Your search found 15 records
1 Melesse, A. M.; Weng, Q.; Thenkabail, Prasad S.; Senay, G. B. 2007. Remote sensing sensors and applications in environmental resources mapping and modelling. Sensors, 7:3209-3241.
Remote sensing ; Sensors ; Imagery ; Models ; Environmental effects ; Drought ; Soil water ; Mapping ; Hydrology ; Forecasting ; Early warning systems
(Location: IWMI HQ Call no: IWMI 621.3678 G000 MEL Record No: H040633)
The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land- cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhausted application of the remote sensing technique rather summary of some important applications in environmental studies and modeling.
2 Woodcock, C. E.; Allen, R.; Anderson, M.; Belward, A.; Bindschadler, R.; Cohen, W.; Gao, F.; Goward, S. N.; Helder, D.; Helmer, E.; Nemani, R.; Oreopoulos, L.; Schott, J.; Thenkabail, Prasad, S.; Vermote, E. F.; Vogelmann, J.; Wulder, M. A.; Wynne, R. 2008. Free access to Landsat imagery. Science, 320: 1011-1012.
(Location: IWMI HQ Call no: IWMI 621.3678 G430 WOO Record No: H041184)
http://www.fs.fed.us/global/iitf/pubs/ja_iitf_2008_woodcock001.pdf
https://vlibrary.iwmi.org/pdf/H041184.pdf
https://vlibrary.iwmi.org/pdf/H041184.pdf
3 Asian Institute of Technology (AIT). 2005. Multi-temporal analysis of normalized differential vegetation index, temperature and other metrics using fusion of high-resolution and low-resolution imageries for global irrigated area mapping. Final report. Unpublished final report of the Space Technology Applications and Research (STAR) Program, submitted to IWMI. 108p.
Irrigated sites ; Irrigated farming ; Mapping ; Vegetation ; Indicators ; Irrigated rice ; Cultivation ; Rain ; Temperature ; Remote sensing ; GIS ; Statistical methods ; Imagery / Thailand
(Location: IWMI HQ Call no: 631.7.2 G750 ASI Record No: H044503)
(0.35 MB)
4 Adams, J. B.; Gillespie, A. R. 2006. Remote sensing of landscapes with spectral images: a physical modeling approach. New York, NY, USA: Cambridge University Press. 362p.
Remote sensing ; Landscape ; Models ; Calibration ; Imagery ; Classification ; Spectroscopy ; Spectral analysis ; Vegetation ; Indicators ; Infrared radiation
(Location: IWMI HQ Call no: 551.48 G000 ADA Record No: H046138)
(0.73 MB)
5 Ashraf, M.; Bhatti, Muhammad Tousif; Shakir, A. S. 2016. River bank erosion and channel evolution in sand-bed braided reach of River Chenab: role of floods during different flow regimes. Arabian Journal of Geosciences, 9(2):1-10. [doi: https://doi.org/10.1007/s12517-015-2114-y]
Riverbank protection ; Erosion control ; Flooding ; Landsat ; Imagery ; Sand ; Open channels ; Monsoon climate ; Flow discharge ; Stream flow ; Environmental protection / Pakistan / Chenab River
(Location: IWMI HQ Call no: e-copy only Record No: H047488)
(5.03 MB)
Braided reaches of large rivers in alluvial plains show major morphological changes, particularly the external bank erosion, due to the flood events. This paper highlights the bank erosion and channel evolution induced by eleven different flood events in a 7-km long reach of the River Chenab, Pakistan. The impact of floods on river bank erosion and channel evolution is analyzed under low and high flow conditions. Flood-induced changes, for river’s external banks and channel evolution, were assessed by processing Landsat ETM+ images in ArcGIS tool, and their inter-relationship is evaluated through regression analysis. The results revealed that the major morphological changes were triggered by the flood events occurred during the high flow or Monsoon season (July–September), whereas the flood events of similar magnitude occurring during low flow season (October–March) did not induce such changes. Mostly, the erosion remained limited to the middle part of the reach,where the branch channel flows along the bank. The average annual bank erosion rates are much higher as compared with a global scale. Data analysis showed a strong correlation between the mean high flows and total bank erosion indicating that Monsoon seasonal flows and floods are responsible for bank erosion. The present study further identifies the river bank locations highly susceptible to erosion by developing the correlation between bank erosion and branch channel progression. Strong correlation for bank erosion could be established with the shift of branch channels position flowing along the banks in braided reaches of sand bed rivers. However, the presence of sand bars along the river banks resulted in reduced erosion that weakens this relationship. The findings of the present study can help develop better understanding about the bank erosion process and constitute a key element to inform and improve river bank management.
6 Siddiqui, Salman. 2016. Sri Lanka’s drone pioneers. ICT Update: a current awareness bulletin for ACP agriculture, 2p.
(Location: IWMI HQ Call no: e-copy only Record No: H047540)
http://ictupdate.cta.int/Regulars/Perspectives/Sri-Lanka-s-drone-pioneers/(82)/1461765974
https://vlibrary.iwmi.org/pdf/H047540.pdf
https://vlibrary.iwmi.org/pdf/H047540.pdf
(0.12 MB)
7 Hussain, Asghar; Baker, Tracy. 2016. Tana River Basin, Kenya: geodatabase and mapping tool. User guide. Colombo, Sri Lanka: International Water Management Institute (IWMI). 138p. [doi: https://doi.org/10.5337/2016.210]
Administration ; Infrastructure ; Geography ; Land use ; Land cover ; Living standards ; River basin management ; Watersheds ; Guidelines ; Software ; Imagery ; GIS ; Mapping ; Meteorological stations ; Temperature ; Soils ; Irrigation ; Farming systems ; Water power ; Dams ; Population density ; Demography ; Natural resources ; Environmental effects ; Urban areas ; Rangelands ; Water resources / Kenya / Tana River Basin
(Location: IWMI HQ Call no: IWMI Record No: H047737)
(2 MB)
8 Dinka, M. O. 2017. Lake Basaka expansion: challenges for the sustainability of the Matahara Irrigation Scheme, Awash River Basin (Ethiopia). Irrigation and Drainage, 66(3):305-315. [doi: https://doi.org/10.1002/ird.2114]
Lakes ; Expansion ; Groundwater table ; Water quality ; Salinity ; Waterlogging ; Irrigation schemes ; Sustainability ; Landsat ; Imagery ; Spatial distribution ; Mapping ; Soil quality ; Soil fertility ; Agricultural production ; Sugarcane ; Plantations ; Productivity / Ethiopia / Lake Basaka / Awash River Basin / Matahara Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H048188)
(0.91 MB)
The Matahara Sugar Estate (MSE), after nearly 60 years of irrigation, is experiencing the effects of waterlogging and salinization in some fields. The problem is believed to be the result of the expansion of (saline and alkaline) Lake Basaka towards the plantation fields. The objective of this study was to determine the geometry of the lake (area and shape) in roughly the past half- century (1957–2015) from both Landsat images and local information and then assess its negative effects on MSE’s soil and water quality. Monthly groundwater (GW) depth was monitored using piezometer tubes. Water and soil samples were collected from each of the piezometer locations and analysed for important physico-chemical parameters. The results indicate that the lake expanded approximately 47.3 km2 in the past half-century. The soil quality was found to be very poor in plantation sections with very shallow GW depth and severe salinity conditions. The lake, as revealed by the results, is intruding into the groundwater system of MSE on the Abadir side. Assuming continuation of the past trends, the lake is expected to inundate parts of MSE in the next few years and, hence, challenge the production and productivity of MSE significantly. The lake has the potential to join the Awash River, thereby impacting all downstream irrigation developments in the basin and the livelihood of the people depending on the water resources. As the area is situated in the uppermost part of Main Ethiopian Rift Valley, other factors are expected to exacerbate its expansion even in the future. Overall, the study results present the potential damage caused by the lake to MSE and provides valuable information for the reclamation measures to be taken for the sustainability of MSE.
9 Zhang, Y.; Chen, G.; Vukomanovic, J.; Singh, K. K.; Liu, Y.; Holden, S.; Meentemeyer, R. K. 2020. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247:111945. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111945]
Land cover mapping ; Imagery ; Urban development ; Landscape ; Remote sensing ; Semantic standard ; Databases ; Models ; Suburban areas / USA / North Carolina / Raleigh / Durham / Chapel Hill
(Location: IWMI HQ Call no: e-copy only Record No: H049774)
(7.14 MB)
Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.
10 Pudasainee, A.; Chaulagain, B. P. 2020. Prospects of ICT based agricultural technology in Nepal. Nepalese Journal of Agricultural Sciences, 19:223-235.
Precision agriculture ; Information and Communication Technologies ; Unmanned aerial vehicles ; Imagery ; Geographical information systems ; Satellites ; Plant health ; Fertilizers ; Decision making ; Agroindustrial sector ; Farmers ; Models / Nepal
(Location: IWMI HQ Call no: e-copy only Record No: H049813)
(0.41 MB)
This cloud agriculture system (CAS) combines drone assisted diagnostics and prescription agriculture (DADAPA), value addition to agriculture produce (VAAP) and cloud market system (CMS). The CAS can be an advantage to the country where lack of trained agriculture service providers, complex geographical patterns and rain fed farming hinders the crop productivity. The low-cost drone imagery and data based analysis as DADAPA in combination with VAAP and CMS will have a significant impact for the upliftment of farming community in Nepal. The CAS can supplement wet bench laboratory and skilled agriculture services. That addresses insufficient market information system and the knowledge in value addition to crops and vegetables. It provides services to farmers by prescribing solutions accurately for problems like irrigation, weed management, plant health and growth, soil nutrition and fertilizers applications, diagnosing different diseases as precision agriculture. Information on agri-products and price in the market develops confidence and income to farmers and wholesalers.
11 Kleinschroth, F.; Banda, K.; Zimba, H.; Dondeyne, S.; Nyambe, I.; Spratley, S.; Winton, R. S. 2022. Drone imagery to create a common understanding of landscapes. Landscape and Urban Planning, 228:104571. [doi: https://doi.org/10.1016/j.landurbplan.2022.104571]
Drones ; Imagery ; Landscape ; Land use ; Stakeholders ; Nexus approaches ; Water power ; Biodiversity ; Non-governmental organizations / Southern Africa / Zambia / Zambezi River
(Location: IWMI HQ Call no: e-copy only Record No: H051509)
https://www.sciencedirect.com/science/article/pii/S0169204622002201/pdfft?md5=ccc09f89f85f9ce4d83c59511659de55&pid=1-s2.0-S0169204622002201-main.pdf
https://vlibrary.iwmi.org/pdf/H051509.pdf
https://vlibrary.iwmi.org/pdf/H051509.pdf
(17.40 MB) (17.4 MB)
Negotiated solutions among contrasting land use interests in the nexus of water, energy, food and ecosystems require cooperation between actors with different viewpoints and backgrounds. We suggest aerial imagery and videos, captured by drones, to be “boundary objects”, easily interpretable landscape representations that might create a common understanding across stakeholders through their universal interpretability. We collected drone imagery and videos from different angles of a wide range of landscapes in Zambia, showing agricultural areas, forests, wetlands and water infrastructure. Then, we took the imagery back to the field to probe the perceptions of multiple stakeholders, including staff from both governmental and non-governmental organizations, hydropower operators, small- and large-scale farmers. In focus group discussions, we assessed the interpretability of oblique images, taken at an angle by a video drone, compared to nadir (vertical) imagery from Google Earth and from a high-end mapping drone. We show that oblique images produced better identification results across all groups of stakeholders, but especially from small-scale farmers, suggesting this type of imagery is helpful to empower people who lack previous experience in interpreting nadir images. Overall, the appreciation of the aesthetic value and the perceived professional benefits of drone imagery are high, but technical and legal barriers impede a wider adoption of the technology. While we highlight ethical concerns and technical limitations, we suggest that conservationists and environmental planners could benefit from a critical use of affordable video drones so as to produce intuitive landscape representations useful for more effective multi-stakeholder collaborations.
12 Mabhaudhi, Tafadzwanashe; Bangira, T.; Sibanda, M.; Cofie, Olufunke. 2022. Use of drones to monitor water availability and quality in irrigation canals and reservoirs for improving water productivity and enhancing precision agriculture in smallholder farms. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on West and Central African Food Systems Transformation. 36p.
Water availability ; Water quality ; Monitoring ; Irrigation canals ; Reservoirs ; Water productivity ; Precision agriculture ; Smallholders ; Unmanned aerial vehicles ; Imagery ; Remote sensing ; Floods ; Mapping ; Water levels ; Parameters
(Location: IWMI HQ Call no: e-copy only Record No: H051656)
(735 KB)
The report provides a methodology protocol for measuring temporal and spatial changes in water quantity and quality using drone imagery. The procedure is informed by the need for effective and sustainable water resource use to enhance water productivity under climate change. It is based on a literature review that allows the identification of appropriate processes, materials, and procedures for water monitoring, including mapping spatial and temporal dynamics of reservoirs, measurement of water quality parameters, and flood mapping of irrigation canals.
13 Tariq, A.; Qin, S. 2023. Spatio-temporal variation in surface water in Punjab, Pakistan from 1985 to 2020 using machine-learning methods with time-series remote sensing data and driving factors. Agricultural Water Management, 280:108228. [doi: https://doi.org/10.1016/j.agwat.2023.108228]
Remote sensing ; Machine learning ; Surface water ; Landsat ; Imagery ; Semiarid zones ; Anthropogenic factors ; Satellite imagery ; Rainfall ; Drought ; Climate change ; Water management / Pakistan / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H052068)
https://www.sciencedirect.com/science/article/pii/S0378377423000938/pdfft?md5=2a94e87efaa0408a92698fe702ea8835&pid=1-s2.0-S0378377423000938-main.pdf
https://vlibrary.iwmi.org/pdf/H052068.pdf
https://vlibrary.iwmi.org/pdf/H052068.pdf
(9.08 MB) (9.08 MB)
Pakistan is home to many natural and artificial bodies of water, which are inevitable for agriculture, domestic use, recreation, etc. In the arid, semi-arid, and wet areas of the land, the distribution, spatio-temporal variations, and the impacts on water dynamics of climate and anthropogenic drivers were studied. In this study, we used Landsat, 5, 7, and 8 satellite images to generate annual and seasonal water frequency maps per pixel for the 1985–2020 period. The analysis findings have shown substantial inter-and intra-annual variability in rainfall and temperature. In addition, results revealed significant zonal disparities in water patterns, with the arid zone displaying the most drastic variations. We found out that the area (2530.42 km2) is occupied by water bodies, of which 1322.24 km2 (52.25%) seasonal and 1208.18 km2 (47.75%) are permanent water zones. There is a dramatic decline rate of 1.02 ± 1.84 km2/year in contrast to permanent water (0.97 ± 1.99 km2/year), total inland seasonal water has increased. During July and August, Punjab has the highest seasonal water area (1822.30 km2) and declines to the lowest (523.20 km2) in October to December and February to May when permanent water (708.12 km2) is greater than that of seasonal water. Gross Domestic Product (GDP) and rainfall were positively related to the surface water areas, while the temperature was inversely related. The outcome of our research offers valuable insights into future spatio-temporal variations, the supply of surface water in Punjab in the context of such anthropogenic and climate change activities.
14 Farooq, M.; Mushtaq, F.; Yousuf, U. 2024. Estimation of loss in arable land and irrigation requirements using high-resolution imagery and google earth engine. Irrigation and Drainage, 17p. (Online first) [doi: https://doi.org/10.1002/ird.2931]
Irrigation ; Irrigated land ; Land use ; Land cover ; Resolution ; Imagery ; Water resources ; Water management ; Treaties ; Food security ; Farmland ; Vegetation ; Satellite imagery ; Water allocation ; Urbanization / India / Pakistan / Indus Basin / Padshahi Canal
(Location: IWMI HQ Call no: e-copy only Record No: H052542)
(10.20 MB)
Water resources planning and management are critical in intricate basins such as the Indus Basin, shared by India and Pakistan under the Indus Water Treaty (IWT) for food security, conserving the environment, sustainable economic development and supporting livelihoods. The present study assesses arable land loss within the Padshahi and Sindh Extension (SE) canal catchments over 54 years, utilizing high-resolution satellite imagery and Google Earth Engine's normalized difference vegetation index (NDVI) derivations for strategizing irrigation efficiency, minimizing water loss and ensuring sustainable utilization of limited water resources under the IWT. Results revealed that irrigated land has decreased from 5127 ha (1966) to 3501 ha (2020) in both canals. The Padshahi canal sees substantial loss (1278 ha), primarily due to the highest transitions from agricultural land/crop land (-69%) to built-up areas. The SE canal, experiencing shifts to horticulture and plantation, records relatively fewer changes in built-up areas (348 ha). The monthly variation in the NDVI clearly depicted the high demand for irrigation to cater to agricultural lands with the onset of the sowing season for paddy in the Padshahi (1900 ha) and SE (2600 ha) canals in May.
15 Gokool, S.; Mahomed, M.; Brewer, K.; Naiken, V.; Clulow, A.; Sibanda, M.; Mabhaudhi, Tafadzwanashe. 2024. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure. Heliyon, 10(5):E26913. [doi: https://doi.org/10.1016/j.heliyon.2024.e26913]
Crops ; Mapping ; Unmanned aerial vehicles ; Imagery ; Machine learning ; Smallholders ; Farmers ; Land use ; Land cover / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052587)
https://www.cell.com/action/showPdf?pii=S2405-8440%2824%2902944-X
https://vlibrary.iwmi.org/pdf/H052587.pdf
https://vlibrary.iwmi.org/pdf/H052587.pdf
(6.41 MB) (6.41 MB)
Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.
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