Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

2021 ◽  
Author(s):  
Antonello Bonfante ◽  
Arturo Erbaggio ◽  
Eugenia Monaco ◽  
Rossella Albrizio ◽  
Pasquale Giorio ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
State Estimation ◽  
Spectral Data ◽  
Spatial Resolution ◽  
Spectral Region ◽  
Vegetation Indices ◽  
Plant Management ◽  
Spectral Vegetation Indices ◽  
Data Source

<p>Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.</p><p>The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.</p><p>The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of <2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across  400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.</p><p>The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).</p><p>The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.</p><p>The contribution has been realized within the CNR BIO-ECO project.</p>

scholarly journals Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping

2021 ◽  
Author(s):  
Maria Polivova ◽  
Anna Brook
Keyword(s):  
State Estimation ◽  
Vegetation Indices ◽  
Principal Component ◽  
State Monitoring ◽  
Early Stress ◽  
Alternative Approach ◽  
Crop Development ◽  
Spectral Vegetation Indices ◽  
Data Source ◽  
Stress States

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. These technologies have great importance for plant state monitoring, especially for agriculture. The main aim is to assess the performance level of the selected VIs calculated from space-borne multispectral imagery and point-based field spectroscopy in application to crop state estimation. The results obtained indicate that space-borne VIs react on phenology. This feature makes it an appropriate data source for monitoring crop development, crop water needs and yield prediction. Field spectrometer VIs were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability of the calculated values. The results obtained in the second part of the presented study were reported on crop state estimated by 17 VIs known as sensitive to plant drought. An alternative approach for identification early stress by VIs proposed in this study is Principal Component Analysis (PCA). The results show that PCA has identified the degree of similarity of the different states and together with reference stress states from the control plot clearly estimated stress in the actual irrigated field, which was hard to detect by VIs values only.

scholarly journals Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

2017 ◽  
Vol 31 (3) ◽  
pp. 419-432 ◽  
Author(s):  
Bogna Uździcka ◽  
Marcin Stróżecki ◽  
Marek Urbaniak ◽  
Radosław Juszczak
Keyword(s):  
Spectral Data ◽  
Spring Barley ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Winter Rye ◽  
Net Ecosystem Productivity ◽  
Ecosystem Productivity ◽  
Crop Species ◽  
Gross Ecosystem Productivity ◽  
Spectral Vegetation Indices

AbstractThe aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO2exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO2fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R2for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R2values indicate the utility of spectral vegetation indices in estimating CO2fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

DISSEMINATION AND USE OF INFORMATION ON CLIMATE CHANGE AND VARIABILITY: A CASE STUDY OF FARMERS IN MALUGA AND CHIBELA VILLAGES IN SEMI-ARID CENTRAL TANZANIA

Mousaion ◽  
2016 ◽  
Vol 33 (3) ◽  
pp. 1-24
Author(s):  
Emmanuel Elia ◽  
Stephen Mutula ◽  
Christine Stilwell
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
Diffusion Of Innovations ◽  
Agricultural Development ◽  
Agricultural Sector ◽  
Policy Framework ◽  
Accurate Information ◽  
Adaptation To Climate Change ◽  
Climate Change And Variability ◽  
Semi Arid

This study was part of broader PhD research which investigated how access to, and use of, information enhances adaptation to climate change and variability in the agricultural sector in semi-arid Central Tanzania. The research was carried out in two villages using Rogers’ Diffusion of Innovations theory and model to assess the dissemination of this information and its use by farmers in their adaptation of their farming practices to climate change and variability. This predominantly qualitative study employed a post-positivist paradigm. Some elements of a quantitative approach were also deployed in the data collection and analysis. The principal data collection methods were interviews and focus group discussions. The study population comprised farmers, agricultural extension officers and the Climate Change Adaptation in Africa project manager. Qualitative data were subjected to content analysis whereas quantitative data were analysed to generate mostly descriptive statistics using SPSS.  Key findings of the study show that farmers perceive a problem in the dissemination and use of climate information for agricultural development. They found access to agricultural inputs to be expensive, unreliable and untimely. To mitigate the adverse effects of climate change and variability on farming effectively, the study recommends the repackaging of current and accurate information on climate change and variability, farmer education and training, and collaboration between researchers, meteorology experts, and extension officers and farmers. Moreover, a clear policy framework for disseminating information related to climate change and variability is required.

KORESPONDENSI FONEMIS BAHASA SUNDA DAN BAHASA JAWA

IdeBahasa ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 121-132
Author(s):  
Shifa Nur Zakiyah ◽  
Susi Machdalena ◽  
Tb. Ace Fachrullah
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Interview Techniques ◽  
Data Source ◽  
Historical Comparative ◽  
Linguistic Approach ◽  
The Relationship ◽  
Recording Techniques

This article discussed the phonemic correspondence in Sundanese and Javanese using a historical comparative linguistic approach. The problem to be examined in this study is the form of phonemic correspondence in Sundanese and Javanese. The purpose of this study was to determine the phonemic correspondence sets in the comparison between Sundanese and Javanese. The method used in this research to analyze the data is the phonemic correspondence method. The correspondence method is used to find the relationship between languages ​​in the field of language sounds (phonology). Phonemic correspondence is used to determine regular phonemic changes in the languages ​​being compared. Data collection used interview techniques, note techniques and recording techniques. After the data is collected, then the data is classified according to the problem being studied and grouped into more specifics. After that, conclusions will be made based on the results of the data analysis. The data source obtained comes from 200 swadesh vocabularies in Sundanese and Javanese. From 200 swadesh vocabulary data found 49 data included in phonemic correspondence which is divided into 12 correspondence sets. The results of this study include the formation of correspondences in Sundanese and Javanese, namely, (ɛ ~ i) and (i ~ ɛ), (a ~ ɔ) and (ɔ ~ a), (d ~ D), (t ~ T) , (ɤ ~ ə), (b ~ w), (ɔ ~ u) and (ɔ ~ U), (i ~ I), (ø ~ h) and (h ~ ø), (ø ~ m), and (a ~ ə).

METODE BIMBINGAN DAN KONSELING ISLAM DALAM MENANAMKAN KEDISIPLINAN SHOLAT DHUHA PADA ANAK HIPERAKTIF DI MI NURUL ISLAM NGALIYAN SEMARANG

2017 ◽  
Vol 36 (1) ◽  
pp. 1
Author(s):  
Hidayatul Khasanah ◽  
Yuli Nurkhasanah ◽  
Agus Riyadi
Keyword(s):  
Qualitative Research ◽  
Data Collection ◽  
Role Model ◽  
Hyperactive Child ◽  
Guidance And Counseling ◽  
Discipline Problems ◽  
Hyperactive Children ◽  
Data Source

<p>This research aimed to describe the characteristics of hyperactive children and analyze methods of Islamic guidance and counseling in instilling discipline of Duha prayer in hyperactive children in MI Nurul Islam Ngaliyan Semarang. This research is qualitative research. The data source is a teacher as well as a hyperactive child. Methods of data collection using interviews, observation, and documentation. The results showed that hyperactive children have discipline problems in implementing the Duha prayer in congregation. Islamic guidance and counseling methods used to embed discipline of Duha prayer for hyperactive children consisting of four methods: the method of habituation, role model, motivation and supervision.</p><p align="center"><strong>***</strong></p><p>Penelitian ini merupakan penelitian kualitatif yang bertujuan untuk mendiskripsikan karakteristik anak hiperaktif dan menganalisis metode bimbingan dan konseling Islam dalam menanamkan kedisiplinan shalat dhuha pada anak hiperaktif di MI Nurul Islam Ngaliyan Semarang. Jenis penelitian ini merupakan penelitian kualitatif. Sumber data penelitian ini adalah guru serta anak hiperaktif. Metode pengumpulan data menggunakan wawancara, observasi, dan dokumentasi. Hasil penelitian menunjukkan bahwa pertama anak hiperaktif memiliki problem kedisiplinan dalam melaksanakan shalat dhuha berjamaah. Kedua, metode bimbingan dan konseling Islam yang digunakan untuk menanamkan kedisiplian shalat dhuha bagi anak hiperaktif terdiri dari empat metode yaitu metode pembiasaan, metode tauladan, metode nasehat (motivasi), dan metode pengawasan ketika shalat dhuha berjamaah berlangsung.</p>

scholarly journals A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Matieu Henry ◽  
Zaheer Iqbal ◽  
Kristofer Johnson ◽  
Mariam Akhter ◽  
Liam Costello ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Data Collection ◽  
Land Cover ◽  
Forest Inventory ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
Forest Resources ◽  
National Forest ◽  
Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

scholarly journals The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

2021 ◽  
Vol 13 (10) ◽  
pp. 1958
Author(s):  
Shelly Elbaz ◽  
Efrat Sheffer ◽  
Itamar M. Lensky ◽  
Noam Levin
Keyword(s):  
Spatial Resolution ◽  
Seasonal Changes ◽  
Dominant Role ◽  
Vegetation Indices ◽  
Individual Species ◽  
Viewing Angle ◽  
Pixel Size ◽  
Mediterranean Species ◽  
Near Surface ◽  
Nadir Point

Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VIs) resampled to 14, 30, 125, and 500 cm to simulate the spatial resolutions available from some satellites, and VENμS Level 1 product (with a nominal spatial resolution of 5.3 m at nadir; seven VIs; 1551 individual plants). The various sensors described seasonal changes in the species’ VIs at different levels of success. Strong correlations between the near-surface sensors for a given VI and species mostly persisted for all spatial resolutions ≤125 cm. The UAV ExG index presented high correlations with the ground camera data in most species (pixel size ≤125 cm; 9 of 12 species with R ≥ 0.85; p < 0.001), and high classification accuracies (pixel size ≤30 cm; 8 species with >70%), demonstrating the possibility for detailed species mapping from space. The seasonal dynamics of the species obtained from VENμS demonstrated the dominant role of ephemeral herbaceous vegetation on the signal recorded by the sensor. The low variance between the species as observed from VENμS may be explained by its coarse spatial resolution (effective ground spatial resolution of 7.5) and its non-nadir viewing angle (29.7°) over the study area. However, considering the challenging characteristics of the research site, it may be that using a VENμS type sensor (with a spatial resolution of ~1 m) from a nadir point of view and in more homogeneous and dense areas would allow for detailed mapping of Mediterranean species based on their seasonality.

scholarly journals Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Potential ◽  
Large Scale ◽  
Water Status ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Severe Drought ◽  
Stem Water Potential ◽  
Stem Water ◽  
Sentinel 2

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).

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