scholarly journals Aedes aegypti and Aedes albopictus abundance, landscape coverage and spectral indices effects in a subtropical city of Argentina

2022 ◽  
Author(s):  
Mia Elisa Martin ◽  
Ana Carolina Alonso ◽  
Janinna Faraone ◽  
Marina Stein ◽  
Elizabet L Estallo
Keyword(s):  
Aedes Aegypti ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Spectral Indices ◽  
Remote Sensors ◽  
Natural Park ◽  
Ndvi Index ◽  
Outdoor Environments ◽  
Cover Class ◽  
Abundance And Distribution

The presence, abundance and distribution of Aedes (Stegomyia) aegypti (Linnaeus 1762) and Aedes (Stegomyia) albopictus (Skuse 1894) could be conditioned by different data obtained from satellite remote sensors. In this paper, we aim to estimate the effect of landscape coverage and spectral indices on the abundance of Ae. aegypti and Ae. albopictus from the use of satellite remote sensors in Eldorado, Misiones, Argentina. Larvae of Aedes aegypti and Ae. albopictus were collected monthly from June 2016 to April 2018, in four outdoor environments: tire repair shops, cemeteries, family dwellings, and an urban natural park. The proportion of each land cover class was determined by Sentinel-2 image classification. Furthermore spectral indices were calculated. Generalized Linear Mixed Models were developed to analyze the possible effects of landscape coverage and vegetation indices on the abundance of mosquitoes. The model's results showed the abundance of Ae. aegypti was better modeled by the minimum values of the NDVI index, the maximum values of the NDBI index and the interaction between both variables. In contrast, the abundance of Ae. albopictus has to be better explained by the model that includes the variables bare soil, low vegetation and the interaction between both variables.

Use of water indices in the determination of thermokarst lakes according to remote sensing data

2021 ◽  
pp. 67-74
Author(s):  
Artem Pshenichnikov
Keyword(s):  
Visual Analysis ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Identical Result ◽  
Spectral Indices ◽  
Quadratic Error ◽  
Thermokarst Lakes ◽  
Ndvi Index ◽  
Water Indices

The results of application of six spectral indices (AWEI, MNDWI, NDVI, NDWI, TCW, WRI) for the isolation of thermokarst lakes in tundra landscapes of northern Yakutia are presented. To assess the accuracy of decryption of lakes, an average quadratic error (MSE) was calculated. The minimum MSE value is 0.11 km2 and corresponds to the NDWI index. An almost identical result (0.12 km2) is found in the WRI index, slightly worse (0.15 km2) one — in the NDVI index. An MNDWI index has the highest mean square error (7.02 km2). Visual analysis also showed better decryption of water bodies using the NDWI, WRI and NDVI indices, which allows the use of these indices for automatical isolatation water bodies.

scholarly journals Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

2020 ◽  
Vol 10 (16) ◽  
pp. 5540 ◽  
Author(s):  
Maria Casamitjana ◽  
Maria C. Torres-Madroñero ◽  
Jaime Bernal-Riobo ◽  
Diego Varga
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Multispectral Images ◽  
Surface Soil Moisture ◽  
Mountain Environments ◽  
Tropical Mountain ◽  
Moisture Analysis

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

scholarly journals Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

2020 ◽  
pp. 31
Author(s):  
M. P. Martín ◽  
J. Pacheco-Labrador ◽  
R. González-Cascón ◽  
G. Moreno ◽  
M. Migliavacca ◽  
...  
Keyword(s):  
Structural Characteristics ◽  
Vegetation Indices ◽  
Matter Content ◽  
Tree Cover ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Area Index ◽  
Remote Sensors ◽  
Spectral Bands ◽  
The Relationship

<p>Mixed vegetation systems such as wood pastures and shrubby pastures are vital for extensive and sustainable livestock production as well as for the conservation of biodiversity and provision of ecosystem services, and are mostly located in areas that are expected to be more strongly affected by climate change. However, the structural characteristics, phenology, and the optical properties of the vegetation in these mixed -ecosystems such as savanna-like ecosystems in the Iberian Peninsula which combines herbaceous and/or shrubby understory with a low density tree cover, constitute a serious challenge for the remote sensing studies. This work combines physical and empirical methods to improve the estimation of essential vegetation variables: leaf area index (<em>LAI</em>, m<sup>2</sup> / m<sup>2</sup> ), leaf (C<sub>ab,leaf</sub>, μg / cm<sup>2</sup> ) and canopy(C<sub>ab,canopy</sub>, g / m<sup>2 </sup>) chlorophyll content, and leaf (C<sub>m, leaf</sub>, g / cm<sup>2</sup> ) and canopy (C<sub>m,canopy</sub>, g / m<sup>2</sup> ) dry matter content in a dehesa ecosystem. For this purpose, a spectral simulated database for the four main phenological stages of the highly dynamic herbaceous layer (summer senescence, autumn regrowth, greenness peak and beginning of senescence), was built by coupling PROSAIL and FLIGHT radiative transfer models. This database was used to calibrate different predictive models based on vegetation indices (VI) proposed in the literature which combine different spectral bands; as well as Partial Least Squares Regression (PLSR) using all bands in the simulated spectral range (400-2500 nm). PLSR models offered greater predictive power (<em>R<sup>2</sup></em> ≥ 0.93, <em>RRMSE</em> ≤ 10.77 %) both for the leaf and canopy- level variables. The results suggest that directional and geometric effects control the relationships between simulated reflectance factors and the foliar parameters. High seasonal variability is observed in the relationship between biophysical variables and IVs, especially for <em>LAI</em> and <em>C<sub>ab</sub></em>, which is confirmed in the PLSR analysis. The models developed need to be validated with spectral data obtained either with proximal or remote sensors.</p>

scholarly journals Time Series Analysis Using Digital Classification of Data on Land Use and Land Cover in Dohuk Governorate

2021 ◽  
Vol 910 (1) ◽  
pp. 012124
Author(s):  
Mohammed Younis Salim ◽  
Narmin Abduljaleel Ibrahim
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Urban Areas ◽  
Bare Soil ◽  
Error Matrix ◽  
Ndvi Index ◽  
Open Forests ◽  
Soil And Water

Abstract This study deals with the analysis and detection of changes in land cover patterns and land uses, especially forests in Amadiya district in Dohuk Governorate. It carred out in northern of Iraq by area is (2775.21) km2 and the district is located astronomically between longitudes (01/04 ° 43), (17/08 ° 44), it extends between two circles of latitude, which are (16/50 ° 36) and ('30.'21 ° 37) north, during the periods (1999-2006-2013-2019). Application of the Supervised Classification and the detection of change over time in a comparative manner and by relying on the satellite images of the Land sat ETM satellite were used. The Landsat OLI satellite with a distinctive capacity of 30 meters in the Arc map 10.6.1 program, and one of the indicators of environmental degradation in the land cover patterns, which is the NDVI index for all study periods, was used to reveal the role of natural and human factors that lead to changes in the land cover patterns in the study area. The classification revealed the existence of five types of common land cover, which included dense forests, open forests, urban areas, bare soil and water, which showed clear changes in these land coverings during the period from 1999 to 2019, which were represented by a decrease in forests, bare soil and water by a percentage of (54.76601%), (5.212329%), (2.149469%) respectively, while the Dense and urban areas by (16.35919%) and (21.51301%) in 2019, respectively. The classification accuracy of the Spatial indication was estimated based on the error matrix from there we found that the accuracy was (93.29%) this indicates that the classification accuracy is very good It is acceptable and can relied upon and recommended for classification.

scholarly journals Replacing the Red Band with the Red-SWIR Band (0.74ρred+0.26ρswir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background

2019 ◽  
Vol 11 (7) ◽  
pp. 851 ◽  
Author(s):  
Xuehong Chen ◽  
Zhengfei Guo ◽  
Jin Chen ◽  
Wei Yang ◽  
Yanming Yao ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Color Variation ◽  
Vegetation Coverage ◽  
Large Area ◽  
Area Index ◽  
Soil Line ◽  
Swir Band

Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.

scholarly journals Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

2019 ◽  
Vol 11 (24) ◽  
pp. 2925 ◽  
Author(s):  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Danilo Roberto Pereira ◽  
Érika Akemi Saito Moriya ◽  
Nilton Nobuhiro Imai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nitrogen Content ◽  
Learning Algorithms ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Spectral Vegetation Indices ◽  
Canopy Nitrogen

The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

scholarly journals Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2675 ◽  
Author(s):  
Linlin Zhang ◽  
Qingyan Meng ◽  
Shun Yao ◽  
Qiao Wang ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Optical Data ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Backscattering Coefficient ◽  
Vegetation Water

Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

scholarly journals Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices

2021 ◽  
Vol 13 (11) ◽  
pp. 2036
Author(s):  
Elio Romano ◽  
Simone Bergonzoli ◽  
Ivano Pecorella ◽  
Carlo Bisaglia ◽  
Pasquale De Vita
Keyword(s):  
Durum Wheat ◽  
Precision Agriculture ◽  
Survey Methods ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Spectral Indices ◽  
Entire Study ◽  
Growing Seasons ◽  
Use Of Time ◽  
Occurrence Matrix

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.

scholarly journals Uso y selección de microhábitat en un ensamble de anuros del Chaco Serrano de Argentina

2020 ◽  
Vol 68 (3) ◽  
Author(s):  
Rodrigo A. Nieva Cocilio ◽  
Juan C. Acosta ◽  
Graciela M. Blanco
Keyword(s):  
Seasonal Variations ◽  
Linear Models ◽  
Bare Soil ◽  
Temporal Variations ◽  
Microhabitat Use ◽  
Amphibian Species ◽  
Natural Park ◽  
Chaco Serrano ◽  
Species Overlap ◽  
Species Activity

Introduction: Research on spatial resource usage and partition strategies is important to understand the mechanisms of coexistence in sympatric amphibian species, even more when there are temporal variations in habitat availability. Objective: To learn about the patterns of microhabitat use, its seasonal variations and the possible influence of phylogeny on an anuran assembly of the Chaco Serrano in Argentina. Methods: Samplings were carried out in the Valle Fértil Natural Park protected area, Valle Fértil Department, San Juan, between 2012 and 2017. In the field, we recorded the microhabitat where each specimen was found, and we also measured site variables. In addition, microhabitats availability was determined. Data were analyzed using Manly’s selectivity index. Generalized linear models (GLM) were used to assess temporal variations in microhabitat use. Results: The species evaluated were: Rhinella arenarum arenarum, Leptodactylus latrans, Pleurodema tucumanum and Odontophrynus occidentalis. All species showed differences in usage frequencies: R. a. arenarum showed preferences for rocky and aquatic sites, P. tucumanum showed preferences towards bare soil and rocky sites, L. latrans and O. occidentalis showed greater preferences for rocky and aquatic sites. All species but Odontophrynus exhibited seasonal variations in microhabitat selection and usage. Odontophrynus occidentalis showed differences in usage proportions among microhabitats. Conclusions: This study shows plasticity in microhabitat usage as an important determinant of anuran spatial distribution, without apparent restrictions imposed by space competition or phylogeny. When species activity is coincident, the space resource may be distributed in a way that species overlap is reduced.

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