Anne Schucknecht

Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series

Abstract Desertification is a challenge in north-eastern Brazil (NEB) that needs to be understood to develop sustainable land-use strategies. This study analyses regional vegetation dynamics in NEB and the compatibility of two NDVI data sets to support future desertification assessment studies in the semi-arid Caatinga biome. Vegetation variability and trends in NEB are analysed for 1982–2006, based on monthly AVHRR (GIMMS) NDVI data. The GIMMS data are compared with MODIS NDVI for the overlapping period 2001–2006. Existing statistical methods are applied and existing NDVI analyses in NEB expanded in respect to vegetation trend analysis and data set comparison.

Investigating the relationship between the inter-annual variability of satellite-derived vegetation phenology and a proxy of biomass production in the Sahel

Abstract: In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the

Large-scale synoptic types and their impact on European precipitation

Atmospheric circulation strongly modulates precipitation patterns throughout Europe. A selection of synoptic types was chosen to investigate the impact of circulation on the spatial distribution of precipitation within Europe and neighbouring regions (for 1951–2010). Applied were (1) the original and one automated version of the Grosswetterlagen classification, (2) the Vangengeim–Girs classification and (3) a dataset of the North Atlantic Oscillation (NAO). Daily values of the E-OBS gridded precipitation dataset were allocated to synoptic types, visualising precipitation anomalies (in percent) during winter (October–March) and summer (April–September) half years. Anomalies from average precipitation conditions (only) contain days connected to each of the investigated synoptic types. Distinct anomaly patterns become visible and are explained by the location of pressure systems. Patterns are spatially similar between both half years for Grosswetterlagen and Vangengeim–Girs classifications, while the NAO shows pronounced seasonal changes. Precipitation anomaly maps were applied to help explain observed changes in European precipitation amounts from 1981 to 2010, as compared to 1951–1980. Changes of precipitation amounts were related to frequency changes of synoptic types, predominantly during the winter half year. Here, increasing (decreasing) frequencies of synoptic types connected to westerly (easterly) inflow supported higher precipitation amounts in northern Europe and lower amounts in southern Europe.

Vegetation Greenness in Northeastern Brazil and Its Relation to ENSO Warm Events

The spatio-temporal variability of trends in vegetation greenness in dryland areas is a well-documented phenomenon in remote sensing studies at global to regional scales. The underlying causes differ, however, and are often not well understood. Here, we analyzed the trends in vegetation greenness for a semi-arid area in northeastern Brazil (NEB) and examined the relationships between those dynamics and climate anomalies, namely the El Nino Southern Oscillation (ENSO) for the period 1982 to 2010, based on annual Normalized Difference Vegetation Index (NDVI) values from the latest version of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset (NDVI3g) dataset. Against the ample assumption of ecological and socio-economic research, the results of our inter-annual trend analysis of NDVI and precipitation indicate large areas of significant greening in the observation period. The spatial extent and strength of greening is a function of the prevalent land-cover type or biome in the study area. The regression analysis of ENSO indicators and NDVI anomalies reveals a close relation of ENSO warm events and periods of reduced vegetation greenness, with a temporal lag of 12 months. The spatial patterns of this relation vary in space and time. Thus, not every ENSO warm event is reflected in negative NDVI anomalies. Xeric shrublands (Caatinga) are more sensitive to ENSO teleconnections than other biomes in the study area.

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions.

The Modified Rainfall Anomaly Index (mRAI)—is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics?

Precipitation extremes affect various economic sectors and may result in substantial costs for societies. Future projections of such extreme occurrences are needed to successfully develop robust regional adaptation strategies. Model ensemble-based approaches provide a higher level of confidence since they compensate to some degree for the uncertainties of individual climate model projections. An ensemble of twelve regional climate projections from five regional climate models was used to evaluate the suitability of a modified version of the Rainfall Anomaly Index (mRAI) as an alternative to the Standardised Precipitation Index (SPI) in assessing future precipitation conditions. We compared frequency distributions and trends of the mRAI with the SPI for a test region that is climatologically representative of Central Eastern Europe. Both indices are highly correlated with each other at all tested timescales—both for stations and for regionally averaged data—with Pearson correlation coefficients >>0.9 and Spearman correlation coefficients >0.99. There are no significant differences in their frequency distributions, although the mRAI shows slightly higher frequencies in the classes of ‘moderately dry’ to ‘very dry’ conditions. The change signals revealed by SPI and mRAI are very similar for mean changes as well as for changes in the extremes. Considering the large bandwidth of change signals of individual regional climate projections, the mRAI provides sufficiently robust results for the evaluation of future precipitation anomaly trends. The notably more complex calculation of the SPI has no appreciable advantage for this application.

Spatial and temporal variability of vegetation status in Paraíba, Northeastern Brazil

Desertification is a serious problem in northeastern Brazil and the land degradation process needs to be understood in order to develop a sustainable land use. This study considers the aspect of spatial and temporal vegetation variability in the state of Paraíba. A ten year time period of the enhanced vegetation index (EVI) from MODIS satellite data with a high temporal resolution (16 days) and medium spatial resolution (250 m) was analyzed. In addition, a ten year EVI time series with monthly time intervals and 1 km spatial resolution was correlated with a precipitation time series. Vegetation in Paraíba is characterized by a high variability of greenness on the intra-annual and inter-annual level as well as on the spatial dimension. The highest correlation between EVI and precipitation is achieved for a time lag of 1 month (regional R= 0.64).

Remote sensing monitoring of land restoration interventions in semi-arid environments using a before-after control-impact statistical design

Highlights • A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed.• The intervention impact is evaluated by a before–after control-impact sampling design.• The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change.• The method is applicable to NDVI and other remote sensing-derived variables.