scholarly journals Tracing the Long-Term Evolution of Land Cover in an Alpine Valley 1820–2015 in the Light of Climate, Glacier and Land Use Changes

2021 ◽  
Vol 9 ◽  
Author(s):  
Severin Hohensinner ◽  
Ulrike Atzler ◽  
Andrea Fischer ◽  
Gabriele Schwaizer ◽  
Kay Helfricht
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Sediment Supply ◽  
Reconstruction Method ◽  
Optical Remote Sensing ◽  
Fluvial System ◽  
Ecosystem Research ◽  
Alpine Valley ◽  
Transformation Processes

Alpine glacial environments and their fluvial systems are among those landscapes most comprehensively affected by climate change. Typically, studies on the consequences of climate change in such environments, e.g., glacier retreat, cover a maximum of 70 years, reflecting the availability of orthophotos or satellite images. This study addresses the long-term transformation processes in a glaciated catchment and highlights the role of human agency in a changing Alpine environment. In order to identify land cover changes between 1820 and 2015 in the Long-Term Ecosystem Research (LTER) site “Jamtal” (Tyrol, Austria) we apply a “regressive-iterative GIS reconstruction method” combining both historical maps and optical remote sensing data. Below 2,100 m a.s.l. the Jamtal experienced a massive 62% decline of unvegetated debris areas and bedrock outcrops (so-called “wasteland”) that was mainly transformed to grassland and sparsely wooded areas. Forests increased by an outstanding 323% and grassland was replaced by sparsely or densely wooded areas. This primarily reflects the abandonment of agricultural uses at unfavourable remote sites. In the higher (formerly) glaciated subbasin, ice-covered areas declined by 55%, which was associated with a major (82%) growth of exposed wasteland. Concurrently, Alpine grassland expanded by 196% and krumholz even by 304%. Approximately half of the new fluvial system that evolved in deglaciated areas between 1870 and 1921 still existed in 2015. Unconsolidated debris buried almost one fifth of the new channels, and almost one third was colonized by vegetation. Recent data show that the deglaciation process is much faster than the colonization process by Alpine vegetation. Accordingly, the extent of wasteland has expanded and potentially amplifies the sediment supply to the fluvial system. Alterations in high Alpine hydrological and sediment/debris regimes significantly affect human use in lower, more favourable areas of the Alpine region. The long-term investigation of the Alpine landscape reveals that the transformation processes have accelerated in recent decades.

scholarly journals Long-term effects of climate and land cover change on freshwater provision in the tropical Andes

2015 ◽  
Vol 12 (6) ◽  
pp. 5219-5250 ◽  
Author(s):  
A. Molina ◽  
V. Vanacker ◽  
E. Brisson ◽  
D. Mora ◽  
V. Balthazar
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Catchment Area ◽  
Strong Increase ◽  
Tropical Andes ◽  
Evaporative Water ◽  
Exotic Tree

Abstract. Andean headwater catchments play a pivotal role to supply fresh water for downstream water users. However, few long-term studies exist on the relative importance of climate change and direct anthropogenic perturbations on flow regimes. In this paper, we assess multi-decadal change in freshwater provision based on long time series (1974–2008) of hydrometeorological data and land cover reconstructions for a 282 km2 catchment located in the tropical Andes. Three main land cover change trajectories can be distinguished: (1) rapid decline of native vegetation in montane forest and páramo ecosystems in ~1/5 or 20% of the catchment area, (2) expansion of agricultural land by 14% of the catchment area, (3) afforestation of 12% of native páramo grasslands with exotic tree species in recent years. Given the strong temporal variability of precipitation and streamflow data related to El Niño–Southern Oscillation, we use empirical mode decomposition techniques to detrend the time series. The long-term increasing trend in rainfall is remarkably different from the observed changes in streamflow that exhibit a decreasing trend. Hence, observed changes in streamflow are not the result of long-term climate change but very likely result from direct anthropogenic disturbances after land cover change. Partial water budgets for montane cloud forest and páramo ecosystems suggest that the strongest changes in evaporative water losses are observed in páramo ecosystems, where progressive colonization and afforestation of high alpine grasslands leads to a strong increase in transpiration losses.

scholarly journals Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time

2021 ◽  
Vol 13 (19) ◽  
pp. 3953
Author(s):  
Patrick Clifton Gray ◽  
Diego F. Chamorro ◽  
Justin T. Ridge ◽  
Hannah Rae Kerner ◽  
Emily A. Ury ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Agricultural Land ◽  
Coastal Areas ◽  
Support Vector ◽  
Validation Data

The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decadal cycles using only a decade or less of data. Understanding these long-term changes in low lying coastal areas, home to a huge proportion of the global population, is of particular importance. Relatively simple deep learning models that extract representative spatiotemporal patterns can lead to major improvements in temporal generalizability. To provide insight into major changes in low lying coastal areas, our study (1) developed a recurrent convolutional neural network that incorporates spectral, spatial, and temporal contexts for predicting land cover class, (2) evaluated this model across time and space and compared this model to conventional Random Forest and Support Vector Machine methods as well as other deep learning approaches, and (3) applied this model to classify land cover across 20 years of Landsat 5 data in the low-lying coastal plain of North Carolina, USA. We observed striking changes related to sea level rise that support evidence on a smaller scale of agricultural land and forests transitioning into wetlands and “ghost forests”. This work demonstrates that recurrent convolutional neural networks should be considered when a model is needed that can generalize across time and that they can help uncover important trends necessary for understanding and responding to climate change in vulnerable coastal regions.

scholarly journals Historical Arable Land Change in an Eco-Fragile Area: A Case Study in Zhenlai County, Northeastern China

2018 ◽  
Vol 10 (11) ◽  
pp. 3940 ◽  
Author(s):  
Yuanyuan Yang ◽  
Shuwen Zhang
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Arable Land ◽  
Agricultural Activity ◽  
Reconstruction Method ◽  
Land Change ◽  
Land Transformation ◽  
Mass Migration ◽  
Agricultural Areas

Long-term land changes are cumulatively a major driver of global environmental change. Historical land-cover/use change is important for assessing present landscape conditions and researching ecological environment issues, especially in eco-fragile areas. Arable land is one of the land types influenced by human agricultural activity, reflecting human effects on land-use and land-cover change. This paper selected Zhenlai County, which is part of the farming–pastoral zone of northern China, as the research region. As agricultural land transformation goes with the establishment of settlements, in this research, the historical progress of land transformation in agricultural areas was analyzed from the perspective of settlement evolution, and the historical reconstruction of arable land was established using settlement as the proxy between their inner relationships, which could be reflected by the farming radius. The results show the following. (1) There was little land transformation from nonagricultural areas into agricultural areas until the Qing government lifted the ban on cultivation and mass migration accelerated the process, which was most significant during 1907–1912; (2) The overall trend of land transformation in this region is from northeast to southwest; (3) Taking the topographic maps as references, the spatial distribution of the reconstructed arable land accounts for 47.79% of the maps. When this proxy-based reconstruction method is applied to other regions, its limitations should be noticed. It is important to explore the research of farming radius calculations based on regional characteristics. To achieve land-system sustainability, long-term historical land change trajectories and characteristics should be applied to future policy making.

scholarly journals An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: a geospatial approach

2018 ◽  
Vol 6 (4) ◽  
pp. 39-47 ◽  
Author(s):  
Firoz Ahmad ◽  
Md Meraj Uddin ◽  
Laxmi Goparaju
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Forest Fire ◽  
Future Climate ◽  
Future Climate Change ◽  
Land Use Land Cover ◽  
Tribal Population ◽  
Agricultural Vulnerability

AbstractGeospatial evaluation of various datasets is extremely important because it gives a better comprehension of the past, present and future and can therefore be significantly utilized in effective decision making strategies. This study examined the relationships, using geospatial tools, between various diversified datasets such as land use/land cover (LULC), long term Normalized Difference Vegetation Index (NDVI) based changes, long term forest fire points, poverty percentage, tribal percentage, forest fire hotspots, climate change vulnerability, agricultural vulnerability and future (2030) climate change anomalies (RCP-6) of Jharkhand state, India, for a better understanding and knowledge of its vegetation health, LULC, poverty, tribal population and future climate change impact. The long term NDVI (1982-2006) evaluation revealed negative change trends in seven northwest districts of Jharkhand state, these were: Hazaribag, Ramgarh, Palamu, Lohardaga, Chatra, Garhwa and Latehar. The forests as well as the agriculture of these districts have lost their greenness during this period. The forest fire frequency events were found to be more pronounced in the land use/land cover of “tropical lowland forests, broadleaved, evergreen, <1000 m” category, and were roughly twice the intensity of the “tropical mixed deciduous and dry deciduous forests” category. In the nine districts of Jharkhand it was found that 40 % of the population was living below the poverty line which is around twice the national average. The highest poverty districts, in percentage, were: Garwah (53.93), Palamu (49.24), Latehar (47.99) and Chatra (46.2). The southwest and south of Jharkhand state shows a tribal population density of more than 40%. The climate change vulnerability was found to be highest in the district of Saraikela followed by Pashchim Singhbhum, whereas agricultural vulnerability was found to be highest in the district of Pashchim Singhbhum followed by Saraikela, Garhwa, Simdega, Latehar, Palamu and Lohardaga. The temperature anomalies prediction for the year 2030 shows an increasing trend in temperature with values of 0.8°C to 1°C in the state of Jharkhand. The highest increases were observed in the districts of Pashchim Singhbhum, Simdega and Saraikela. Based on these evaluations we can conclude that a few of the districts of Jharkhand, such as Pashchim Singhbhum, Garhwa, Palamu and Latehar need to be prioritized for development on an urgent basis. The outcomes of this study would certainly guide the policymakers to prepare more robust plans when keeping in mind the future climate change impacts for the prioritization of various districts of Jharkhand which suffer from extreme poverty, diminished livelihood and insignificant agricultural productivity for the betterment of the people of Jharkhand based on their adaptive capacity.

scholarly journals Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

2021 ◽  
Vol 15 (9) ◽  
pp. 4261-4279
Author(s):  
Xiaodan Wu ◽  
Kathrin Naegeli ◽  
Valentina Premier ◽  
Carlo Marin ◽  
Dujuan Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Land Cover ◽  
Snow Cover ◽  
Area Coverage ◽  
Slope Aspect ◽  
Hindu Kush ◽  
Very High

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

Modeling the changing sediment yield of the Amazon under climate change and deforestation scenarios and the possible impacts on the Guiana coast

2020 ◽  
Author(s):  
Safaa Naffaa ◽  
L.P.H. (Rens) van Beek ◽  
Frances E.Dunn ◽  
Steven de Jong
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Sediment Yield ◽  
Coastal Erosion ◽  
Sediment Supply ◽  
Trapping Efficiency ◽  
Amazon River ◽  
Land Cover Changes

&lt;p&gt;The Amazon River is an important source of the sediment that is transported and accumulated along the coast of Suriname. As such it is an important factor in maintaining the coastline as this sediment is deposited in mud banks that move towards the shore and coalesce with it, thus preventing coastal erosion. Accordingly, a steady and adequate supply of sediment from the Amazon river is required especially considering increased coastal erosion rates that may occur as a result of rising sea levels due to climate change. Yet at the same time, climate change may alter the hydrological regime of the Amazon and influence its transport capacity, affecting sediment transport to the mouth and coast. Furthermore, the sediment supply to the river may be altered as a result of land cover changes and other anthropogenic activities, including deforestation and sediment trapping in existing and future planned reservoirs.&lt;br&gt;Studying the transport of sediment from source to sink and quantifying how future changes affect the mean rate of sediment supply to the Surinam coast and its variability will lead to a better understanding of the intricacies involved. We use a spatial-temporal process-based model together with a set of plausible scenarios of future change based on combinations of the Shared Socioeconomic Pathways (SSP) and the Representative Concentration Pathways (RCP). In this study, we used two models: PCRGLOB-Set and PCRGLOB-WB. PCRGLOB-SET is based on the RUSLE equation and is used to assess the local sediment supply including the effects of land cover changes. PCRGLOB-WB simulates hydrological responses and changes under climate and land-use change. Moreover, PCRGLOB-WB is used to determine the trapping efficiency of reservoirs. The PCRGLOB-WB model was applied to a business-as-usual scenario for the 21st century (SSP 2 with RCP 6.0) and we considered uncertainty in the projected climate by using 5 Global Climate Models (GCMs). We apply the model to different future scenarios considering climate, socioeconomic and land-use change. For validation, the observations of six stations along the Amazon river were compared to the estimations of the models for the historical period (1971-2010), which also serves as a reference run to evaluate changes in sediment production and sediment yield.&amp;#160;&lt;/p&gt;

scholarly journals Long-Term Land Use Land Cover Change in Urban Centers of Southwest Ethiopia From a Climate Change Perspective

2020 ◽  
Vol 2 ◽  
Author(s):  
Tesfaye Dessu ◽  
Diriba Korecha ◽  
Debela Hunde ◽  
Adefires Worku
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Kendall Test ◽  
Land Use Land Cover ◽  
Urban Centers ◽  
Southwest Ethiopia ◽  
Mann Kendall Test

Long-term urban land use land cover change (LULCC) dynamics and climate change trends in Southwest Ethiopia's four urban centers were examined for 60 years. Remote sensing, aerial photos, and Landsat, temperature, and rainfall data were analyzed from a climate change perspective over the Jimma, Bedelle, Bonga, and Sokorru urban centers of southwest Ethiopia from 1953 to 2018. Based on geospatial analysis and maximum likelihood supervised image classification techniques to classify LULCC categories, the Mann-Kendall test was applied to perform trend analyses on temperature and rainfall. The LULCC analysis revealed that built-up areas over the urban centers had shown an increasing trend, with the highest increment by 2,360 hectares over Jimma, while vegetation, wetland, and cropland declined due to conversion of plain lands to built-up areas and other similar zones. The pronounced decline of vegetation coverage was 1,427, 185,116, and 32 hectares in Jimma, Bedelle, Bonga, and Sokorru, respectively. Mann-Kendall test results showed a significant sign of intra-seasonal and inter-annual variability of rainfall while the summer and annual rainfall patterns remained less variable compared to other seasons. This study's findings revealed that when the mean between the two climatic normals of 1953–86 is compared with 1987–2018, the temperature has significantly increased in the latter three decades. The rapid expansion of built-up areas coupled with a sharp decline of green space or vegetation and agricultural/croplands could lead to gradual changes in LULCC classes, which have contributed to the changing of the local climate, especially the surface temperature and rainfall over the urban centers of southwest Ethiopia. Therefore, we recommend that the local urban administrations emphasize sustainable urban development by integrating urban planning policies with land use to protect the environment by adopting local municipal adaptation and national climate change strategies. Restoration of the local environment and creation of climate-smart cities could be critical to the resilience of urban dwellers and ecosystems to the changing climate by enhancing grass-root climate services. To that end, we recommend further advanced research to understand how urban LULC-related changes and other factors contribute to local and regional climates, as urban areas of Southwest Ethiopia are undergoing a rapid transformation of their rural surroundings.

Multigrain seabed sediment transport modelling for the south-west Australian Shelf

2009 ◽  
Vol 60 (7) ◽  
pp. 774 ◽  
Author(s):  
F. Li ◽  
C. M. Griffiths ◽  
C. P. Dyt ◽  
P. Weill ◽  
M. Feng ◽  
...  
Keyword(s):  
Climate Change ◽  
Sediment Transport ◽  
Continental Shelf ◽  
Morphological Changes ◽  
High Energy ◽  
Sediment Supply ◽  
Climate Change Scenarios ◽  
The South ◽  
South West

With increasing concerns about climate change and sea-level rise, there is a need for a comprehensive understanding of the sedimentary processes involved in the erosion, transport and deposition of sediment on the continental shelf. In the present paper, long-term and large-scale seabed morphological changes on the south-west Australian continental shelf were investigated by a comprehensive sediment transport model, Sedsim. The investigated area covers the continental shelf and abyssal basins of the south-western region. The regional seabed is sensitive to environmental forces and sediment supply, and most terrigenous sediment carried down by major rivers is trapped in inland lakes or estuaries. Only a small fraction of fine-grain sediment reaches the continental shelf. The simulation has also confirmed that the Leeuwin Current and high-energy waves play the most important roles in regional long-term seabed evolution. Although the numerical implementation only approximates some forcing and responses, it represents a significant step forward in understanding the nature of potential long-term seabed change as a response to possible climate change scenarios. The 50-year forecast on the seabed morphological changes provides a reference for the management of coastal and offshore resources, as well as infrastructure, in a sustainable way.

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