Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland) DOI Creative Commons
А. В. Чинилин, Nikolai Lozbenev, P. M. Shilov

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2229 - 2229

Published: Dec. 20, 2024

This study presents an approach for predicting soil class probabilities by integrating synthetic composite imagery of bare with long-term vegetation remote sensing data and survey data. The goal is to develop detailed maps the agro-innovation center “Orlovka-AIC” (Samara Region), a focus on lithological heterogeneity. Satellite were sourced from cloud-filtered collection Landsat 4–5 7 images (April–May, 1988–2010) 8–9 (June–August, 2012–2023). Bare surfaces identified using threshold values NDVI (<0.06), NBR2 (<0.05), BSI (>0.10). Synthetic generated calculating median reflectance across available spectral bands. Following adoption no-till technology in 2012, average additionally calculated assess condition agricultural lands. Seventy-one sampling points within classified both Russian WRB classification systems. Logistic regression was applied pixel-based prediction. model achieved overall accuracy 0.85 Cohen’s Kappa coefficient 0.67, demonstrating its reliability distinguishing two main classes: agrochernozems agrozems. resulting map provides robust foundation sustainable land management practices, including erosion prevention use optimization.

Language: Английский

Digital mapping of soil properties in the high latitudes of Russia using sparse data DOI
Azamat Suleymanov, Evgeny Abakumov, Ivan Alekseev

et al.

Geoderma Regional, Journal Year: 2024, Volume and Issue: 36, P. e00776 - e00776

Published: Feb. 2, 2024

Language: Английский

Citations

8
Conventional and Digital Soil Mapping in the Central Part of the Smolenskoe Poozer’e National Park DOI

Albina Kornilova,

М. А. Смирнова, Ilia Semenkov

et al.

Eurasian Soil Science, Journal Year: 2025, Volume and Issue: 58(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Regional resource provision map: methodology and key approaches DOI Open Access
А. А. Адамбекова, Moldir Mukan, Bazhan Turebekova

et al.

Bulletin of Turan University, Journal Year: 2024, Volume and Issue: 2, P. 124 - 138

Published: June 30, 2024

The achievement of sustainable development goals with the help implementation a systematic approach to managing resource potential regions through is one actual objectives in regional management. Mapping known as an approach, which allows combining several data sources different scaling. This study aims develop provision map for creating conditions. Multidisciplinary research valuable source this that unit ESG criteria and their commitment cartographic science tools. methodology presented form sequence actions draw up supply map. Using Western Kazakhstani region confirms validity scientific applied methodology. outcomes contain proven arguments further based on issues constructing integrated maps regions. Key cartography approaches make it possible recommendations similar use terms decision-making interregional interaction, taking into account potential, consisting natural, labor, financial, infrastructural capabilities environmental risk assessments. Developed were tested Microsoft Power BI SuperMap (laboratory “Geoinformation Cartography” Kazakh National University named after al-Farabi Kazakh).

Language: Английский

Citations

1
Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone DOI

Suleymanov Azamat,

Asylbaev Ilgiz,

Suleymanov Ruslan

et al.

Geoderma Regional, Journal Year: 2024, Volume and Issue: 38, P. e00855 - e00855

Published: Aug. 30, 2024

Language: Английский

Citations

1
Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China DOI Creative Commons
Ruizhen Wang, Weitao Chen, Hao Chen

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 449, P. 117023 - 117023

Published: Sept. 1, 2024

Language: Английский

Citations

1
Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning DOI
Bifeng Hu, Yibo Geng,

Kejian Shi

et al.

CATENA, Journal Year: 2024, Volume and Issue: 249, P. 108635 - 108635

Published: Dec. 9, 2024

Language: Английский

Citations

1
Digital Cartography Transforming Travel Decision-Making With VGI DOI
Munir Ahmad, Yasir Ansari,

Achmad Izzul Waro

et al.

Advances in hospitality, tourism and the services industry (AHTSI) book series, Journal Year: 2024, Volume and Issue: unknown, P. 127 - 148

Published: Nov. 27, 2024

Volunteered Geographic Information (VGI) significantly enhances travel planning by offering dynamic, user-generated data across diverse categories such as POIs, restaurants, shops, historical sites, hidden gems, and natural wonders. By leveraging VGI, travelers can uncover unique, off-the-beaten-path experiences stay updated with real-time information on transportation networks, accessibility, live traffic conditions. VGI also provide valuable reviews ratings, updates, visual content that aid in decision-making. However, to maximize the benefits of it's crucial identify reliable sources, cross-reference traditional resources, consider strengths limitations content. Integrating guidebooks, blogs, perspectives allow build personalized, well-rounded itineraries cater their specific interests preferences. The emergence new technologies like AI, ML, AR, VR will further revolutionize making even more intuitive immersive.

Language: Английский

Citations

0
Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland) DOI Creative Commons
А. В. Чинилин, Nikolai Lozbenev, P. M. Shilov

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2229 - 2229

Published: Dec. 20, 2024

This study presents an approach for predicting soil class probabilities by integrating synthetic composite imagery of bare with long-term vegetation remote sensing data and survey data. The goal is to develop detailed maps the agro-innovation center “Orlovka-AIC” (Samara Region), a focus on lithological heterogeneity. Satellite were sourced from cloud-filtered collection Landsat 4–5 7 images (April–May, 1988–2010) 8–9 (June–August, 2012–2023). Bare surfaces identified using threshold values NDVI (<0.06), NBR2 (<0.05), BSI (>0.10). Synthetic generated calculating median reflectance across available spectral bands. Following adoption no-till technology in 2012, average additionally calculated assess condition agricultural lands. Seventy-one sampling points within classified both Russian WRB classification systems. Logistic regression was applied pixel-based prediction. model achieved overall accuracy 0.85 Cohen’s Kappa coefficient 0.67, demonstrating its reliability distinguishing two main classes: agrochernozems agrozems. resulting map provides robust foundation sustainable land management practices, including erosion prevention use optimization.

Language: Английский

Citations

0