Your search found 9 records
1 de Bie, C. A. J. M.; Khan, M. R.; Smakhtin, Vladimir; Venus, V.; Weir, M. J. C.; Smaling, E. M. A. 2011. Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping. International Journal of Remote Sensing, 32(21):6673-6693. [doi: https://doi.org/10.1080/01431161.2010.512939]
Remote sensing ; GIS ; Image analysis ; Land use ; Land cover ; Mapping ; Vegetation ; Crop management / India / Andhra Pradesh / Nizamabad District
(Location: IWMI HQ Call no: e-copy only Record No: H044207)
(3.14 MB)
Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l’Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1 km2 resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.
2 Bandara, K. M. P. S. 1998. Water needs and water use of agro-ecosystems in the Kirindi Oya Watershed, Sri Lanka: a remote sensing approach. MSc thesis. Enschede, Netherlands: ITC. 51p.
Water requirements ; Water use ; Agroecosystems ; Watersheds ; Remote sensing ; Research methods ; Evapotranspiration ; Data ; Vegetation ; Image analysis / Sri Lanka / Kirindi Oya
(Location: IWMI HQ Call no: 333.91 G744 BAN Record No: H044403)
(0.28 MB)
3 Nguyen, T. T. H.; De Bie, C. A. J. M.; Ali, A.; Smaling, E. M. A.; Hoanh, Chu Thai. 2011. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis. International Journal of Remote Sensing, 33(2):415-434. [doi: https://doi.org/10.1080/01431161.2010.532826]
Irrigated rice ; Crop management ; Remote sensing ; Mapping ; Deltas ; Image analysis ; Vegetation ; Indicators ; Data / Vietnam / Mekong delta
(Location: IWMI HQ Call no: e-copy only Record No: H044487)
(3.68 MB)
Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l’Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998–2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 lasses with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water controlmeasures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.
4 Asian Institute of Technology (AIT). 2004. Near real time agriculture activity monitoring using multi-temporal Modis earth observation satellite data. Final report. Unpublished final report submitted to the committee, Royal Thai Government Joint Research Project. 68p.
Remote sensing ; Satellite surveys ; Image analysis ; Image processing ; Vegetation ; Indicators ; Agriculture ; Crops ; Monitoring ; Surveys ; Land use / Thailand / Suphanburi Province
(Location: IWMI HQ Call no: P 8095 Record No: H044511)
(0.26 MB)
5 Thenkabail, P. S.; Lyon, J. G.; Huete, A. (Eds.) 2012. Hyperspectral remote sensing of vegetation. Boca Raton, FL, USA: CRC Press. 705p.
Remote sensing ; Vegetation ; Indicators ; Multispectral imagery ; Satellite observation ; Satellite imagery ; Image analysis ; Data processing ; Data analysis ; Algorithms ; Models ; Sensors ; Water use ; Agriculture ; Crop management ; Cereal crops ; Cotton ; Botany ; Tissue analysis ; Nitrogen content ; Moisture content ; Plant diseases ; Pastures ; Indicator plants ; Species ; Canopy ; Forest management ; Tropical forests ; Wetlands ; Ecosystems ; Soil properties ; Land cover ; Reflectance
(Location: IWMI HQ Call no: 621.3678 G000 THE Record No: H044548)
(0.54 MB)
6 Japan International Cooperation Agency (JICA); Kokusai Kogyo Co. Ltd.; Laos. Ministry of Health. 1995. The study on groundwater development for Champasak and Saravan provinces in Lao People's Democratic Republic Final report - supporting report. Tokyo, Japan: Japan International Cooperation Agency; Tokyo, Japan: Kokusai Kogyo Co. Ltd. 245p.
Groundwater development ; Remote sensing ; Image analysis ; Land use ; Water resources ; Wells ; Drilling ; Pumping ; Agriculture ; Water use ; Water quality ; Irrigation water ; Irrigation programs ; Hydrological factors ; Surveys ; Databases ; Data analysis ; Water rights / Laos / Champasak Province / Salavan Province
(Location: IWMI HQ Call no: e-copy only Record No: H044685)
http://lvzopac.jica.go.jp/external/library?func=function.opacsch.mmindex&view=view.opacsch.toshoshozodsp&lang=eng&shoshisbt=1&shoshino=0000086966&volno=0
https://vlibrary.iwmi.org/pdf/H044685.pdf
https://vlibrary.iwmi.org/pdf/H044685.pdf
(9.99 MB)
7 Chemin, Yann. (Ed.) 2012. Remote sensing of planet earth. Rijeka, Croatia: InTech. 240p.
Remote sensing ; GIS ; Vegetation ; Water resources ; Surface Water ; Mapping ; Monitoring ; Wetlands ; Lakes ; Satellite surveys ; Satellite imagery ; Image analysis ; Image processing ; Data ; Analytical methods ; Time series analysis ; Land cover ; Land classification ; Land use ; Tsunamis ; Snow cover ; Models ; Environmental effects ; Water vapour / Brazil / China / Italy / Indonesia / Thailand / Chile / Japan / Solomon Islands / Samoa Islands / Indonesia / Peruacu watershed / Tibet Plateau / Umbria / Subasio Mountain Regional Park / Banda Aceh / Phang Nga / Phuket / Tohoku / Okushiri Island / Banda Aceh
(Location: IWMI HQ Call no: IWMI Record No: H044692)
http://www.intechopen.com/books/show/title/remote-sensing-of-planet-earth
https://vlibrary.iwmi.org/pdf/H044692.pdf
https://vlibrary.iwmi.org/pdf/H044692.pdf
(28.13 MB) (28.13MB)
8 Uddin, K.; Gurung, D. R.; Amarnath, Giriraj; Shrestha, B. 2013. Application of remote sensing and GIS for flood hazard management: a case study from Sindh Province, Pakistan. American Journal of Geographic Information System, 2(1):1-5. [doi: https://doi.org/10.5923/j.ajgis.20130201.01]
Remote sensing ; GIS ; Vegetation ; Natural disasters ; Flood control ; Case studies ; Image analysis ; Models ; Mapping / Pakistan / Nepal / Sindh Province
(Location: IWMI HQ Call no: e-copy only Record No: H045720)
http://www.sapub.org/global/showpaperpdf.aspx?doi=10.5923/j.ajgis.20130201.01
https://vlibrary.iwmi.org/pdf/H045720.pdf
https://vlibrary.iwmi.org/pdf/H045720.pdf
(0.64 MB) (669.10KB)
Floods are one of the most common hazards in the world, affecting people’s lives and livelihoods. Flood hazard mapping and flood shelters suitability analysis are vital elements in appropriate land use planning for flood-prone areas. This paper describes application of Remote Sensing (RS) and Geographical Information Systems (GIS) in identifying flood hazard zones and flood shelters and are therefore important tools for planners and decision makers. The purpose of this article is to describe a simple and efficient methodology to accurately delineate flood inundated areas, flood-hazard areas, and suitable areas for flood shelter to minimize flood impacts. Possible extent of flooding and suitable location flood shelter sites were modeled and mapped for Sindh Province in Pakistan, using the software ArcGIS model builder. The output was validated using inundation maps based on flood events that took place in 2010 in Pakistan. These were mapped using object-based image analysis (OBIA) implemented in eCognition software. The catastrophic flood of 2010 inundated a total area of 7579 km2, while the modeled result indicated the hazard area to be 6216 km2 out of 46138 km2. Discrepancies in modeled and mapped results are insignificant and acceptable considering the manual flood management interventions which are beyond the capability of models to represent. Thus, this method is robust enough to develop flood hazard zoning maps and map shelter sites for flood management.
9 Pandey, S. K.; Chand, N.; Nandy, S.; Muminov, A.; Sharma, A.; Ghosh, Surajit; Srinet, R. 2020. High-resolution mapping of forest carbon stock using Object-Based Image Analysis (OBIA) technique. Journal of the Indian Society of Remote Sensing, 48(6):865-875. [doi: https://doi.org/10.1007/s12524-020-01121-8]
Forests ; Carbon stock assessments ; Mapping ; Satellite imagery ; Image analysis ; Techniques ; Estimation / India / Uttarakhand / Barkot Forest
(Location: IWMI HQ Call no: e-copy only Record No: H050799)
(6.21 MB)
This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)—WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. Object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.
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