Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012

Changes in vegetation phenology are among the most sensitive biological responses to global change. While land surface phenological changes in the Northern Hemisphere have been extensively studied from the widely used long-term AVHRR (Advanced Very High Resolution Radiometer) data, current knowledge on land surface phenological trends and the associated drivers remains uncertain for the tropics. This uncertainty is partly due to the well-known challenges of applying satellite-derived vegetation indices from the optical domain in areas prone to frequent cloud cover. The long-term vegetation optical depth (VOD) product from satellite passive microwaves features less sensitivity to atmospheric perturbations and measures different vegetation traits and functioning as compared to optical sensors. VOD thereby provides an independent and complementary data source for studying land surface phenology and here we performed a combined analysis of the VOD and AVHRR NDVI (Normalized Difference Vegetation Index) datasets for the dry tropics (25°N to 25°S) during 1992–2012. We find a general delay in the VOD derived start of season (SOS) and end of season (EOS) as compared to NDVI derived metrics, however with clear differences among land cover and continents. Pixels characterized by significant phenological trends (P < 0.05) account for up to 20% of the study area for each phenological metric of NDVI and VOD, with large spatial difference between the two sensor systems. About 50% of the pixels studied show significant phenological changes in either VOD or NDVI metrics. Drivers of phenological changes were assessed for pixels of high agreement between VOD and NDVI phenological metrics (serving as a means of reducing noise-related uncertainty). We find rainfall variability and woody vegetation change to be the main forcing variables of phenological trends for most of the dry tropical biomes, while fire events and land cover change are recognized as second-order drivers. Taken together, our study provides new insights on land surface phenological changes and the associated drivers in the dry tropics, as based on the complementary long-term data sources of VOD and NDVI, sensitive to changes in vegetation water content and greenness, respectively

RNA Sequencing Characterizes Transcriptomes Differences in Cold Response Between Northern and Southern Alternanthera philoxeroides and Highlight Adaptations Associated With Northward Expansion

Alternanthera philoxeroides recently expanded its range northwards in China. It is unknown if the range expansion has a genetic and/or epigenetic basis, or merely an environmental basis due to a warming climate. To test these possibilities, we used an RNAseq approach with a common greenhouse design to examine gene expression in individuals from the northern edge and central portion of alligator weed range from China to determine if there were differences in their responses to cold temperatures. We hypothesized that if the recent range expansion was primarily environmental, we would observe few differences or only differences unrelated to low-temperature adaptations. We assembled over 75,000 genes of which over 65,000 had long open reading frames with similarity to sequences from arabidopsis. Differences in expression between northern and southern populations that were both exposed to low temperatures showed similar expression among genes in the C-REPEAT/DRE BINDING FACTOR (CBF) regulon. However, gene set and sub-network enrichment analysis indicated differences in the response of photosynthetic processes and oxidative stress responses were different between the two populations and we relate these differences to cold adaptation. The transcriptome differences in response to cold between the individuals from the two populations is consistent with adaptations potentiating or resulting from selection after expansion into colder environments and may indicate that genetic changes have accompanied the recent northward expansion of A. philoxeroides in China. However, we cannot rule out the possibility of epigenetic changes may have a role in this expansion

Effect of excess iron and copper on physiology of aquatic plant Spirodela polyrrhiza (L.) Schleid” Environ

ABSTRACT: To elucidate effect of chemical reagents addition on growth of aquatic plants in restoration of aquatic ecosystem, Spirodela polyrrhiza (L.) Schleid was used to evaluate its physiological responses to excess iron (Fe 31 ) and copper (Cu 21 ) in the study. Results showed that accumulation of iron and copper both reached maximum at 100 mg L 21 iron or copper after 24 h short-term stress, but excess iron and copper caused plants necrosis or death and colonies disintegration as well as roots abscission at excess metal concentrations except for 1 mg L 21 iron. Significant differences in chlorophyll fluorescence (Fv/Fm) were observed at 1-100 mg L 21 iron or copper. The synthesis of chlorophyll and protein as well as carbohydrate and the uptake of phosphate and nitrogen were inhibited seriously by excess iron and copper. Proline content decreased with increasing iron or copper concentration, however, MDA content increased with increasing iron or copper concentration.

Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer&rsquo;s and user&rsquo;s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase&rsquo;s and reviving phase&rsquo;s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Person re-identification using local relation-aware graph convolutional network

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian images. As a result, local features contain limited information from one pedestrian image, and cannot benefit from other pedestrian images. In this paper, we propose a novel approach named Local Relation-Aware Graph Convolutional Network (LRGCN) to learn the relationship of local features among different pedestrian images. In order to completely describe the relationship of local features among different pedestrian images, we propose overlap graph and similarity graph. The overlap graph formulates the edge weight as the overlap node number in the node’s neighborhoods so as to learn robust local features, and the similarity graph defines the edge weight as the similarity between the nodes to learn discriminative local features. To propagate the information for different kinds of nodes effectively, we propose the Structural Graph Convolution (SGConv) operation. Different from traditional graph convolution operations where all nodes share the same parameter matrix, SGConv learns different parameter matrices for the node itself and its neighbor nodes to improve the expressive power. We conduct comprehensive experiments to verify our method on four large-scale person re-ID databases, and the overall results show LRGCN exceeds the state-of-the-art methods

Whole Genome Association Study in a Homogenous Population in Shandong Peninsula of China Reveals JARID2 as a Susceptibility Gene for Schizophrenia

DNA pooling can provide an economic and efficient way to detect susceptibility loci to complex diseases. We carried out a genome screen with 400 microsatellite markers spaced at approximately 10 cm in two DNA pools consisting of 119 schizophrenia (SZ) patients and 119 controls recruited from a homogenous population in the Chang Le area of the Shandong peninsula of China. Association of D6S289, a dinucleotide repeat polymorphism in the JARID2 gene with SZ, was found and confirmed by individual genotyping (X2 = 17.89; P = .047). In order to refine the signal, we genotyped 14 single nucleotide polymorphisms (SNPs) covering JARID2 and the neighboring gene, DNTBP1, in an extended sample of 309 cases and 309 controls from Shandong peninsula (including the samples from the pools). However, rs2235258 and rs9654600 in JARID2 showed association in allelic, genotypic and haplotypic tests with SZ patients from Chang Le area. This was not replicates in the extended sample, we conclude that JARID2 could be a susceptibility gene for SZ