Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal)

Shrestha, Dhruba Pikha; Zinck, J. A.

doi:10.1016/s0303-2434(01)85024-8

Cited by 74 publications

(36 citation statements)

References 6 publications

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“…While a similar user's accuracy was obtained after combining the three RS classification maps and the GIS ancillary data, the producer's accuracy increased from 82.6% to 92.7%, implying a decrease of 10% in omission errors. These results are in line with the conclusions made by Shrestha and Zinck [64] and Rozenstein and Karnieli [14].…”

Section: Accuracy Of Rice Area Classificationsupporting

confidence: 92%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error 237(RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

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Section: Accuracy Of Rice Area Classificationsupporting

confidence: 92%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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“…As a consequence, a bias in the class of 'clear or deep water' was induced -it was the class with the lowest reflectance in all bands. Similar issues have been pointed out in studies that dealt with the classification of land cover in mountainous area (Srestha & Zinck, 2001). …”

Section: Discussionsupporting

confidence: 69%

“…All the images had low sun elevation angles, ranging from 36.7 to 55.1°. Low sun elevation is known to generate shadows in the presence of abrupt topography, which affects the classification of land cover types of low reflectance such as clear or deep water (Srestha & Zinck, 2001). These shadows were filtered from the classification using the first critical index of the identified small www.ccsenet.org/enrr Environment and Natural Resources Research Vol.…”

Section: Classification Of Land Covermentioning

confidence: 99%

ICT-Based Identification and Characterisation of Small Reservoirs in the Limpopo River Basin in Zimbabwe

Mulengera¹,

Manzungu²,

Onema³

2012

ENRR

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Semi-arid conditions that prevail in the Limpopo basin in general and Zimbabwe in particular emphasise the importance of water storage. In this respect small reservoirs play a critical role in sustaining rural livelihoods. However, the management of small reservoirs in Zimbabwe is negatively affected by missing or poorly coordinated information relating to such key attributes as location, capacity, and environmental condition. A study was undertaken to identify and characterise small reservoirs in the Limpopo river basin in Zimbabwe. The objective of the study was to identify small reservoirs and characterise them in terms of capacity, and chlorophyll-a and turbidity indices, as proxies for measuring environmental degradation of catchments in which these are located. The study was carried out in Gwanda district that is located in South West Zimbabwe. Identification was done by processing Landsat TM 4-5 images of February-March and April-May 2009, using Geographical Information Systems. Time and cost considerations were the main factors in the selection of the technology and the images that were used. Field inspections were used to validate selected parameters from February to April 2011. A total of 256 small reservoirs, with an estimated total capacity of 17 million m 3 , were identified. The capacities of reservoirs were found to vary widely from around 4,000 m 3 to over 650,000 m 3 . About half (46%) of the small reservoirs were found to be dry by May, just two months after the end of the wet season. Seven reservoirs, which represented 2% of the total number and 3% of the total capacity of reservoirs, were characterised as highly turbid. Twenty-three reservoirs, representing 9% of the total number and 3% of the total capacity of reservoirs, showed signs of environmental degradation in the catchments. The smallest reservoirs were found to be most vulnerable to environmental degradation. The study concluded that Information and Communication Technologies (ICTs) can successfully be used to identify and characterise small reservoirs in the data-poor Limpopo basin in Zimbabwe, and can be used by decentralised water institutions and River Basin Organisations (RBOs) to better manage available water resources for the benefit of rural communities found therein.

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“…Shrestha & Zinck, 2001;Tomppo et al, 2008). Moreover, being based on field assessments on the large-scale representative bases, the presented map per se is a benchmark to further monitoring of forest deadwood content, and for stratified sampling designs.…”

Section: Resultsmentioning

confidence: 99%

Deadwood distribution in European forests

et al. 2017

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National forest inventories are a primary source of data for the assessment of forest resources and lastly more often biodiversity at national scales. The diversity of adopted sampling designs and measurements reduces the prospect for a reliable comparison of generated estimates. The ICP Forest dataset represents a unique opportunity for a standardized approach of forest estimates through Europe. This work aims to provide a distribution map of the mean deadwood volume in European forest. A total of 3243 ICP Forests plots were analysed and presented. The study area extends over 3,664,576 km 2 interesting 19 countries. We observed that the highest percentage of plots show a deadwood volume lower than 50 m 3 ha −1 , with a few of forests attaining around the maximum of 300 m 3 ha −1 . Forests with more than 100 m 3 ha −1 are concentrated in mountainous regions, central Europe and other regions, linked to high-forest management types, while coppices-derived forest systems (part of the Great Britain, Mediterranean region) show lower deadwood content. The map of deadwood volume on European Forests is of interests for scientists, land planners, forest managers and decision-makers, as a reference for further evaluation of changes, stratified sampling, ground reference for model validation, restoration and conservation purposes. ARTICLE HISTORY

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Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal)

Cited by 74 publications

References 6 publications

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

ICT-Based Identification and Characterisation of Small Reservoirs in the Limpopo River Basin in Zimbabwe

Deadwood distribution in European forests

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