ◽  
Joaquim J. Sousa ◽  
Luís Pádua
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Data Interpretation ◽  
Vegetation Monitoring ◽  
Distribution Maps ◽  
Monitoring Process ◽  
Multi Temporal ◽  
The Creation ◽  
The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

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 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 Expansion of photovoltaic systems in multicampi higher education institutions: evaluation and guidelines

2021 ◽  
Vol 56 (4) ◽  
pp. 697-709
Author(s):  
Osvaldo Augusto Vasconcelos de Oliveira Lopes Da Silva ◽  
José Machado Moita Neto ◽  
Marcos Antônio Tavares Lira ◽  
Fabrício Higo Monturil de Morais
Keyword(s):  
Higher Education ◽  
Higher Education Institutions ◽  
Environmental Benefits ◽  
Pv System ◽  
Pv Systems ◽  
Performance Indexes ◽  
Federal Institute ◽  
General Goal ◽  
Solar Resource ◽  
Education Science

Considering the multicampi organizational structure of higher education institutions (HEIs), the expansion of photovoltaic (PV) systems previously installed in the facilities, the great potential for PV generation in Brazil, and the 2030 Agenda, the general goal of this research study is to evaluate and promote the expansion of the aforementioned PV systems. For this purpose, the PV system installed at the Federal Institute of Education, Science and Technology of Piauí comprising a future expansion is characterized by a thorough literature and documentary research. The solar resource available at the campuses of the institution was estimated using the second version of the Brazilian Atlas of Solar Energy. The technical–economic viability of the system expansion is assessed through the average parameters and minimum performance indexes required by the institution. Thus, it is possible to prove the effectiveness of the methodology to identify investment priorities and guide the construction and expansion of other PV systems, confirming that this process is technically and economically feasible as associated with strategic adherence, also bringing several environmental benefits.

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 Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation

2020 ◽  
Vol 12 (17) ◽  
pp. 2696 ◽  
Author(s):  
Martyna Wakulińska ◽  
Adriana Marcinkowska-Ochtyra
Keyword(s):  
National Parks ◽  
Vegetation Indices ◽  
European Space Agency ◽  
Principal Component ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Vegetation Monitoring ◽  
Multi Temporal ◽  
Mountain Vegetation ◽  
Sentinel 2

The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks.

scholarly journals Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles

Agriculture ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 277 ◽  
Author(s):  
Héctor García-Martínez ◽  
Héctor Flores-Magdaleno ◽  
Roberto Ascencio-Hernández ◽  
Abdul Khalil-Gardezi ◽  
Leonardo Tijerina-Chávez ◽  
...  
Keyword(s):  
Neural Network ◽  
Grain Yield ◽  
Correlation Coefficient ◽  
Plant Density ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Relative Importance ◽  
Yield Estimation ◽  
Corn Grain ◽  
Corn Grain Yield

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

scholarly journals Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

2021 ◽  
Vol 27 (1) ◽  
pp. 50-58
Author(s):  
M B Saleh ◽  
◽  
R W Dewi ◽  
L B Prasetyo ◽  
N A Santi
Keyword(s):  
Dimensional Space ◽  
Vegetation Indices ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Estimation Model ◽  
Large Area ◽  
Lowland Forest ◽  
Three Dimensional Space

Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.

scholarly journals SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

2021 ◽  
Vol 70 (1-2) ◽  
pp. 67-75
Author(s):  
Polina Lemenkova
Keyword(s):  
Vegetation Indices ◽  
Landsat Tm ◽  
Coastal Vegetation ◽  
Atmospheric Effects ◽  
Vegetation Monitoring ◽  
Vegetation Greenness ◽  
Landsat Tm Image ◽  
Harsh Climate ◽  
Water Values ◽  
River Valleys

Summary The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from -112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region.

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