Exploring vegetation indices adequate in detecting twister disease of onion using Sentinel-2 imagery

Isip, M. F.; Alberto, Ronaldo T.; Biagtan, A. R.

doi:10.1007/s41324-019-00297-7

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…Host plants presenting conspicuous stress indicators (leaf browning, wilting, chlorosis and desiccation) can be discriminated by Sentinel-2 satellites through spectral shifts [221,225,226,232,233]. This satellite imagery has been widely used to support analysis of land use and relevant geophysical variables (e.g., leaf area index, leaf chlorophyll content and leaf water content, Box 2).…”

Section: Vegetation Indexmentioning

confidence: 99%

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

et al. 2020

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Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmental, social and economic impacts. Once restricted to the Americas, it has recently invaded Europe, where multiple dramatic outbreaks have highlighted critical challenges for its management. Here, we review the most recent advances on the identification, distribution and management of X. fastidiosa and its insect vectors in Europe through genetic and spatial ecology methodologies. We underline the most important theoretical and technological gaps that remain to be bridged. Challenges and future research directions are discussed in the light of improving our understanding of this invasive species, its vectors and host–pathogen interactions. We highlight the need of including different, complimentary outlooks in integrated frameworks to substantially improve our knowledge on invasive processes and optimize resources allocation. We provide an overview of genetic, spatial ecology and integrated approaches that will aid successful and sustainable management of one of the most dangerous threats to European agriculture and ecosystems.

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Section: Vegetation Indexmentioning

confidence: 99%

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

et al. 2020

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“…Berikut penelitian terdahulu yang mendasari pemilihan indeks komposit. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), dan PSSRa (Pigment Specific Simple Ratio for Chlorophyll a) memiliki sensitivitas tinggi dan mampu meningkatkan akurasi kerusakan bawang merah (Isip et al, 2019). EVI2 (Enhanced Vegetation Index 2) mendeteksi warna tanah tempat vegetasi dengan kecenderungan hijau untuk membedakan kerapatan antar kanopi tanaman (Rocha & Shaver, 2009).…”

Section: Variabel Dasar Dan Kompositunclassified

Penerapan Pembelajaran Mesin Untuk Estimasi Luas Lahan Bawang Merah Berdasarkan Data Citra Satelit Resolusi Menengah

Zulkarnain¹,

Marsisno²

2022

semnasoffstat

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Kabupaten Nganjuk merupakan produsen bawang merah terbesar kedua nasional. Potensi tersebut membuat upaya besar dalam menghitung luas lahannya. Hasil penghitungan eye estimate yang tidak dapat dipertanggungjawabkan masih digunakan. Pengindraan jarak jauh (indraja) menawarkan alternatif menggunakan citra satelit resolusi menengah Sentinel-2 sebagai sumber informasi. Nilai pita spektral Sentinel-2 di bulan Juni dan Agustus 2020 diekstrak menjadi variabel dasar dan komposit, kemudian dilatih untuk pemodelan pembelajaran mesin. Variabel tereduksi kemudian dievaluasi dua tahap. Evaluasi internal nilai performa dan uji Mc Nemar menunjukkan model Support Vector Machine (SVM) unggul untuk objek Juni, sedangkan Random Forest (RF) untuk Agustus 2020. Evaluasi eksternal selisih total luas lahan terhadap data publikasi Badan Pusat Statistik (BPS) menunjukkan model SVM diunggulkan kedua objek karena selisih total terkecil yaitu 897,53 dan 5382,48 hektar. Selisih total yang sangat tinggi kurang diharapkan sehingga pemilihan dan pengembangan model, pelabelan secara intensif, atau penerapan pemilihan variabel penelitian ini dapat dipakai untuk penelitian mendatang.

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“…It is also important to stress that ecologists, botanists and remote sensing scientists should come together and agree on standardised variables that are critical for mapping and monitoring crop disease and pests using remote sensing data. Isip et al [208] indicated that Sentinel 2-based VIs can be used for the detection of twister disease in the field since it gives greater discrimination and high accuracies.…”

Section: New Freely Available Rs Data To Improve Monitoring Agricultural Crop Statusmentioning

confidence: 99%

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

et al. 2021

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Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.

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Exploring vegetation indices adequate in detecting twister disease of onion using Sentinel-2 imagery

Cited by 13 publications

References 18 publications

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

Penerapan Pembelajaran Mesin Untuk Estimasi Luas Lahan Bawang Merah Berdasarkan Data Citra Satelit Resolusi Menengah

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

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