Your search found 9 records
1 Gumma, M. K.; Thenkabail, P. S.; Nelson, A.. 2011. Mapping irrigated areas using MODIS 250 meter time-series data: a study on Krishna River Basin (India) Water, 3(1):113-131. [doi: https://doi.org/10.3390/w3010113]
Groundwater irrigation ; Surface irrigation ; Irrigated sites ; Mapping ; Land use ; Land cover ; Remote sensing ; Models ; Time series analysis ; River basins / India / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H043483)
(3.70 MB)
Mapping irrigated areas of a river basin is important in terms of assessing water use and food security. This paper describes an innovative remote sensing based vegetation phenological approach to map irrigated areas and then the differentiates the ground water irrigated areas from the surface water irrigated areas in the Krishna river basin (26,575,200 hectares) in India using MODIS 250 meter every 8-day near continuous time-series data for 2000–2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern obtained in irrigated classes enabled demarcation between: (a) irrigated surface water double crop, (b) irrigated surface water continuous crop, and (c) irrigated ground water mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with ground water due to the continuity of water supply. Surface water availability, on the other hand, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double cropped and light irrigation have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and ground water irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of 9 distinct classes consisting of Irrigated, rainfed, and other land use classes were also derived using MODIS 250 meter near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-meter to 4 meter) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86).
2 Gumma, M. K.; Thenkabail, P. S.; Hideto, F.; Nelson, A.; Dheeravath, V.; Busia, D.; Rala, A. 2011. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sensing, 3(4):816-835. [doi: https://doi.org/10.3390/rs3040816]
Remote sensing ; Methodology ; Mapping ; Irrigated land ; Irrigated farming ; Land use ; Land cover ; Satellite imagery ; Statistics / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H044267)
(1.69MB)
Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.
3 Tuong, T. P.; Humphreys, E.; Khan, Z. H.; Nelson, A.; Mondal, M.; Buisson, Marie-Charlotte; George, P. 2014. Messages from the Ganges Basin development challenge: unlocking the production potential of the polders of the coastal zone of Bangladesh through water management investment and reform. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). 32p.
Water management ; Water resources ; Water governance ; Coastal area ; River basin development ; Investment ; Cropping systems ; Drainage ; Climate change ; Dry season ; Wet season ; Aquaculture ; Agriculture ; Reclaimed land / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H046498)
http://r4d.dfid.gov.uk/pdf/outputs/WaterfoodCP/CPWF-Ganges-basin-messages-final.pdf
https://vlibrary.iwmi.org/pdf/H046498.pdf
https://vlibrary.iwmi.org/pdf/H046498.pdf
(1.16 MB)
4 Chandna, P. K.; Nelson, A.; Khan, M. Z. H.; Hossain, M. M.; Rana, M. S.; Mondal, M.; Mohanty, S.; Humphrey, L.; Rashid, F.; Tuong, T. P. 2015. Targeting improved cropping systems in the coastal zone of Bangladesh: a decision tree approach for mapping recommendation domains. In Humphreys, E.; Tuong, T. P.; Buisson, Marie-Charlotte; Pukinskis, I.; Phillips, M. (Eds.). Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21-23 October 2014. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). pp.522-541.
Cropping systems ; Agricultural development ; Coastal area ; Spatial distribution ; Analysis ; Mapping ; Land use ; High yielding varieties ; Rice ; Seasonal cropping ; Transplanting ; Water management ; Surface water ; Groundwater ; Soil salinity ; Brackish water ; Aquaculture ; Shrimp culture ; Reclaimed land / Bangladesh / Barisal
(Location: IWMI HQ Call no: IWMI Record No: H047211)
https://cgspace.cgiar.org/bitstream/handle/10568/66389/Revitalizing%20the%20Ganges%20Coastal%20Zone%20Book_Low%20Version.pdf?sequence=1
https://vlibrary.iwmi.org/pdf/H047211.pdf
https://vlibrary.iwmi.org/pdf/H047211.pdf
(0.65 MB) (11.9 MB)
5 Gumma, M. K.; Kajisa, K.; Mohammed, I. A.; Whitbread, A. M.; Nelson, A.; Rala, A.; Kuppannan, Palanisami. 2015. Temporal change in land use by irrigation source in Tamil Nadu and management implications. Environmental Monitoring and Assessment, 187(1):1-17. [doi: https://doi.org/10.1007/s10661-014-4155-1]
Land use ; Land cover ; Groundwater irrigation ; Irrigated sites ; Irrigation canals ; Tank irrigation ; Spectral analysis ; Rain ; Crop management ; River basins ; Agriculture ; Remote sensing / India / Tamil Nadu
(Location: IWMI HQ Call no: e-copy only Record No: H047509)
(6.45 MB)
Interannual variation in rainfall throughout Tamil Nadu has been causing frequent and noticeable land use changes despite the rapid development in groundwater irrigation. Identifying periodically water-stressed areas is the first and crucial step to minimizing negative effects on crop production. Such analysis must be conducted at the basin level as it is an independent water accounting unit. This paper investigates the temporal variation in irrigated area between 2000–2001 and 2010–2011 due to rainfall variation at the state and sub-basin level by mapping and classifying Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite satellite imagery using spectral matching techniques. A land use/land cover map was drawn with an overall classification accuracy of 87.2 %. Area estimates between the MODISderived net irrigated area and district-level statistics (2000–2001 to 2007–2008) were in 95 % agreement. A significant decrease in irrigated area (30–40 %) was observed during the water-stressed years of 2002–2003, 2003–2004, and 2009–2010. Major land use changes occurred three times during 2000 to 2010. This study demonstrates how remote sensing can identify areas that are prone to repeated land use changes and pin-point key target areas for the promotion of drought-tolerant varieties, alternativewater management practices, and new cropping patterns to ensure sustainable agriculture for food security and livelihoods.
6 del Rio-Mena, T.; Willemen, L.; Tesfamariam, G. T.; Beukes, O.; Nelson, A.. 2020. Remote sensing for mapping ecosystem services to support evaluation of ecological restoration interventions in an arid landscape. Ecological Indicators, 113:106182. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106182]
Ecosystem services ; Ecological control ; Remote sensing ; Arid zones ; Normalized difference vegetation index ; Revegetation ; Earth observation satellites ; Geographical information systems ; Essential oils ; Biomass ; Thicket ; Forage ; Land degradation ; Erosion control ; Water flow ; Regulations ; Livestock ; Indicators ; Models / South Africa / Baviaanskloof Hartland Bawarea Conservancy
(Location: IWMI HQ Call no: e-copy only Record No: H049672)
(1.27 MB)
Considerable efforts and resources are being invested in integrated conservation and restoration interventions in rural arid areas. Empirical research for quantifying ecosystem services – nature’s benefits to people – is essential for evaluating the range of benefits of ecological restoration and to support its use in natural resource management. Satellite remote sensing (RS) can be used to monitor interventions, especially in large and remote areas. In this study we used field measurements, RS-based information from Sentinel-2 imagery together with soil and terrain data, to estimate ecosystem service supply and evaluate integrated ecological restoration interventions. We based our research on the arid, rural landscape of the Baviaanskloof Hartland Bawarea Conservancy, South Africa, where several integrated interventions have been implemented in areas where decades of small livestock farming has led to extensive land degradation. Interventions included i) long term livestock exclusion, ii) revegetating of degraded areas, iii) a combination of these two, and iv) essential oil production as alternatives to goat and sheep farming. We assessed six ecosystem services linked to the objectives of the interventions: erosion prevention, climate regulation, regulation of water flows, provision of forage, biomass for essential oil production, and the sense of place through presence of native species. We first estimated the ecosystem service supply based on field measurements. Secondly, we explored the relationships between ecosystem services quantities derived from the field measurements with 13 Sentinel-2 indices and four soil and terrain variables. We then selected the best fitting model for each ecosystem service. Finally, we compared the supply of ecosystem services between intervened and non-intervened sites. Results showed that models based on Sentinel-2 indices, combined with slope information, can estimate ecosystem services supply in the study area even when the levels of field-based ecosystem services supplies are low. The RS-based models can assess ecosystem services more accurately when their indicators mainly depend on green vegetation, such as for erosion prevention and provision of forage. The agricultural fields presented high variability between plots on the provision of ecosystem services. The use of Sentinel-2 vegetation indices and terrain data to quantify ecosystem services is a first step towards improving the monitoring and assessment of restoration interventions. Our results showed that in the study area, livestock exclusion lead to a consistent increase in most ecosystem services.
7 Steinbach, S.; Cornish, N.; Franke, J.; Hentze, K.; Strauch, A.; Thonfeld, F.; Zwart, Sander J.; Nelson, A.. 2021. A new conceptual framework for integrating earth observation in large-scale wetland management in East Africa. Wetlands, 41(7):93. [doi: https://doi.org/10.1007/s13157-021-01468-9]
Wetlands ; Environmental management ; Earth observation satellites ; Sustainable use ; Food security ; Environmental protection ; Surface water ; Land use ; Land cover ; Ecosystems ; Large scale systems ; Decision making ; Spatial data / East Africa / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H050718)
https://link.springer.com/content/pdf/10.1007/s13157-021-01468-9.pdf
https://vlibrary.iwmi.org/pdf/H050718.pdf
https://vlibrary.iwmi.org/pdf/H050718.pdf
(5.27 MB) (5.27 MB)
Wetlands are abundant across the African continent and provide a range of ecosystem services on different scales but are threatened by overuse and degradation. It is essential that national governments enable and ensure the sustainable use of wetland resources to maintain these services in the long run. As informed management decisions require reliable, up-to-date, and large coverage spatial data, we propose a modular Earth observation-based framework for the geo-localisation and characterization of wetlands in East Africa. In this study, we identify four major challenges in spatial data supported wetland management and present a framework to address them. We then apply the framework comprising Wetland Delineation, Surface Water Occurrence, Land Use/Land Cover classification and Wetland Use Intensity for the whole of Rwanda and evaluate the ability of these layers to meet the identified challenges. The layers’ spatial and temporal characteristics make them combinable and the information content, of each layer alone as well as in combination, renders them useful for different wetland management contexts.
8 Steinbach, S.; Hentschel, E.; Hentze, K.; Rienow, A.; Umulisa, V.; Zwart, Sander J.; Nelson, A.. 2023. Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales. Ecological Informatics, 75:102032. [doi: https://doi.org/10.1016/j.ecoinf.2023.102032]
Wetlands ; Ecosystems ; Environmental health ; Assessment ; Remote sensing ; Indicators ; Earth observation satellites ; Datasets ; Land use ; Surface water ; Water quality ; Vegetation ; Gomorphology ; Satellite imagery / East Africa / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H051812)
https://www.sciencedirect.com/science/article/pii/S1574954123000614/pdfft?md5=37e51464f7fbd9d1321d786007b58ce3&pid=1-s2.0-S1574954123000614-main.pdf
https://vlibrary.iwmi.org/pdf/H051812.pdf
https://vlibrary.iwmi.org/pdf/H051812.pdf
(8.71 MB) (8.71 MB)
To avoid wetland degradation and promote sustainable wetlands use, decision-makers and managing institutions need quantified and spatially explicit information on wetland ecosystem condition for policy development and wetland management. Remote sensing holds a significant potential for wetland mapping, inventorying, and monitoring. The Wetland Use Intensity (WUI) indicator, which is not specific to a particular crop and which requires little ancillary data, is based on the Mean Absolute Spectral Dynamics (MASD), which is a cumulative measure of reflectance change across a time series of optical satellite images. It is sensitive to the compound effects of land cover changes caused by different agricultural practices, flooding or burning. The more frequent and intrusive management practices are on the land cover, the stronger the WUI signal. WUI thus serves as a surrogate indicator to measure pressure on wetland ecosystems.
We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks.
We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks.
9 del Rio-Mena, T.; Willemen, L.; Vrieling, A.; Nelson, A.. 2023. How remote sensing choices influence ecosystem services monitoring and evaluation results of ecological restoration interventions. Ecosystem Services, 64:101565. [doi: https://doi.org/10.1016/j.ecoser.2023.101565]
(Location: IWMI HQ Call no: e-copy only Record No: H052381)
https://www.sciencedirect.com/science/article/pii/S221204162300058X/pdfft?md5=116f6f50806abf95646d362c61188c07&pid=1-s2.0-S221204162300058X-main.pdf
https://vlibrary.iwmi.org/pdf/H052381.pdf
https://vlibrary.iwmi.org/pdf/H052381.pdf
(7.87 MB) (7.87 MB)
Large-scale ecological restorations are recognized worldwide as an effective strategy to combat environmental degradation and promote sustainability. Remote sensing (RS) imagery, such as obtained from Landsat and Sentinel-2 satellites, can provide spatial, spectral, and temporal information on ecosystem service supply to support monitoring and evaluation of restoration interventions. However, because of the abundance of satellite data and methodological analysis options, choices in data selection and processing options need to be made. This study explored the effect of RS choices on the evaluation of changes in ecosystem services as a result of ecological restoration interventions. Using the ecosystem service of forage provision for wildlife as an example, we used a before-after-control-impact (BACI) analysis to compare how the following choices affected restoration evaluation outcomes: a) different number of control pixels; b) different spatial distribution of control pixels; c) intra-annual image selection; and d) different reference periods. In addition, e) we evaluated the effect of using two different satellite sensor types, using the ecosystem service ‘erosion prevention’ as an example. We explored the effect of these five choices for restoration sites in the Baviaanskloof, South Africa. Results showed that the choice of intra-annual image selection, and the reference period describing the ‘before state’ had a strong effect on the outcomes, often leading to opposite BACI evaluation results. BACI results were less sensitive to choices related to the number of control points in the evaluation. The impact of methodological choices on the BACI outcomes was greater for the less degraded areas of our study site. Satellite sensor choice resulted in similar temporal trajectories of estimated supply. We demonstrated that RS choices have a strong effect on the evaluation results of restoration interventions. Therefore, we recommend that documenting the key RS choices results is essential when communicating restoration evaluation results in order to properly understand, manage and adapt restoration initiatives.
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