3,150 research outputs found
High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall, Enhanced Vegetation Index and Digital Elevation Model
Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. At last, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.JRC.H.1-Water Resource
Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel
In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security
Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa
CITATION: Munch, Z., et al. 2017. Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa. Geosciences, 7(1):7, doi:10.3390/geosciences7010007.The original publication is available at http://www.mdpi.comLand cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014), to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1) from existing data (2000); and (2) through object-based image analysis (2014) of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%), the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling.http://www.mdpi.com/2076-3263/7/1/7Publisher's versio
Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.
Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017.
To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared
Remote Sensing Applications to Support Locust Management and Research: Evaluating the Potential of Earth Observation for Locust Outbreaks in Different Regions
This dissertation focuses on satellite remote sensing applications for locust management and additional contributions to locust research. Specifically, the remote sensing-based characterization and interpretation of land surface cover and its dynamics are addressed with a special emphasis on the requirements of different locust species. At first, the aim of this dissertation is to provide a holistic overview of the existing applications using satellite data focusing on different locust species and thus, to present current and new opportunities. Furthermore, remote sensing and geospatial datasets are used in a model to categorize areas with ideal and less than ideal conditions for locust outbreaks. The benefit of up-to-date remote sensing data for preventive locust management is demonstrated using time-series-based Sentinel-2 land cover classification. Due to the diversity of the numerous locust species and their spatial distribution in different geographical locations, this research focuses mainly on two locust species, the Italian locust (Calliptamus italicus) and the Moroccan locust (Dociostaurus maroccanus), as well as on selected study areas within their extensive habitats, respectively. Both selected locust species caused numerous damages in Europe, the Caucasus, Central Asia and North Africa in the past. For both species, there is only a limited number of publications exploiting the capabilities of remote sensing methods. Therefore, this dissertation aims to explore the potential approaches of Earth observation datasets to support preventive locust management and research for both species.Die vorliegende Dissertation beschäftigt sich mit dem Einsatz der Satellitenfernerkundung im Bereich Heuschreckenmanagement und -forschung. Die fernerkundungsbasierte Charakterisierung und Interpretation der Landoberflächen-bedeckung und deren Dynamik stehen dabei - mit Fokus auf die Anforderungen der verschiedenen Heuschreckenarten - im Vordergrund. Ziel dieser Dissertation ist es zunächst, einen ganzheitlichen Überblick über vorhandene Anwendungen von Satellitendaten im Kontext Heuschreckenmanagement zu erarbeiten. Des Weiteren werden fernerkundungs- und geobasierten Datensätzen in einem Model verwendet, um Flächen mit idealen bzw. weniger idealen Bedingungen für Heuschreckenausbrüche zu kategorisieren. Der Vorteil von aktuellen Fernerkundungsdaten für präventives Heuschreckenmanagement wird anhand zeitreihenbasierten Sentinel-2 Landbedeckungsklassifikation demonstriert. Aufgrund der Vielfältigkeit der zahlreichen Heuschreckenarten und deren räumlicher Verteilung in verschiedenen geographischen Lagen, konzentriert sich diese Arbeit im Wesentlichen auf zwei Heuschreckenarten, die Italienische Schönschrecke (Calliptamus italicus) und die Marokkanische Wanderheuschrecke (Dociostaurus maroccanus), sowie auf ausgewählte Studiengebiete innerhalb deren weiträumigen Habitaten. Beide Heuschreckenarten verursachten zahlreiche Ausbrüche in der Vergangenheit mit Schäden in Europa, dem Kaukasus, Zentralasien und Nordafrika. Für beide Heuschreckenarten existieren nur wenige Forschungsarbeiten, die sich mit der Anwendung von Fern-erkundungsdaten auseinandersetzen. Vor diesem Hintergrund zielt diese Dissertation auf die Entwicklung von relevanten Methoden unter Einsatz von Fernerkundungsdaten für beide Heuschreckenarten ab, um präventives Heuschreckenmanagement und -forschung zu unterstützen.Данная диссертация раскрывает тему применения спутникового дистанционного зондирования для контроля саранчовых и проведения дополнительных исследований саранчи. В частности, особое внимание уделяется изучению потребностей различных видов саранчовых при описании характеристик земного покрова и его динамики на основе данных дистанционного зондирования. Первостепенная цель данной диссертации состоит в том, чтобы предоставить целостный обзор существующих приложений, использующих спутниковые данные, в разрезе различных видов саранчовых для того, чтобы раскрыть текущие и потенциальные возможности. Кроме того, дистанционное зондирование и наборы геопространственных данных используются для классификации территорий с идеальными и не идеальными условиями для нашествий саранчи. исследование сосредоточено в основном на двух видах саранчи, итальянского пруса (Calliptamus italicus) и марокканской саранче (Dociostaurus maroccanus), а также на определенных территориях, в пределах их обширногo местообитаний
Climatic, environmental and socio-economic factors for malaria transmission modelling in KwaZulu-Natal, South Africa.
Doctoral Degree. University of KwaZulu-Natal, Durban.Sub-Saharan Africa (SSA) largely bears the burden of the global malaria disease, with the transmission and intensity influenced by the interaction of a variety of climatic, environmental, socio-economic, and human factors. Other factors include parasitic and vectoral factors. In South Africa (SA) in general and KwaZulu-Natal (KZN) in particular, the change of the malaria control intervention policy in 2000, may be responsible for the significant progress over the past two decades in reducing malaria case report to near zero. Currently, malaria incidence in KZN is less than 1 case per 1000 persons at risk placing the province in the malaria elimination stage. To meeting the elimination target, it is necessary to study the dynamics of malaria transmission in KZN employing various analytical/statistical models. Thus, the aim of this study was to explore the factors that influence malaria transmission by employing different analytical models and approaches in a setting with low malaria endemicity and transmission. This involves a sound appraisal of the existing literature on the contribution of remote sensing technology in understanding malaria transmission, evaluation of existing malaria control intervention; delineation of empirical map of malaria risk; provide information on the climatic, environmental and socio-economic factors that influences malaria risk and transmission; and formulation of a relevant malaria forecast and surveillance models. The investigator started with a systemic review of studies in chapter two. The studies were aimed at identifying significant remotely-sensed climatic and environmental determinants of malaria transmission for modelling malaria transmission and risk in SSA via a variety of statistical approaches. Normalised difference vegetation index (NDVI) was identified as the most significant remotely-sensed climatic/environmental determinants of malaria transmission in SSA. Majority of the studies employed the generalised linear modelling approach compared to the Bayesian modelling approach. In the third chapter, malaria cases from the endemic areas of KZN with remotely-sensed climatic and environmental data were used to model the climatic and environmental determinants of malaria transmission and develop a malaria risk map in KZN. The spatiotemporal zero inflated Poisson model formulated indicates that at 95% Bayesian credible interval (BCI) NDVI (0.91; 95% BCI = 0.71, -1.12), precipitation (0.11; 95% BCI = 0.08, 0.14), elevation (0.05; 95% BCI = 0.032, 0.07) and night temperature (0.04; 95% BCI = 0.03, 0.04) are significantly related to malaria transmission in KZN, SA. The area with the highest risk of malaria morbidity in KZN was identified as the north-eastern part of the province. The fourth chapter was to establish the socio-economic status (SES) that influence malaria transmission in the endemic areas of KZN, by employing a Bayesian inference approach. The obtained posterior samples revealed that, significant association existed between malaria disease and low SES such as illiteracy, unemployment, no toilet facilities and no electricity at 95% BCI Lack of toilet facilities (odds ration (OR) =12.54; 95% BCI = 0.63, 24.38) exhibited the strongest association with malaria and highest risk of malaria disease. This was followed by no education (OR =11.83; 95% BCI = 0.54, 24.27) and lack of electricity supply (OR =10.56; 95% BCI = 0.43, 23.92) respectively. In the fifth chapter, the seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the malaria control intervention, dichlorodiphenyltrichloroethane (DDT) on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w0= −1174.781, p-value = 0.003) following the implementation of the intervention policy. Finally, the sixth chapter employed a SARIMA modelling approach to predict malaria cases in the endemic areas of KZN. Three plausible models were identified, and based on the goodness of fit statistics and parameter estimation, the SARIMA (0,1,1) (0,1,1)12 model was identified as the best fit model. The SARIMA (0,1,1)(0,1,1)12 model was used to forecast malaria cases during 2014, and it was observed to fit closely with the reported malaria cases during January to December 2014. The models generated in this study demonstrated the need for the KZN malaria program, relevant policy makers and stakeholders to further strengthen the KZN malaria elimination efforts. The required malaria elimination fortification are not limited to the implementation of additional sustainable developmental approach that combines both improved malaria intervention resources and socio-economic conditions, strengthening of existing community health workers, and strengthening of the already existing cross-border collaborations. However, more studies in the area of statistical modelling as well as practical applications of the generated models are encouraged. These can be accomplished by exploring new avenues via cross-sectional survey to understand the impact of community and social related structures in malaria burden; strengthening of existing community health workers; knowledge, attitude and practices in malaria control and intervention; and the likely effects of temporal/seasonal and spatial variations of malaria incidence in neighbouring endemic countries should be explored
Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia
Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates.
A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia.
Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia.
The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia.
At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity.
The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia.
In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought
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