scholarly journals Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

2021 ◽  
Vol 13 (24) ◽  
pp. 13554
Author(s):  
Velia Bigi ◽  
Ingrid Vigna ◽  
Alessandro Pezzoli ◽  
Elena Comino
Keyword(s):  
Vegetation Dynamics ◽  
Management Strategies ◽  
Vegetation Indices ◽  
Climatic Conditions ◽  
Percentage Increase ◽  
Intergovernmental Panel ◽  
Spectral Angle Mapper ◽  
Vegetation Growth ◽  
Multi Temporal ◽  
Sentinel 2

According to the Intergovernmental Panel on Climate Change, the Horn of Africa is getting drier. This research aims at assessing browning and/or greening dynamics and the suitability of Sentinel-2 satellite images to map changes in land cover in a semiarid area. Vegetation dynamics are assessed through a remote sensing approach based on densely vegetated areas in a pilot area of North Horr Sub-County, in northern Kenya, between 2016–2020. Four spectral vegetation indices are calculated from Sentinel-2 images to create annual multi-temporal images. Two different supervised classification methods—Minimum Distance and Spectral Angle Mapper—are then applied in order to identify dense vegetated areas. A general greening is found to have occurred in this period with the exception of the year 2020, with an average annual percentage increase of 19%. Results also highlight a latency between climatic conditions and vegetation growth. This approach is for the first time applied in North Horr Sub-County and supports local decision-making processes for sustainable land management strategies.

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

scholarly journals Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa

2019 ◽  
Vol 11 (3) ◽  
pp. 278 ◽  
Author(s):  
Samuel Kumbula ◽  
Paramu Mafongoya ◽  
Kabir Peerbhay ◽  
Romano Lottering ◽  
Riyad Ismail
Keyword(s):  
South Africa ◽  
Management Strategies ◽  
Vegetation Indices ◽  
Model Performance ◽  
Area Under The Curve ◽  
Mass Trapping ◽  
Eucalyptus Nitens ◽  
Operating Characteristics ◽  
Presence Data ◽  
Sentinel 2

Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.

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 Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Radosław Gurdak ◽  
Maciej Bartold
Keyword(s):  
Chlorophyll Fluorescence ◽  
Crop Production ◽  
Pearson Correlation ◽  
Vegetation Indices ◽  
Climatic Conditions ◽  
Sugar Beets ◽  
Spectral Vegetation Indices ◽  
Meteorological Observations ◽  
The Impact ◽  
Sentinel 2

AbstractThe increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=-0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

scholarly journals Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 785
Author(s):  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Rigas Giovos ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Climatic Conditions ◽  
Vine Growth ◽  
Protected Designation Of Origin ◽  
Growth Differences ◽  
Sentinel 2

Remote sensing satellite platforms provide accurate temporal and spatial information useful in viticulture with an increasing interest in their use. This study aims to identify the possibilities of freely available and with frequent revisit time Sentinel-2 satellites, to monitor vine growth at regional scale on a vine-growing Protected Designation of Origin (PDO) zone during the growing season of the year 2019. This study aims to: (i) investigate through several Vegetation Indices (VIs) the vine growth differences across the zone and relations with topographic parameters; (ii) identify VIs that best recognize differences on subzones of different climatic conditions; (iii) explore the effectiveness of the Sentinel-2 data monitoring management applications. A total of 27 vineyards were selected for field and satellite data collection. Several VIs have been calculated per vineyard from a 20-date time series dataset. VIs showed high negative correlation with topographic parameter of elevation on the flowering stage. The analysis of variance between the VIs of the subzones showed that these regions have statistically significant differences, that most VIs can expose on the flowering and harvest stage, and only Normalized Difference Vegetation Index (NDVI) and VIs using Red-Edge bands during the veraison period. Sentinel-2 data show great effectiveness on monitoring management applications (tillage and trimming).

Heavily data-constrained mechanistic ecohydrological modeling can guide management of pre-Alpine grasslands in the present and future climate

2020 ◽  
Author(s):  
Martina Botter ◽  
Matthias Zeeman ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Management Strategies ◽  
Climatic Conditions ◽  
Mechanistic Modeling ◽  
System Response ◽  
Summer Drought ◽  
Vegetation Productivity ◽  
Vegetation Growth ◽  
Soil Biogeochemistry ◽  
Alpine Regions ◽  
The Impact

<p><span>The pressure of climate change and increasing food demand on agricultural systems made management strategies crucial for matching the production goals without affecting water quantity and quality. This is, for instance, the case for managed grasslands in the Alpine and pre-alpine regions. This study combines a large suite of observations from the TERENO observatory and ScaleX campaigns with mechanistic modeling, in order to analyze the response of managed grasslands north of the Alps to different climatic conditions and management strategies, aimed at evaluating changes in the ecohydrological response, as well as carbon, water and nutrient fluxes.  </span></p><p><span>First, we used the data to evaluate the performance of the mechanistic Tethys-Chloris (T&C) model, which fully integrates the solution of surface energy balance and hydrological budget with vegetation dynamics and soil biogeochemistry, for the period 2012-2016. This is characterized by significant climatic inter-annual variability and including </span><span>the extraordinarily warm year 2015. The observations cover three different grassland sites composed by flux towers, soil moisture and temperature probes, lysimeters, nutrient leaching and dedicated vegetation sampling campaigns, allowing an unprecedented validation opportunity of model skills for multiple variable and temporal scales. The observed system response, in terms of water, energy and nutrients dynamics, are successfully reproduced, which increases confidence on the model capability to reproduce the feedbacks among hydrology, vegetation growth and soil biogeochemistry. The results highlight the impact of an early begin of the growing season on the vegetation productivity and nutrients leaching in years with reduced snow cover,  as well as the effects of summer drought on vegetation productivity. </span></p><p><span>Second, numerical experiments are used to test the response of this ecosystem to different grassland fertilization and cutting scenarios in the present climate and in warmer and CO<sub>2</sub> enriched conditions. Of particular interest are the number and timing of grass cuts and fertilizer applications that could optimize grassland productivity without compromising water quality in a warmer climate. </span></p>

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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 Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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