These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
127 related articles for article (PubMed ID: 38671156)
1. Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques. Suwanprasit C; Shahnawaz Sci Rep; 2024 Apr; 14(1):9609. PubMed ID: 38671156 [TBL] [Abstract][Full Text] [Related]
2. Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture. Kutz K; Cook Z; Linderman M Sci Rep; 2022 Jul; 12(1):11291. PubMed ID: 35789170 [TBL] [Abstract][Full Text] [Related]
3. Rapid and automatic burned area detection using sentinel-2 time-series images in google earth engine cloud platform: a case study over the Andika and Behbahan Regions, Iran. Farhadi H; Mokhtarzade M; Ebadi H; Beirami BA Environ Monit Assess; 2022 Apr; 194(5):369. PubMed ID: 35430649 [TBL] [Abstract][Full Text] [Related]
4. Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape. Ngadze F; Mpakairi KS; Kavhu B; Ndaimani H; Maremba MS PLoS One; 2020; 15(5):e0232962. PubMed ID: 32459824 [TBL] [Abstract][Full Text] [Related]
5. Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sun C; Bian Y; Zhou T; Pan J Sensors (Basel); 2019 May; 19(10):. PubMed ID: 31130689 [TBL] [Abstract][Full Text] [Related]
6. Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data. Adiningrat DP; Schlund M; Skidmore AK; Abdullah H; Wang T; Heurich M Environ Monit Assess; 2024 Aug; 196(9):841. PubMed ID: 39183185 [TBL] [Abstract][Full Text] [Related]
7. Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods. Erdanaev E; Kappas M; Wyss D Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35957240 [TBL] [Abstract][Full Text] [Related]
9. Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage. Zhang S; Zhao G; Lang K; Su B; Chen X; Xi X; Zhang H Sensors (Basel); 2019 Mar; 19(7):. PubMed ID: 30934683 [TBL] [Abstract][Full Text] [Related]
10. Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models. Mohammad L; Bandyopadhyay J; Sk R; Mondal I; Nguyen TT; Lama GFC; Anh DT J Environ Manage; 2023 Oct; 343():118226. PubMed ID: 37245309 [TBL] [Abstract][Full Text] [Related]
11. Application of satellite remote sensing data and random forest approach to estimate ground-level PM Wongnakae P; Chitchum P; Sripramong R; Phosri A Environ Sci Pollut Res Int; 2023 Aug; 30(38):88905-88917. PubMed ID: 37442931 [TBL] [Abstract][Full Text] [Related]
12. Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study. Atasever ÜH; Tercan E Environ Sci Pollut Res Int; 2024 Jan; 31(4):5304-5318. PubMed ID: 38112873 [TBL] [Abstract][Full Text] [Related]
13. Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches. Meneguzzo DM; Liknes GC; Nelson MD Environ Monit Assess; 2013 Aug; 185(8):6261-75. PubMed ID: 23255169 [TBL] [Abstract][Full Text] [Related]
14. Semi-automated detection of ungulates using UAV imagery and reflective spectrometry. De Kock ME; Pohůnek V; Hejcmanová P J Environ Manage; 2022 Oct; 320():115807. PubMed ID: 35944320 [TBL] [Abstract][Full Text] [Related]
16. Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm. Tan C; Zhou X; Zhang P; Wang Z; Wang D; Guo W; Yun F PLoS One; 2020; 15(3):e0228500. PubMed ID: 32160185 [TBL] [Abstract][Full Text] [Related]
17. Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application. Maxwell SK Spat Spatiotemporal Epidemiol; 2010 Dec; 1(4):231-7. PubMed ID: 21135917 [TBL] [Abstract][Full Text] [Related]
18. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Kattenborn T; Eichel J; Fassnacht FE Sci Rep; 2019 Nov; 9(1):17656. PubMed ID: 31776370 [TBL] [Abstract][Full Text] [Related]
19. The rectangular tile classification model based on Sentinel integrated images enhances grassland mapping accuracy: A case study in Ordos, China. Guo F; Fan L; Chen W; Xiao D; Niu H PLoS One; 2024; 19(4):e0301444. PubMed ID: 38626150 [TBL] [Abstract][Full Text] [Related]