NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China

Vegetation is an important component of the terrestrial ecosystem that plays an essential role in the exchange of water and energy in climate and biogeochemical cycles. This study investigated the spatiotemporal variation of normalized difference vegetation index (NDVI) in northern China using the GIMMS-MODIS NDVI during 1982–2018. We explored the dominant drivers of NDVI change using regression analyses. Results show that the regional average NDVI for northern China increased at a rate of 0.001 year−1. NDVI improved and degraded area corresponded to 36.1% and 9.7% of the total investigated area, respectively. Climate drivers were responsible for NDVI change in 46.2% of the study area, and the regional average NDVI trend in the region where the dominant drivers were temperature (T), precipitation (P), and the combination of precipitation and temperature (P&T), increased at a rate of 0.0028, 0.0027, and 0.0056 year−1, respectively. We conclude that P has positive dominant effects on NDVI in the subregion VIAiia, VIAiic, VIAiib, VIAib of temperate grassland region, and VIIBiia of temperate desert region in northern China. T has positive dominant effects on NDVI in the alpine vegetation region of Qinghai Tibet Plateau. NDVI is negatively dominated by T in the subregion VIIBiib, VIIBib, VIIAi, and VIIBi of temperate desert regions. Human activities affect NDVI directly by reforestation, especially in Shaanxi, Shanxi, and Hebei provinces.

Analysis of polymorphism of strawberry genotypes (Fragaria L.) according to the strawberry red root spot resistance gene RPF1 for identification of strawberry forms promising for breeding and horticulture

Red root spot (Phytophthora fragariae var. fragariae Hickman) is one of the most important strawberry diseases in the temperate climate zone. Identification of forms, carrying resistance genes, is an important stage in breeding programs aimed at obtaining red root spot resistant strawberry varieties. Diagnostic DNA markers of target genes will increase reliability of identification and efficiency of strawberry breeding for the creation of resistant genotypes. The purpose of this study is analysis of polymorphism of wild species of Fragaria L. genus and strawberry varieties (F. × ananassa Duch.) according to the strawberry red root spot resistance gene Rpf1 using molecular markers. The research sunjects were the wild species F. orientalis Los., F. moschata Duch., F. ovalis Rydb., F. virginiana Duch. ssp. platypetala and strawberry varieties (F. × ananassa Duch.) of different ecological and geographic origin. Total genomic DNA of strawberry was extracted from the fresh leaves using the Puchooa method. To assess the allelic state of Rpf1 gene, SCAR-R1A marker (linked to the Rpf1 dominant allele) and OPO-16C marker (linked to the rpf1 recessive allele) have been used. SCAR-R1A marker was identified in wild species F. virginiana Duch. ssp. platypetala (vegetation region: British Columbia, Canada), pineapple strawberry varieties Bylinnaya and promising selected forms 62-41 (Bylinnaya × Feyyerverk), 65-17 and 65-24 (Olimpiyskaya nadezhda × Bylinnaya). These genotypes are characterized by heterozygous Rpf1rpf1 genotype according to Rpf1 gene (both markers are present in genotype) and can be used as red root spot resistance source in marker-assisted selection. In the remaining studied genotypes of strawberry, SCAR-R1A marker was not detected, which presumably indicated their homozygous recessive genotype rpf1rpf1 according to Rpf1 gene. The research results can be useful for breeders of strawberry and researchers of plant biodiversity of p. Fragaria.

Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping

When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available.

Analysis Of Vegetation Classification Algorithms On Satellite Images And Medical Images

: In recent days, the remote sensing algorithms are used in the medical field for improving the visualization of the medical images. Because, the medical images are generally in the gray scale image format for better visualization the colour Doppler or spectrograms are used but they are expensive. To overcome this drawback the remote sensing algorithm is applied to the medical images to group the pixels and visualize in different colours. The image processing techniques is used to classify the vegetation region into 16 samples. The image pre-processing is done by Wiener filter to remove the noise. Feature extraction is carried out by Grey Level Co-occurrence Matrix (GLCM) and the spectral bands are optimized by Particle Swarm Optimization (PSO) .The classification of vegetation region is classified by Extreme Learning Machine. In this, the comparisons of the remote sensing algorithms like IRVM-MFO, ELM-DF and ELM-PSO for the Indian pines and Salinas Dataset. Among these the ELM- Dragon Fly algorithm produced the best results for both the sets. Hence, this ELM-DF is applied to the Brain tissue region segmentation. In this paper the analysis is performed to find the efficient method for vegetation classification by comparing with other methods. Simulations are carried out on two datasets such as Indian Pine and Salinas scene. Performance metrics such as accuracy, specificity, and sensitivity have been evaluated that show the efficiency of the proposed classifier.

Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin

The estimation of fractional vegetation cover (FVC) by using remote sensing images has become feasible. Based on Landsat8-OLI images and field data obtained from an unmanned aerial vehicle, we established an empirical model (EM) and a pixel decomposition model (PDM) of FVC in the desert vegetation region, steppe vegetation region, meadow vegetation region and mixed vegetation region (the three vegetation region types) of the Qaidam Basin, and the inversion accuracies of the models were compared. The results show the following: (1) Vegetation classification inversion (VCI) provides a promising approach for FVC estimation. The accuracy of FVC by VCI was obviously better than that achieved using vegetation mixed inversion (VMI); (2) Differences were observed in the FVC estimation between VCI and VMI by the EM in areas with relatively high-density vegetation cover (FVC > 60%). The FVC in some parts of steppe region in the basin was slightly overestimated by VMI of the EM; 3) VCI estimated by the PDM resulted in lower inversion values for extremely low-density vegetation cover (FVC ≤ 10%) and higher inversion values for high-density vegetation cover (FVC > 80%). The FVC inversion was underestimated by the PDM in steppe and meadow regions with FVC > 15% in the basin. The application of VCI in different models can provide new ideas for the sustainable study of vegetation in arid regions.

Mapping the vegetation soil and water region analysis of Tuticorin district using Landsat images

Mapping of water bodies, soil and vegetation region from satellite imagery has been widely explored in the recent past. Several approaches have been developed to detect water bodies and identify the soil types from different satellite imagery varying in spatial, spectral, and temporal characteristics. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed is increased. Its imagery produces a better result in classifying the soil and water regions. The current study puts forward an approach to map water bodies, soil and vegetation region from a Landsat satellite imagery using the various processing models. In this study, to identify the water region and soil region, we go with water index, vegetation index and soil index measures. By using reflectance bands, it is easy to analyze the water, vegetation and soil regions. The proposed method accurately and quickly discriminated the water, vegetation and soil region from other land cover features.

Why do horseflies need polarization vision for host detection? Polarization helps tabanid flies to select sunlit dark host animals from the dark patches of the visual environment

Horseflies (Tabanidae) are polarotactic, being attracted to linearly polarized light when searching for water or host animals. Although it is well known that horseflies prefer sunlit dark and strongly polarizing hosts, the reason for this preference is unknown. According to our hypothesis, horseflies use their polarization sensitivity to look for targets with higher degrees of polarization in their optical environment, which as a result facilitates detection of sunlit dark host animals. In this work, we tested this hypothesis. Using imaging polarimetry, we measured the reflection–polarization patterns of a dark host model and a living black cow under various illumination conditions and with different vegetation backgrounds. We focused on the intensity and degree of polarization of light originating from dark patches of vegetation and the dark model/cow. We compared the chances of successful host selection based on either intensity or degree of polarization of the target and the combination of these two parameters. We show that the use of polarization information considerably increases the effectiveness of visual detection of dark host animals even in front of sunny–shady–patchy vegetation. Differentiation between a weakly polarizing, shady (dark) vegetation region and a sunlit, highly polarizing dark host animal increases the efficiency of host search by horseflies.

Potencial antibiótico da própolis apícola Potiguar em bactérias de importância veterinária

<p>A Própolis de abelhas africanizadas (<em>Apis mellifera</em> L.) é um produto da colmeia, elaborado a partir de exsudações de resinas que as abelhas recolhem de determinadas plantas. A composição química da própolis é complexa e relacionada à diversidade vegetal encontrada em torno da colmeia. Estudos recentes demonstram que a própolis possui uma série de propriedades biológicas, essas propriedades têm feito da própolis uma importante matéria-prima para as indústrias farmacêutica, alimentícia e de cosméticos. O estudo dessas propriedades é, portanto, necessário, a fim de se obter um produto com alto padrão de qualidade e valor agregado. Este trabalho tem como objetivo a avaliação do efeito de sete diferentes extratos alcoólicos da própolis (EAP) apícola Potiguar no desenvolvimento de quatro microrganismos de importância veterinária. As colmeias habitadas com enxames de abelhas africanizadas (<em>Apis mellifera</em> L.) selecionados para coleta da própolis estavam organizadas em apiários, distribuídos em região de vegetação distinta no estado do Rio Grande do Norte, Brasil. As coletas de material no campo ocorreram no período dos meses de outubro a dezembro de 2013, a obtenção dos extratos e os ensaios do potencial antibiótico ocorreram durante o ano de 2014. Foi identificado que os EAP 1, 6 e 7 foram ativos nos quatro microrganismos testados, e os EAP 3 e 4 não demonstraram-se ativos para nenhum microrganismo. Os resultados encontrados evidenciam a superioridade da própolis vermelha do mangue Potiguar quando comparados aos resultados citados em outros estudos para os mesmos microrganismos.</p><p align="center"><strong><em>Antibiotic potential of the Potiguar bee propolis on the bacteria of veterinary importance</em></strong></p><p><strong>Abstract: </strong>Propolis Africanized bees (Apis mellifera L.) is a product of the bee hive, elaborated based on exudates resins that bees collect from certain plants. The chemical composition of propolis is complex and related to plant diversity found around the bee hive. Recent studies have shown that propolis has a number of biological properties, these properties have made from propolis an important raw material for the pharmaceutical, food and cosmetic industries. The study of the properties is therefore necessary in order to obtain a product with a high standard of quality and value. This study aims to evaluate seven different alcoholic extracts of propolis (AEP), of Potiguar honey bees, in the development of four microorganisms of great veterinary importance. The bee hives inhabited by swarms of Africanized bees (<em>Apis mellifera</em> L.) selected for the collection of propolis were organized in apiary distributed in different vegetation region in the state of Rio Grande do Norte, Brazil. The material collected in the field occurred in the period from October to December 2013, obtaining the extracts and antibiotic potential of the trials took place during the year 2014. It was identified that the EAP 1, 6 and 7 were active in all four tested microorganisms, and the EAP 3 and 4 are not demonstrated to be active for any microorganism. The results show the superiority of red propolis Potiguar of mangrove when compared to the results cited in other studies for the same microorganisms.</p>