Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics

Wheat is the most widely cultivated crop in the world providing critical food source of most countries. It exceeds most of the grain crops in acreage and production because of its ability to grow in wide range of climatic and geographic conditions. Timely and reliable information on wheat acreages is essential for government services in order to formulate their policies for planning of agricultural production and monitoring their food supply. Traditionally, agricultural statistics is considered as the main source of such information. Unfortunately, existing statistical data of wheat acreages of Turkey, mostly dependent on farmers’ declarations, does not provide spatial information of where this crop specifically is grown. Satellite remote sensing technology can enable the acquisition of such information indirectly with the use of ancillary data of crop statistics. This study aims to determine wheat cultivation areas of Turkey as percentage per unit area in a crop map by integrating time series satellite NDVI imagery with the official crop statistics through regression analysis. The regression results indicated that satellite data explained 95.8% of the variability in official wheat crop statistics and actual wheat cropping areas were significantly related to NDVI-based wheat classes. Validation of the produced wheat map showed that there was good agreement between actual wheat fractions and estimated NDVI-based wheat fractions explaining approximately 69% (Adj. R2) of the total variability between them. This study suggests use of the methodology employed here to governing bodies that need to identify and to map current wheat cropping areas

Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach that goes beyond the use of quality flags and upper envelope filtering is tested to detect when and where long-duration clouds are responsible for unreliable NDVI readings, so that a user can flag those data as missing. The study is based on MODIS Terra and the combined Terra-Aqua 16-day NDVI product for the south of Ghana, where persistent cloud cover occurs throughout the year. The combined product could be assumed to have less cloud contamination, since it is based on two images per day. Short-duration cloud effects were removed from the two products through using the adaptive Savitzky–Golay filter. Then for each ‘cleaned’ product an unsupervised classified map was prepared using the ISODATA algorithm, and, by class, plots were prepared to depict changes over time of the means and the standard deviations in NDVI values. By comparing plots of similar classes, long-duration cloud contamination appeared to display a decline in mean NDVI below the lower limit 95% confidence interval with a coinciding increase in standard deviation above the upper limit 95% confidence interval. Regression analysis was carried out per NDVI class in two randomly selected groups in order to statistically test standard deviation values related to long-duration cloud contamination. A decline in seasonal NDVI values (growing season) were below the lower limit of 95% confidence interval as well as a concurrent increase in standard deviation values above the upper limit of the 95% confidence interval were noted in 34 NDVI classes. The regression analysis results showed that differences in NDVI class values between the Terra and the Terra-Aqua imagery were significantly correlated (p < 0.05) with the corresponding standard deviation values of the Terra imagery in case of all NDVI classes of two selected NDVI groups. The method successfully detects long-duration cloud contamination that results in unreliable NDVI values. The approach offers scientists interested in time series analysis a method of masking by area (class) the periods when pre-cleaned NDVI values remain affected by clouds. The approach requires no additional data for execution purposes but involves unsupervised classification of the imagery to carry out the evaluation of class-specific mean NDVI and standard deviation values over time

Improved drought detection to support crop insurance models: powerpoint

Anomaly assessment for drought monitoring, as required for index insurance applications, is commonly done by comparing actual NDVI measurements against their historical records on a pixel-by-pixel basis. Limited years of satellite records with operational real-time availability result in time-series with a relative low count in annual repeats, e.g., the VEGETATION sensor onboard SPOT and Proba-V has completed at present only 19 full annual repeats. This number is too low for agricultural index insurance models that require accurate assessments of impacts of perils (e.g. of drought) on crop performance during a specific growing season. Generally, they want to see at least 30 annual repeats. Then, considering that the index models typically focus on the left-tail part of the NDVI frequency-distribution, to extract NDVI-thresholds that correspond to drought incidence statistics, the obtainable model accuracy further drops. Typical trigger points used by index insurance models concern the 15th and 5th percentile statistics, which implies that even with thirty annual repeats, these percentiles cannot be robustly estimated. Derived results are thus hardly acceptable to actuaries of insurance companies and financial institutes alike. In this paper, we propose an innovative way to assess NDVI anomalies that significantly increases the statistical basis for their calculation. Rather than focusing only on a single pixel, we make use of the fact that large clusters of pixels respond relatively homogeneously to anomalies in weather patterns. These clusters have a similar land cover and land use, which are determined by climatic conditions, including its variability, most notably drought frequency, duration and severity. The clusters can be derived with unsupervised classification by analyzing the similarity in their long-term NDVI-profiles. Using NDVI-measurements of all pixels in a cluster and all their annual repeats, allows accurate extraction of needed left-tail percentile statistics. Subsequently, seasonal anomalies for individual pixels are then compared against these percentiles. Based on this logic, we have designed an index insurance model that is currently implemented in Ethiopia, and that utilizes real-time Proba-V data as broadcasted through EUMETCast. The model showed an excellent correspondence with surveyed data on farmers experiences. An added value is that the presented approach can easily be scaled to other regions, and that it can also be utilized to benefit real-time drought early warning schemes

Characterising the nature of ocean-surface heterogeneity extracted from hypertemporal Earth Observation data

The ongoing temporal data "explosion" in Earth Observation imagery presents the oceanographic community with opportunities to analyse 30+ year archives of ocean surface measurements. While multiple hyper-temporal analysis tools exist for terrestrial applications, unsupervised data-driven methods are needed to extract and identify spatio-temporal ocean surface patterns. In particular, heterogeneity mapping of the ocean surface is needed to underpin time series analysis of satellite-derived ocean parameters, such as sea surface temperature and chlorophyll. Ultimately, derived products must be of benefit to the oceanographic and marine ecology communities, with clear meaningful guidance on their contents and how to interpret them. The Ocean-surface Heterogeneity MApping (OHMA) algorithm was developed to extract annual heterogeneity summaries from hyper-temporal datasets of GHRSST multi-sensor SST data. The OHMA algorithm produces a classification ensemble relying on optimised iterations of ISODATA classification. The ensembles highlight spatio-temporally homogeneous and heterogeneous ocean regions. The approach is entirely data-driven to meet current limitations when working with hyper-temporal ocean data, and to overcome challenges of obtaining ocean surface in-situ data which drive more supervised approaches. The OHMA map is validated for 2011 using measures of in-situ spatio-temporal heterogeneity from transect data. This approach can help optimise in-situ sampling efforts, with significant cost savings. It is critical to provide guidance to oceanographic and ecology users on how to interpret the features an OHMA map presents. Efforts undertaken to clarify interpretation of an OHMA map, through comparing the heterogeneity features highlighted in the OHMA outputs to SST front summaries derived from SST data, clearly demonstrated that heterogeneous regions cannot be explained primarily by the presence and magnitude of SST fronts. The spatio-temporal characteristics of other mesoscale features, such as eddies and surface currents, may also influence the features expressed in an OHMA output which are of interest to end user communities. Measuring the contribution of the various mesoscale features to a region's heterogeneity, therefore requires a multi-feature (and therefore multi-variable) synergistic approach

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Author Posting.© The Author(s), 2015. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 174 (2016): 44-55, doi:10.1016/j.rse.2015.12.003.Pastoralist households across East Africa face major livestock losses during drought periods that can cause persistent poverty. For Kenya and southern Ethiopia, an existing index insurance scheme aims to reduce the adverse effects of such losses. The scheme insures individual households through an area-aggregated seasonal forage scarcity index derived from remotely-sensed normalized difference vegetation index (NDVI) time series. Until recently, insurance contracts covered animal losses and indemnity payouts were consequently made late in the season, based on a forage scarcity index incorporating both wet and dry season NDVI data. Season timing and duration were fixed for the whole area (March-September for long rains, October-February for short rains). Due to demand for asset protection insurance (pre-loss intervention) our aim was to identify earlier payout options by shortening the temporal integration period of the index. We used 250m-resolution 10-day NDVI composites for 2001-2014 from the Moderate Resolution Imaging Spectroradiometer (MODIS). To better describe the period during which forage develops, we first retrieved per-pixel average season start- and end-dates using a phenological model. These dates were averaged per insurance unit to obtain unit-specific growing period definitions. With these definitions a new forage scarcity index was calculated. We then examined if shortening the temporal period further could effectively predict most (>90%) of the interannual variability of the new index, and assessed the effects of shortening the period on indemnity payouts. Our analysis shows that insurance payouts could be made one to three months earlier as compared to the current index definition, depending on the insurance unit. This would allow pastoralists to use indemnity payments to protect their livestock through purchase of forage, water, or medicines.AV was funded under a contract from the International Livestock Research Institute. CCU was supported by the U.S. National Science Foundation under grant OCE-1203892.2016-12-1

Şeref madalyası

Taha Toros Arşivi, Dosya No: 34-Safiye-Hüseyin ElbiUnutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010

Historical extension of operational NDVI products for livestock insurance in Kenya

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index