scholarly journals Research of tools for monitoring changes in natural and anthropogenic-transformed ecosystems

2021 ◽  
Vol 937 (2) ◽  
pp. 022047
Author(s):  
A Belyaev ◽  
S Kramarov ◽  
O Mityasova ◽  
O Popov ◽  
V Khramov
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Open Data ◽  
Satellite Monitoring ◽  
Additional Tool ◽  
Crop Monitoring ◽  
Accurate Definition ◽  
Agricultural Use ◽  
Definition Of

Abstract Decarbonization issues are one of the main strategic directions of modern environmental development today. New technologies of agricultural use of soils are used to fix carbon in the soil in the form of humus, which ultimately helps to reduce the greenhouse effect and actively affects the amount of carbon entering the atmosphere. The use of open data of remote sensing of the Earth from space (further - RSE) together with the data of satellite monitoring of the Normalized Difference Vegetation Index (NDVI) can allow us to obtain new methods of carbonation analysis. In this paper, we consider the possibilities of such use of standard NDVI data together with a more accurate definition of specific boundaries of agricultural fields in order to increase the accuracy of research results. This article shows the results of processing data and images obtained using open crop monitoring data. The proposed technology is proposed by us as an additional tool for monitoring changes in the ecosystems of the regions.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

2019 ◽  
Vol 11 (19) ◽  
pp. 2228 ◽  
Author(s):  
Ali Nasrallah ◽  
Nicolas Baghdadi ◽  
Mohammad El Hajj ◽  
Talal Darwish ◽  
Hatem Belhouchette ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Weather Conditions ◽  
Crop Monitoring ◽  
Temporal Series ◽  
Automated Method ◽  
Low Incidence ◽  
Phenological Phases

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

scholarly journals Seasonal Variation of Surface Temperature–Modulating Factors in the Sonoran Desert in Northwestern Mexico

2012 ◽  
Vol 51 (8) ◽  
pp. 1519-1530 ◽  
Author(s):  
Iryna Tereshchenko ◽  
Alexander N. Zolotokrylin ◽  
Tatiana B. Titkova ◽  
Luis Brito-Castillo ◽  
Cesar Octavio Monzon
Keyword(s):  
Surface Temperature ◽  
Sonoran Desert ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Threshold Value ◽  
Radiation Mechanism ◽  
Modis Ndvi ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Northwestern Mexico ◽  
Definition Of

AbstractThe authors explore a new approach to monitoring of desertification that is based on use of results on the relation between albedo and surface temperature for the Sonoran Desert in northwestern Mexico. The criteria of predominance of radiation by using the threshold value of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were determined. The radiation mechanism for regulating the temperature of the surface and the definition of threshold values for AVHRR and MODIS NDVI have an objective justification for the energy budget, which is based on the dominance of radiation surface temperature regulation in relation to evapotranspiration. Changes in the extent of arid regions with AVHRR NDVI of <0.08 and MODIS NDVI of <0.10 can be considered to be a characteristic in the evolution of desertification in the Sonoran Desert region. This is true because, in a certain year, the time span of the period when radiation factor predominates is important for the desertification process.

scholarly journals USE OF GREENDRONE UAS SYSTEM FOR MAIZE CROP MONITORING

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 263-268 ◽  
Author(s):  
A. K. Nasir ◽  
M. Tharani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Research Work ◽  
Aerial Images ◽  
Stress Index ◽  
Maize Crop ◽  
Crop Monitoring ◽  
Water Stress Index ◽  
Long Endurance

This research work presents the use of a low-cost Unmanned Aerial System (UAS) – GreenDrone for the monitoring of Maize crop. GreenDrone consist of a long endurance fixed wing air-frame equipped with a modified Canon camera for the calculation of Normalized Difference Vegetation Index (NDVI) and FLIR thermal camera for Water Stress Index (WSI) calculations. Several flights were conducted over the study site in order to acquire data during different phases of the crop growth. By the calculation of NDVI and NGB images we were able to identify areas with potential low yield, spatial variability in the plant counts, and irregularities in nitrogen application and water application related issues. Furthermore, some parameters which are important for the acquisition of good aerial images in order to create quality Orthomosaic image are also discussed.

scholarly journals Multi-criteria analysis for mapping susceptibility to iron formation caves development in the Gandarela mountain range (MG), southeast Brazil

2021 ◽  
Vol 50 (2) ◽  
pp. 173-187
Author(s):  
Iraydes Tálita Nola ◽  
Luis Almeida Bacellar
Keyword(s):  
Fuzzy Logic ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Iron Formation ◽  
Susceptibility Map ◽  
Mountain Range ◽  
Tropical Regions ◽  
High Prediction ◽  
Gis Environment ◽  
Definition Of

In tropical regions, abundant in iron-rich geological materials, caves that are genetically and geographically associated with exploitable mineral deposits may develop. These caves have speleological relevance and are environmentally and legally protected in Brazil. Thus, for better planning of exploitation and environmental licensing, it is necessary to study the genesis and development of the iron formation caves seeking to preserve them without impeding the advancement of mining. This subject is complex, rarely studied, and few are the knowledges on alternatives to predict the occurrence of these caves. This gap justifies the development of research and products capable of assisting decision-makers, planners, and competent authorities in supporting the definition of target sites for speleological prospecting in the field. In this study, the prediction of the factors involved in the development of iron formation caves was evaluated producing a map of susceptibility in a GIS environment using fuzzy logic and an analytical hierarchic process (AHP). Therefore, the variables: iron oxide ratio, slope gradient, normalized difference vegetation index (NDVI), plan curvature, profile curvature, lineament intensity, and height above the nearest drainage (HAND) were selected. These variables were obtained by processing of geospatial data from a region of the Gandarela Range (Minas Gerais, Brazil). The fuzzy logic and AHP techniques were applied, and for the validation of the results, a previously surveyed cave inventory was used. The results showed satisfactory performance of the map produced in predicting areas favorable to the occurrence of iron formation caves, presenting an area under the receiver operating characteristic (ROC) curve of approximately 0.85, which indicates a high prediction rate and validates the proposed method. Such results demonstrate that this susceptibility map was reliable and that the set of criteria and weights used were suitable for mapping areas favorable for speleological prospecting.

scholarly journals Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 879
Author(s):  
Ducthien Tran ◽  
Dawei Xu ◽  
Vanha Dang ◽  
Abdulfattah.A.Q. Alwah
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Data Sources ◽  
Road Density ◽  
Rapid Urbanization ◽  
Spatial Factors ◽  
Digital Elevation ◽  
Important Means ◽  
Elevation Model

In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

scholarly journals Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms

2020 ◽  
Vol 12 (9) ◽  
pp. 3765 ◽  
Author(s):  
Luís Loures ◽  
Alejandro Chamizo ◽  
Paulo Ferreira ◽  
Ana Loures ◽  
Rui Castanho ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Arable Land ◽  
Agricultural Practices ◽  
Environmental Benefits ◽  
World Population ◽  
Small Farms ◽  
The Impact

While the world population continues to grow, increasing the need to produce more and better-quality food, climate change, urban growth and unsustainable agricultural practices accelerate the loss of available arable land, compromising the sustainability of agricultural lands both in terms of productivity and environmental resilience, and causing serious problems for the production-consumption balance. This scenario highlights the urgent need for agricultural modernization as a crucial step to face forthcoming difficulties. Precision agriculture techniques appear as a feasible option to help solve these problems. However, their use needs to be reinvented and tested according to different parameters, in order to define both the environmental and the economic impact of these new technologies not only on agricultural production, but also on agricultural sustainability. This paper intends, therefore, to contribute to a better understanding of the impact of precision agriculture through the use of unmanned aerial vehicles (UAV)/remotely piloted aircraft systems (RPAS) and normalized difference vegetation index (NDVI) techniques in small Mediterranean farms. We present specific data obtained through the application of the aforementioned techniques in three farms located along the Portuguese-Spanish border, considering three parameters (seeding failure, differentiated irrigation and differentiated fertilization) in order to determine not only the ecological benefits of these methods, but also their economic and productivity aspects. The obtained results, based on these methods, highlight the fact that an efficient combination of UAV/RPAS and NDVI techniques allows for important economic savings in productivity factors, thus promoting a sustainable agriculture both in ecological and economic terms. Additionally, contrary to what is generally defended, even in small farms, as the ones assessed in this study (less than 50 ha), the costs associated with the application of the aforementioned precision agriculture processes are largely surpassed by the economic gains achieved with their application, regardless of the notorious environmental benefits introduced by the reduction of crucial production inputs as water and fertilizers.

scholarly journals Using unmanned aerial vehicle for identifying the vegetative vigor and quantify the area occupied by eucalyptus sprouts after chemical weeding in the state of Bahia, Brazil

2020 ◽  
pp. 165
Author(s):  
Caique Carvalho Medauar ◽  
Samuel de Assis Silva ◽  
Luis Carlos Cirilo Carvalho ◽  
Rafael Augusto Soares Tibúrcio ◽  
Paullo Augusto Silva Medauar
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Sampling Methods ◽  
Control Method ◽  
Aerial Images ◽  
Aerial Vehicle

Currently, the efficiency of chemical weeding for controlling eucalyptus sprouts is measured by field sampling, but the inefficiency of the sampling methods has led to the investigation of new technologies, such as using unmanned aerial vehicle (UAV) to help to identify the vegetative vigor of eucalyptus after chemical weeding. This study, therefore, used aerial images obtained by a UAV embedded with a sensor to identify the vegetative vigor and quantify the area occupied by eucalyptus sprouts 90 days after the chemical weeding. The study was conducted in three fields planted with eucalyptus whose sprouts had been previously controlled by the chemical weeding with the Scout® herbicide in November 2016. The vegetative vigor of the eucalyptus sprouts was evaluated from the aerial images obtained by the UAV with embedded sensor, during flights conducted in November 2016 and February 2017, that were used to calculate the normalized difference vegetation index and later, a random sample grid was constructed for each image by supervised classification of the area (m2) to determine the percentage occupied by the sprouts. The used chemical control method neither eradicated the sprouts nor reduced the sprout occupied area. The normalized difference vegetation index and supervised classification tools allowed determining with high precision sprout health status and size, generating interpretable data on the different evaluated fields and periods. The processing of the images obtained by the UAV provided a viable alternative of management to evaluate sprout status in reforestation areas.

scholarly journals Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Dimitrios Stateras ◽  
Dionissios Kalivas
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Digital Terrain Model ◽  
Olive Tree ◽  
Tree Canopy ◽  
Tree Level ◽  
Surface Model

Greek agriculture is mainly based on olive tree cultivation. Farmers have always been concerned about annual olive orchard production. The necessity for the improvement of farming practices initiated the development of new technological tools that are useful in agriculture. The main goal of this study is the utilization of new technologies in order to define the geometry of olive tree configuration, while the development of a forecasting model of annual production in a non-linear olive grove, planted on a hilly uneven terrain is the secondary goal. The field’s orthomosaic, its Digital Terrain Model (DTM) and Digital Surface Model (DSM) were created by employing high resolution multispectral imagery. The Normalized Difference Vegetation Index (NDVI) thematic map has also been developed. The trees’ crowns were isolated employing the field’s orthomosaic, rendering individual polygons for each tree through Object Based Image Analysis (OBIA). The measurements were conducted in a Geographic Information System (GIS) environment and were also verified by ground ones. Tree crown height, surface, and volume were calculated, and thematic maps for each variable were created, allowing for the observation of the spatial distribution for each parameter. The compiled data were statistically analyzed revealing important correlations among different variables. These were employed to produce a model, which would enable production forecasting in kilograms per tree. The spatial distribution of the variables gave noteworthy results due to the similar pattern they followed. Future crop yield optimization, even at a tree level, can be based on the results of the present study. Its conclusions may lead to the development and implementation of precision olive tree cultivation practices.

scholarly journals A Crop Group-Specific Pure Pixel Time Series for Europe

2019 ◽  
Vol 11 (22) ◽  
pp. 2668 ◽  
Author(s):  
Christof J. Weissteiner ◽  
Raúl López-Lozano ◽  
Giacinto Manfron ◽  
Grégory Duveiller ◽  
Josh Hooker ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gaussian Mixture ◽  
Validation Dataset ◽  
Observation Data ◽  
The European Union ◽  
Clear Trend ◽  
Phenological Data ◽  
Modis Ndvi ◽  
Crop Monitoring

Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.

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