Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions

We hypothesized that NDVI time-series composite imagery or clustered data derived from the NDVI time series could serve as effective surrogates for land cover data in predictive modeling of species’ ecological niches and potential geographic distributions. Using two Mexican bird species, we examined our hypothesis with GARP, the Genetic Algorithm for Rule-set Prediction. Inputs included topographic and climate data, as well as the NDVI and clustered NDVI datasets. We used a land cover map previously derived from the NDVI dataset for comparison testing. Considering only topographic factors, we found that the NDVI or clustered NDVI data performed as well as or better than the land cover data. When climate data were added, the land cover data performed better than the NDVI data, but improvements were slight

Time-Series Classification of High-Temporal Resolution AVHRR NDVI Imagery of Mexico

Time-series data from wide-field sensors, acquired for the period of a growing season or longer, capitalize on phenological changes in vegetation and make it possible to identify vegetated land cover types in greater detail. Our objective was to examine the utility of time-series data to rapidly update maps of vegetation condition and land cover change in Mexico as an input to biodiversity modeling. We downloaded AVHRR NDVI 10-day composites from the USGS EROS Data Center for 1992-1993 and adjusted for cloud contamination by further aggregating the data. In the first phase of our analysis, we selected training sites for various land cover types using a land cover map created by the Mexican National Institute of Statistics, Geography, and Informatics (INEGI) as a guide. Since there is a high degree of spectral variability within many of the vegetated land cover types, we subjected the spectral response patterns to cluster analysis. We then used the statistics of the clusters as training data in a supervised classification. We also compared unsupervised and univariate decision tree approaches, but these provided unsatisfactory results. Best results were achieved with a 19-class map of land use/land cover employing a supervised approach

IZA COVID-19 crisis response monitoring: short-run labor market impacts of COVID-19, initial policy measures and beyond

The unprecedented COVID-19 pandemic has a severe impact on societies, economies and labor markets. However, not all countries, socio-economic groups and sectors are equally affected. For example, occupational groups working in sectors where value chains have been disrupted and lockdowns have had direct impacts are affected more heavily, while the slowdown of hiring activities mostly affects young labor market entrants. As a result, there has been a steep increase in unemployment rates in many countries, but not everywhere to the same extent. Part of this difference can be related to the different role and extent of short-time work schemes, which is now being used more widely than during the Great Recession. Some countries have created or expanded these schemes, making coverage less exclusive and benefits more generous, at least temporarily. But short-time work is certainly not a panacea to “flatten the unemployment curve”. Furthermore, next to providing liquidity support to firms, unemployment benefits have been made more generous in many countries. Often, activation principles have also been temporarily reduced. Some countries have increased access to income support to some extent also for non-standard workers, such as temporary agency workers or self-employed workers, on an ad hoc basis. A major change in working conditions is the broad move towards telework arrangements and work from home. Nonetheless, it appears too early to assess the relative success of national strategies to cope with the pandemic and to revitalize the labor market as well as the medium-term fiscal viability of different support measures. Future monitoring will also have to trace policies to cope with the imminent structural changes that might result from the crisis or might be accelerated by the crisis

Controlled human malaria infection with Plasmodium falciparum demonstrates impact of naturally acquired immunity on virulence gene expression

The pathogenesis of Plasmodium falciparum malaria is linked to the variant surface antigen PfEMP1, which mediates tethering of infected erythrocytes to the host endothelium and is encoded by approximately 60 var genes per parasite genome. Repeated episodes of malaria infection result in the gradual acquisition of protective antibodies against PfEMP1 variants. The antibody repertoire is believed to provide a selective pressure driving the clonal expansion of parasites expressing unrecognized PfEMP1 variants, however, due to the lack of experimental in vivo models there is only limited experimental evidence in support of this concept. To get insight into the impact of naturally acquired immunity on the expressed var gene repertoire early during infection we performed controlled human malaria infections of 20 adult African volunteers with life-long malaria exposure using aseptic, purified, cryopreserved P. falciparum sporozoites (Sanaria PfSPZ Challenge) and correlated serological data with var gene expression patterns from ex vivo parasites. Among the 10 African volunteers who developed patent infections, individuals with low antibody levels showed a steep rise in parasitemia accompanied by broad activation of multiple, predominantly subtelomeric var genes, similar to what we previously observed in naïve volunteers. In contrast, individuals with intermediate antibody levels developed asymptomatic infections and the ex vivo parasite populations expressed only few var gene variants, indicative of clonal selection. Importantly, in contrast to parasites from naïve volunteers, expression of var genes coding for endothelial protein C receptor (EPCR)-binding PfEMP1 that are associated with severe childhood malaria was rarely detected in semi-immune adult African volunteers. Moreover, we followed var gene expression for up to six parasite replication cycles and demonstrated for the first time in vivo a shift in the dominant var gene variant. In conclusion, our data suggest that P. falciparum activates multiple subtelomeric var genes at the onset of blood stage infection facilitating rapid expansion of parasite clones which express PfEMP1 variants unrecognized by the host’s immune system, thus promoting overall parasite survival in the face of host immunity

Analysis of Time-Series MODIS 250 m Vegetation Index Data for Crop Classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for largearea crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral–temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region’s major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop’s multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state’s climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season

Global Priorities in Land Remote Sensing

ABSTRACT Improved and up-to-date land use/land cover (LULC) datasets are needed for intensively cropped regions such as the U.S. Central Great Plains, in order to support a variety of science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed crop-related LULC mapping in this region, given its global coverage and unique combination of spatial (250-meter), spectral, temporal (16-day composites), and radiometric (12-bit) resolutions. In this study, a hierarchical MODIS-based crop mapping protocol, which incorporates a time-series of MODIS 250-m NDVI data and a decision tree classifier, was tested for the state of Kansas. The protocol produced a series of three crop-related LULC maps that classified: 1) general crop types (alfalfa, summer crops, winter wheat, and fallow), 2) summer crop types (corn, sorghum, and soybeans), and 3) irrigated/non-irrigated crops. A statistical accuracy assessment and state and sub-state areal comparisons with USDA crop acreage information were conducted for each map to assess its overall quality and highlight any major areas of regional misclassification. The MODIS NDVI-derived maps generally had overall and class-specific accuracies of greater than 80%. Overall accuracies ranged from 84% (summer crop map) to 94% (general crop map). The classified crop areas were within 1-5% of USDA reported crop areas for most classes at the state level. Sub-state comparisons found that for most classes the areal discrepancies were relatively minor throughout the state. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at the 250-m resolution. Regional areal differences were also found for selected classes that were the result of localized precipitation patterns and specific crop practices (i.e. double cropping). These results illustrate the considerable potential of the MODIS-based mapping protocol for spatially and thematically detailed regionalscale crop classification

Automated Mapping of Historical Native American Land Allotments at the Standing Rock Sioux Reservation Using Geographic Information Systems

The General Allotment Act of 1887, also known as the Dawes Act, established the legal basis for the United States government to break up remaining tribally-owned reservation lands in the U.S. by allotting individual parcels to tribal members and selling the remaining “surplus.” This research explores the processes involved in mapping these historical allotments and describes a method to automatically generate spatial data of allotments. A custom geographic information systems (GIS) tool was created that takes tabular based allotment land descriptions and digital Public Land Survey (PLSS) databases to automatically generate spatial and attribute data of those land parcels. The Standing Rock Sioux Tribe of North and South Dakota was used as the initial study area to test the mapping technique, which resulted in successfully auto-mapping over 99.1% of allotted lands on the reservation, including the smallest aliquot parcels. This GIS technique can be used to map any tribal lands or reservation with allotment data available, and currently it can be used to map over 120 individual reservations using publicly available data from the Bureau of Land Management (BLM)