201 research outputs found

    Linkages between Atmospheric Circulation, Weather, Climate, Land Cover and Social Dynamics of the Tibetan Plateau

    Get PDF
    The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. In recent decades, the TP has undergone significant changes due to climate and human activities. Since the 1980s anthropogenic activities, such as the stocking of livestock, land cover change, permafrost degradation, urbanization, highway construction, deforestation and desertification, and unsustainable land management practices, have greatly increased over the TP. As a result, grasslands have undergone rapid degradation and have altered the land surface which in turn has altered the exchange of heat and moisture properties between land and the atmosphere. But gaps still exist in our knowledge of land-atmosphere interactions in the TP and their impacts on weather and climate around the TP, making it difficult to understand the complete energy and water cycles over the region. Moreover, human, and ecological systems are interlinked, and the drivers of change include biophysical, economic, political, social, and cultural elements that operate at different temporal and spatial scales. Current studies do not holistically reflect the complex social-ecological dynamics of the Tibetan Plateau. To increase our understanding of this coupled human-natural system, there is a need for an integrated approach to rendering visible the deep interconnections between the biophysical and social systems of the TP. There is a need for an integrative framework to study the impacts of sedentary and individualized production systems on the health and livelihoods of local communities in the context of land degradation and climate change. To do so, there is a need to understand better the spatial variability and landscape patterns in grassland degradation across the TP. Therefore, the main goal of this dissertation is to contribute to our understanding of the changes over the land surface and how these changes impact the plateau\u27s weather, climate, and social dynamics. This dissertation is structured as three interrelated manuscripts, which each explore specific research questions relating to this larger goal. These manuscripts constitute the three primary papers of this dissertation. The first paper documents the significant association of surface energy flux with vegetation cover, as measured by satellite based AVHRR GIMMS3g normalized difference vegetation index (NDVI) data, during the early growing season of May in the western region of the Tibetan Plateau. In addition, a 1°K increase in the temperature at the 500 hPa level was observed. Based on the identified positive effects of vegetation on the temperature associated with decreased NDVI in the western region of the Tibetan Plateau, I propose a positive energy process for land-atmosphere associations. In the second paper, an increase in Landsat-derived NDVI, i.e., a greening, is identified within the TP, especially during 1990 to 2018 and 2000 to 2018 time periods. Larger median growing season NDVI change values were observed for the Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grassland regions, in comparison to the other three regions studied. Land degradation is prominent in the lower and intermediate hillslope positions in comparison to the higher relative topographic positions, and change is more pronounced in the eastern Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grasslands. Geomorphons were found to be an effective spatial unit for analysis of hillslope change patterns. Through the extensive literature review presented in third paper, this dissertation recommends using critical physical geography (CPG) to study environmental and social issues in the TP. The conceptual model proposed provides a framework for analysis of the dominant controls, feedback, and interactions between natural, human, socioeconomic, and governance activities, allowing researchers to untangle climate change, land degradation, and vulnerability in the Tibetan Plateau. CPG will further help improve our understanding of the exposure of local people to climate and socio-economic and political change and help policy makers devise appropriate strategies to combat future grassland degradation and to improve the lives and strengthen livelihoods of the inhabitants of the TP

    Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

    Get PDF
    Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) timeseries derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns

    Natural or anthropogenic variability? A long-term pattern of the zooplankton communities in an ever-changing transitional ecosystem

    Get PDF
    The Venice Lagoon is an important site belonging to the Italian Long-Term Ecological Research Network (LTER). Alongside with the increasing trend of water temperature and the relevant morphological changes, in recent years, the resident zooplankton populations have also continued to cope with the colonization by alien species, particularly the strong competitor Mnemiopsis leidyi. In this work, we compared the dynamics of the lagoon zooplankton over a period of 20 years. The physical and biological signals are analyzed and compared to evaluate the hypothesis that a slow shift in the environmental balance of the site, such as temperature increase, sea level rise (hereafter called “marinization”), and competition between species, is contributing to trigger a drift in the internal equilibrium of the resident core zooplankton. Though the copepod community does not seem to have changed its state, some important modifications of structure and assembly mechanisms have already been observed. The extension of the marine influence within the lagoon has compressed the spatial gradients of the habitat and created a greater segregation of the niches available to some typically estuarine taxa and broadened and strengthened the interactions between marine species

    Central Russia heavy metal contamination model based on satellite imagery and machine learning

    Get PDF
    Atmospheric heavy metal contamination is a real threat to human health. In this work, we examined several models trained on in situ data and indices got from satellite images. During 2018-2019, 281 samples of naturally growing mosses were collected in the Vladimir, Yaroslavl, and Moscow regions in Russia. The samples were analyzed using Neutron Activation Analysis to get the contamination levels of 18 heavy metals. The Google Earth Engine platform was used to calculate indices from satellite images that represent summarized information about sampling sites. Statistical and neural models were trained on in situ data and the indices. We focused on the classification task with 8 levels of contamination and used balancing techniques to extend the training data. Three approaches were tested: variations of gradient boosting, multilayer perceptron, and Siamese networks. All these approaches produced results with minute differences, making it difficult to judge which one is better in terms of accuracy and graphical outputs. Promising results were shown for 9 heavy metals with an overall accuracy exceeding 89%. Al, Fe, and Sb contamination was predicted for 3,000 and 12,100 grid nodes on a 500 km2 area in the Central Russia region for 2019 and 2020. The results, methods, and perspectives of the adopted approach of using satellite data together with machine learning for HM contamination prediction are presented

    Assessing the Impact of Gold Mining on Forest Cover in the Surinamese Amazon Rainforest from 1997 - 2019: A Semi-Automated Satellite-Based Approach

    Get PDF
    The Amazon rainforest, as a biodiversity hotspot and regulator of the earths climate, is one of the most important ecosystems on earth, but has been facing extensive deforestation for decades due to urban growth, agricultural expansion, logging and mining. Mining (and the use of remote sensing methods to detect it) has been relatively understudied in the Amazon compared to the other drivers up until a decade ago, highlighting the importance of current research. The objectives of this study are: To quantify the increase in industrial and artisanal mining and its impact on forest cover in the northern Amazonian country of Suriname between 1997 and 2019; Evaluate the impact of this expansion on the structure (fragmentation) and health (phenology) of the forest; and improve existing remote sensing techniques for mining detection through the development of a pioneer method based on cloud processing and semi-automated mining reclassification. The cloud processing software known as Google Earth Engine (GEE) was used for the initial land use land cover classification of the study area. Landsat 5 and 8 images and the classification and regression trees (C.A.R.T) algorithm were used in this step. The resulting classified maps were fed into the semi-automated re-classification model developed for this study, producing final re-classified output maps, which were used to analyse the expansion of mining and its associated impacts on forest fragmentation and phenology. The proposed method is the first documented method which combines cloud processing with a semi-automated re-classification model, providing a technologically advanced approach capable of rapid and efficient detection of mines. This approach resulted in an 89.5% accuracy of mining detection, and the combination of speed, efficiency, and highly accurate detection outperformed many of the other currently documented methods for mining detection in the Amazon. The results highlighted that mining increased from 69.4km² in 1997 to 431.6km² in 2019, an increase of 522% over 22 years. This growth led directly to 351.9km² of forest loss, 83% of which was due to artisanal mining. This loss of forest led to a 122.8km² reduction in the effective mesh size for the artisanal mine sub-area, compared to a decrease of 83km² for the Industrial mine sub-area. Mining also caused a decrease in the health of the surrounding forest, with the decrease in peak greenness being more pronounced for artisanal mining compared to industrial mining. Recommendations for future research include exploring the use of higher resolution imagery such as Sentinel for better results, as well as the use of microwave data in the classification to combat the issue of extensive cloud cover in the Amazon. The issue of overclassification present in the proposed method can potentially be combated by exploring combinations of different classification algorithms with the reclassification model

    Relationship between synoptic circulations and the spatial distributions of rainfall in Zimbabwe

    Get PDF
    This study examines how the atmospheric circulation patterns in Africa south of the equator govern the spatial distribution of precipitation in Zimbabwe. The moisture circulation patterns are designated by an ample set of eight classified circulation types (CTs). Here it is shown that all wet CTs over Zimbabwe features enhanced cyclonic/convective activity in the southwest Indian Ocean. Therefore, enhanced moisture availability in the southwest Indian Ocean is necessary for rainfall formation in parts of Zimbabwe. The wettest CT in Zimbabwe is characterized by a ridging South Atlantic Ocean high-pressure, south of South Africa, driving an abundance of southeast moisture fluxes, from the southwest Indian Ocean into Zimbabwe. Due to the proximity of Zimbabwe to the Agulhas and Mozambique warm current, the activity of the ridging South Atlantic Ocean anticyclone is a dominant synoptic feature that favors above-average rainfall in Zimbabwe. Also, coupled with a weaker state of the Mascarene high, it is shown that a ridging South Atlantic Ocean high-pressure, south of South Africa, can be favorable for the southwest movement of tropical cyclones into the eastern coastal landmasses resulting in above-average rainfall in Zimbabwe. The driest CT is characterized by the northward track of the Southern Hemisphere mid-latitude cyclones leading to enhanced westerly fluxes in the southwest Indian Ocean, limiting moist southeast winds into Zimbabwe

    Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data

    Get PDF
    Over 90 % of the world's rice is produced in the Asia–Pacific region. Synthetic-aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. The complexity of rice cultivation patterns in tropical and subtropical regions makes it difficult to construct a representative data-relevant rice crop model, increasing the difficulty in extracting rice distributions from SAR data. To address this problem, a rice area mapping method for large regional tropical or subtropical areas based on time-series Sentinel-1 SAR data is proposed in this study. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatiotemporal statistical features with good generalization ability was selected and then input into the U-Net semantic segmentation model, combined with WorldCover data to reduce false alarms, finally the 20 m resolution rice area map of five countries in mainland Southeast Asia in 2019 was obtained. The proposed method achieved an accuracy of 92.20 % on the validation sample set, and the good agreement was obtained when comparing our rice area map with statistical data and other rice area maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice area mapping with complex cropping patterns and the reliability of the generated rice area maps. The 20 m annual paddy rice area map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun et al., 2022b).</p

    Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

    Get PDF
    This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

    Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands

    Get PDF
    An increase in shrubs or woody species is likely, directly or indirectly, to significantly affect rural livelihoods, wildlife/livestock productivity and conservation efforts. Poor and inappropriate land use management practices have resulted in rangeland degradation, particularly in semi-arid regions, and this has amplified the bush encroachment rate in many African countries, particularly in key savannah rangelands. The rate of encroachment is also perceived to be connected to other environmental factors, such as climate change, fire and rainfall variability, which may influence the structure and density of the shrubs (woody plants), when compared to uncontrolled grazing. Remote sensing has provided robust data for global studies on both bush encroachment and climate variability over multiple decades, and these data have complemented the local and regional evidence and process studies. This paper thus provides a detailed review of the advancements in the use of remote sensing for the monitoring of bush encroachment on the African continent, which is fuelled by climate variability in the rangeland areas

    Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

    Get PDF
    This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics
    corecore