Your search found 8 records
1 Gumma, Murali Krishna; Thenkabail, P. S.; Velpuri, N. M. 2009. Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin. In Bloschl, G.; van de Giesen, N.; Muralidharan, D.; Ren, L.; Seyler, F.; Sharma, U.; Vrba, J. (Eds.). Improving integrated surface and groundwater resources management in a vulnerable and changing world: proceedings of Symposium JS.3 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH), Hyderabad, India, 6-12 September 2009. Wallingford, UK: International Association of Hydrological Sciences (IAHS) pp.271-281. (IAHS Publication 330)
Vegetation ; Phenology ; River basins ; Vegetation ; Maps ; Land cover ; Land use ; Groundwater irrigation ; Surface irrigation ; Canals ; Reservoirs ; Irrigated land ; Time series analysis ; Remote sensing / India / Krishna River basin
(Location: IWMI HQ Call no: e-copy only Record No: H042217)
(1.15 MB)
This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000–2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern, depicting phenology, obtained for the irrigated classes enabled demarcation between: (a) irrigated surface-water double crop, (b) irrigated surface-water continuous crop, and (c) irrigated groundwater mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with groundwater due to the continuity of water supply. Surface water availability, however, 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 (IDBL) and light irrigation (IL) 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 groundwater-irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of nine distinct classes consisting of irrigated, rainfed, and other land-use classes were derived using MODIS 250-m near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-metre to 4 m) 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 Joshi, P. K.; Priyanka, N.; Amarnath, Giriraj. 2011. Geospatial tools to assess forest ecosystems under climate change trajectories. In Joshi, P. K.; Singh, T. P. (Eds.). Geoinformatics for climate change studies. New Delhi, India: The Energy and Resources Institute (TERI) pp.129-176.
Remote sensing ; GIS ; Climate change ; Forests ; Ecosystems ; Phenology ; Ecology ; Greenhouse gases ; Models ; Vegetation ; Mountains ; Wildfires ; Invasive species ; Global warming ; Environmental temperature ; Land use ; Land cover / Nepal / Eastern Himalayas
(Location: IWMI HQ Call no: 621.3678 G000 JOS Record No: H044291)
(4.65 MB)
3 Joshi, P. K.; Singh, T. P. 2011. Geoinformatics for climate change studies. New Delhi, India: The Energy and Resources Institute (TERI). 470p.
Remote sensing ; GIS ; Climate change ; Environmental temperature ; Global warming ; Models ; Mountains ; Glaciers ; Forests ; Ecosystems ; Phenology ; Mapping ; Sea level ; Water management ; Evapotranspiration ; Land degradation ; Satellite imagery ; Natural disasters ; Landslides ; Flooding ; Wildfires ; Risk reduction ; Research ; Greenhouse gases ; Vegetation ; Invasive species ; River basins ; Health hazards ; Waterborne diseases ; Diarrhoea ; Malaria ; Land degradation ; Data analysis / South Africa / Nigeria / Bangladesh / Morocco / Germany / Thailand / Malaysia / Australia / Eastern Cape Province / Mooi River Basin / Weida River Basin / Murray Darling River Basin / Thuringia / Chang Mai / Kanchanaburi
(Location: IWMI HQ Call no: 621.3678 G000 JOS Record No: H044290)
(0.33 MB)
4 Sehgal, V. K.; Jain, S.; Aggarwal, P. K.; Jha, S. 2011. Deriving crop phenology metrics and their trends using times series NOAA-AVHRR NDVI data. Journal of the Indian Society of Remote Sensing, 39(3):373-381. [doi: https://doi.org/10.1007/s12524-011-0125-z]
Remote sensing ; Time series analysis ; Climate change ; Crop production ; Seasonal cropping ; Phenology ; Vegetation ; Indicators / India / Indo-Gangetic Plains
(Location: IWMI HQ Call no: e-copy only Record No: H044601)
(0.64 MB)
In this study, an attempt has been made to derive the spatial patterns of temporal trends in phenology metrics and productivity of crops grown, at disaggregated level in Indo-Gangetic Plains of India (IGP), which are helpful in understanding the impact of climatic, ecological and socio-economic drivers. The NOAA-AVHRR NDVI PAL dataset from 1981 to 2001 was stacked as per the crop year and subjected to Savitzky-Golay filtering. For crop pixels, maximum and minimum values of normalized difference vegetation index (NDVI), their time of occurrence and total duration of kharif (June-October) and rabi (November–April) crop seasons were derived for each crop year and later subjected to pixel-wise regression with time to derive the rate and direction of change. The maximum NDVI value showed increasing trends across IGP during both kharif and rabi seasons indicating a general increase in productivity of crops. The trends in time of occurrence of peak NDVI during kharif dominated with rice showed that the maximum vegetative growth stage was happening early with time during study period across most of Punjab, North Haryana, Parts of Central and East Uttar Pradesh and some parts of Bihar and West Bengal. Only central parts of Haryana showed a delay in occurrence of maximum vegetative stage with time. During rabi, no significant trends in occurrence of peak NDVI were observed in most of Punjab and Haryana except in South Punjab and North Haryana where early occurrence of peak NDVI with time was observed. Most parts of Central and Eastern Uttar Pradesh, North Bihar and West Bengal showed a delay in occurrence of peak NDVI with time. In general, the rice dominating system was showing an increase in duration with time in Punjab, Haryana, Western Uttar Pradesh, Central Uttar Pradesh and South Bihar whereas in some parts of North Bihar and West Bengal a decrease in the duration with time was also observed. During rabi season, except Punjab, the wheat dominating system was showing a decreasing trend in crop duration with time.
5 Kiptala, J. K.; Mohamed, Y.; Mul, Marloes L.; Cheema, M. J. M.; Van der Zaag, P. 2013. Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, eastern Africa. Physics and Chemistry of the Earth, 66:112-122. [doi: https://doi.org/10.1016/j.pce.2013.08.002]
Land use ; Land cover ; Mapping ; Land classification ; Land suitability ; Phenology ; Vegetation ; River basins ; Water resources ; International waters ; Rain ; Remote sensing ; Irrigated farming ; Rainfed farming ; Calibration / Eastern Africa / Tanzania / Kenya / Upper Pangani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046232)
(3.30 MB)
In arid and semi-arid areas, evaporation fluxes are the largest component of the hydrological cycle, with runoff coefficient rarely exceeding 10%. These fluxes are a function of land use and land management and as such an essential component for integrated water resources management. Spatially distributed land use and land cover (LULC) maps distinguishing not only natural land cover but also management practices such as irrigation are therefore essential for comprehensive water management analysis in a river basin. Through remote sensing, LULC can be classified using its unique phenological variability observed over time. For this purpose, sixteen LULC types have been classified in the Upper Pangani River Basin (the headwaters of the Pangani River Basin in Tanzania) using MODIS vegetation satellite data. Ninety-four images based on 8 day temporal and 250 m spatial resolutions were analyzed for the hydrological years 2009 and 2010. Unsupervised and supervised clustering techniques were utilized to identify various LULC types with aid of ground information on crop calendar and the land features of the river basin. Ground truthing data were obtained during two rainfall seasons to assess the classification accuracy. The results showed an overall classification accuracy of 85%, with the producer’s accuracy of 83% and user’s accuracy of 86% for confidence level of 98% in the analysis. The overall Kappa coefficient of 0.85 also showed good agreement between the LULC and the ground data. The land suitability classification based on FAO-SYS framework for the various LULC types were also consistent with the derived classification results. The existing local database on total smallholder irrigation development and sugarcane cultivation (large scale irrigation) showed a 74% and 95% variation respectively to the LULC classification and showed fairly good geographical distribution. The LULC information provides an essential boundary condition for establishing the water use and management of green and blue water resources in the water stress Pangani River Basin.
6 Busetto, L.; Zwart, S. J.; Boschetti, M. 2019. Analysing spatial-temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the phenorice algorithm. International Journal of Applied Earth Observation and Geoinformation, 75:15-28. [doi: https://doi.org/10.1016/j.jag.2018.09.016]
Agricultural practices ; Rice ; Intensive cropping ; Time series analysis ; Satellite observation ; Monitoring ; Rivers ; Irrigated farming ; Estimation ; Phenology ; Moderate resolution imaging spectroradiometer / West Africa / Senegal River Valley
(Location: IWMI HQ Call no: e-copy only Record No: H049456)
(4.87 MB)
In this study we used the PhenoRice algorithm to track recent variations of rice cultivation practices along the Senegal River Valley. Time series of MODIS imagery with 250 m spatial resolution and a nominal 8-days frequency were used as input for the algorithm to map the spatial and temporal variations of rice cultivated area and of several important phenological metrics (e.g., crop establishment and harvesting dates, length of season) for the 2003–2016 period in both the dry and the wet rice cultivation seasons. Comparison between PhenoRice results and ancillary and field data available for the Senegal part of the study area showed that the algorithm is able to track the interannual variations of rice cultivated area, despite the total detected rice area being consistently underestimated. PhenoRice estimates of crop establishment and harvesting dates resulted accurate when compared with field observations available for two sub-regions for a period of 10 years, and thus allow assessing interannual variability and tracking changes in agronomic practices. An analysis of interannual trends of rice growing practices based on PhenoRice results highlighted a clear shift of rice cultivation from the wet to the dry season starting approximately from 2008. The shift was found to be particularly evident in the delta part of the SRV. Additionally, a statistically significant trend was revealed starting 2006 towards a longer dry season (r2 = 0.81; Slope = 1.24 days y-1) and a shorter wet season (r2 = 0.65; Slope = 0.53 days y-1). These findings are in agreement with expert knowledge of changes ongoing in the area. In particular the shorter wet season is attributed to shortage of labor and equipment leading to a delay in completion of harvesting operations in the dry season, which led to the adoption of short-duration rice varieties by farmers in the wet season to avoid risk of yield losses due to climatic constraints. Aforementioned results highlight the usefulness of the PhenoRice algorithm for providing insights about recent variations in rice cultivation practices over large areas in developing countries, where high-quality up to date information about changes in agricultural practices are often lacking.
7 Nandy, S.; Ghosh, Surajit; Singh, S. 2021. Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India. Environmental Monitoring and Assessment, 193(9):616. [doi: https://doi.org/10.1007/s10661-021-09356-9]
Forests ; Phenology ; Climatic factors ; Shorea robusta ; Moderate resolution imaging spectroradiometer ; Time series analysis ; Remote sensing ; Temperature ; Rain ; Vegetation index / India / Assam / Chhattisgarh / Jharkhand / Madhya Pradesh / Meghalaya / Uttarakhand / West Bengal
(Location: IWMI HQ Call no: e-copy only Record No: H050795)
(2.27 MB)
Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002–2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R2) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R2 values were found higher (R2 = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India.
8 Li, R.; Xia, H.; Zhao, X.; Guo, Y. 2023. Mapping evergreen forests using new phenology index, time series sentinel-1/2 and google earth engine. Ecological Indicators, 149:110157. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2023.110157]
(Location: IWMI HQ Call no: e-copy only Record No: H051848)
https://www.sciencedirect.com/science/article/pii/S1470160X23002996/pdfft?md5=f4a27438103256821cf55727aaf90c29&pid=1-s2.0-S1470160X23002996-main.pdf
https://vlibrary.iwmi.org/pdf/H051848.pdf
https://vlibrary.iwmi.org/pdf/H051848.pdf
(25.50 MB) (25.5 MB)
Evergreen forests are sensitive to climate change and their role in the exchange of carbon, water, and energy in terrestrial ecosystems is irreplaceable. The temporal and spatial variation of the evergreen forest is closely related to forest monitoring and management in China, which has a far-reaching impact on forest protection and sustainable development. However, the lack of annual fine resolution maps of evergreen forests limits our exploration of the evolution of the spatiotemporal patterns of evergreen forests. Therefore, it is necessary to timely and accurately maps evergreen forests with high spatial resolution. We used a new phenology index of NDVImax-NDVIwinter_max to identify evergreen forests at the 10-m scale. First, we mapped annual 2019 land cover types to obtain the forest mask. Second, we calculated the classification phenology index by extracting the difference between evergreen forests and deciduous forests in phenological characteristics. Finally, we extracted the evergreen forest in 2019 from the annual forest map based on the constructed phenological indicators. The kappa coefficient of our annual forest map for 2019 was 0.92, and the overall, producer and user accuracies were 97.98%, 90.09%, and 97.46%, respectively. The kappa coefficient of the annual evergreen forest map in 2019 was 0.98, and the overall, producer and user accuracies were 99.73%, 97.84%, and 99.68%, respectively. Our study shows that the new phenological index can identify and map evergreen forests on complex landforms dominated by evergreen-deciduous mixed forests, which can be applied to other regions and years in China. The results of this study have reference value for evaluating the spatial distribution and resource management of evergreen forests.
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