ArticleEnvironmental monitoring and assessment2019
Estimating long-term LULC changes in an agriculture-dominated basin using CORONA (1970) and LISS IV (2013-14) satellite images: a case study of Ramganga River, India.
Article in Environmental monitoring and assessment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The potential of historical spy-satellite imagery to support research in ecology and conservation.Bioscience · 2024Review
- Geochemical evolution of dissolved trace elements in space and time in the Ramganga River, India.Environmental monitoring and assessment · 2023Article
- Large-Scale Marsh Loss Reconstructed from Satellite Data in the Small Sanjiang Plain since 1965: Process, Pattern and Driving Force.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The basin of Ramganga River, a major tributary of Ganga, has seen rapid industrialization, urbanization, and agricultural growth in modern times, especially during and after the 1970s, with consequent changes in Land Use and Land Cover (LULC) of the basin. Object-based classification of seldom-used CORONA images (for 1970) for historical LULC scenario and LISS IV (Resourcesat-2) images (for 2013-14) for the present scenario of the basin was performed with high overall accuracies of 93% and 87% and Kappa coefficients of 0.89 and 0.82 respectively. Object-based classification using the eCognition software tool combined with manual classification of the images revealed significant decrease in total forest cover (~ 485 km
Indexed as
Identifiers
30868267What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.