An example of the effects of anthropogenic changes on natural environment in the Apulian karst (southern Italy)

Andriani, Gioacchino Francesco; Walsh, Nicola

doi:10.1007/s00254-008-1604-6

Cited by 34 publications

(19 citation statements)

References 28 publications

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“…These valleys are locally named "lame", and show subvertical rocky flanks and a flat bottom. Valleys cutting the Murge area act as water channels only during and immediately after heavy rainfall, and can be classified as episodic (ephemeral) rivers (Andriani andWalsh, 2009, Gioia, et al, 2011). In recent years, the important hydraulic function of lame as drainage lines has been heavily altered.…”

Section: Study Area and Datamentioning

confidence: 99%

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Aquilino

Tarantino

Fratino

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet"s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and costeffective means to map and analyse land cover changes over time that can be used as input in land management and policy decisionmaking.

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Section: Study Area and Datamentioning

confidence: 99%

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Aquilino

Tarantino

Fratino

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The preference for large stones can explain the low occurrence of large boulders in the deforested catchment compared with the forested catchment. Selective removal of boulders is also a common stone‐clearing technique used for cultivation in other regions with karst relief or affected by freeze action (Andriani & Walsh, ; Gams, Nicod, Julian, Anthony, & Sauro, ; Kovář, Bačinová, Loula, & Fedorova, ). Rock extraction decreased the boulder areas to only 8.1% of catchment area in the deforested catchment compared with 30% of the forested catchment and gained new land for agriculture.…”

Section: Discussionmentioning

confidence: 99%

Geomorphological and sedimentological indicators of land degradation (Meghalaya Plateau, NE India)

2018

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Replacement of forests by cultivation in hilly regions can dramatically increase overland flow and soil erosion, frequently resulting in land degradation. This study aimed to determine the impact of long‐term human activity and natural geomorphic processes in land degradation of 2 catchments located in the Meghalaya Plateau in northeast India. Both catchments are built by deeply weathered granites with hilly topography but have contrasting land uses. Forested catchment (1.5 ha) is covered by natural deciduous forest, whereas deforested catchment (3.5 ha) is affected by slash and burn cultivation and boulder extraction. Detailed geomorphological mapping and sediment pattern analysis revealed a complex anthropogenic role in land degradation. Human activity contributes directly to the creation of new landforms in the deforested catchment and indirectly by influencing the course and intensity of geomorphological processes, masking the pre‐existing natural relief. Discontinuous agricultural terraces, zero‐order valleys, and furrows created coexisting cascade system and direct connectivity of water and sediment transport within the same slope. Higher content of fine‐grained material and lower content of boulders in the deforested catchment reflect overlapping of 2 traditional occupations of the local population: agriculture and selective boulders extraction, respectively. Human activity combined with accelerated erosion of abundant weathered material led to the transformation of incised valley bottom into flat accumulation plain as well as the extension and aggradation of the alluvial fan. This process increased the proportion of flat landforms favourable for cultivating the deforested catchment under growing population pressure.

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“…), conveyance (culverts, channels, pipes) as well as detention systems, and transition from native to non-native vegetation species (grass, palms, woody plants, etc.). These conversions typically result in hydromodification of stream systems, increasing wet weather runoff and shifting the timing and volume of flood peaks (Murdock et al 2004;Wissmar et al 2004;White and Greer 2006;Vicars-Groening and Williams 2007;Beighley et al 2008;Andriani and Walsh 2009;Im et al 2009;Quan et al 2010). Increased flow rates typically lead to extensive channel erosion and instability (Bledsoe and Watson 2001;Miller and Kochel 2010).…”

Section: Introductionmentioning

confidence: 98%

Integrating hydrologic modeling and land use projections for evaluation of hydrologic response and regional water supply impacts in semi-arid environments

Hogue

2011

Environ Earth Sci

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Semi-arid environments are generally more sensitive to urbanization than humid regions in terms of both hydrologic modifications and water resources sustainability. The current study integrates hydrologic modeling and land use projections to predict long-term impacts of urbanization on hydrologic behavior and water supply in semi-arid regions. The study focuses on the Upper Santa Clara River basin in northern Los Angeles County, CA, USA, which is undergoing rapid and extensive development. The semi-distributed Hydrologic Simulation Program Fortran (HSPF) model is parameterized with land use, soil, and channel characteristics of the study watershed. Model parameters related to hydrologic processes are calibrated at the daily time step using various spatial configurations of precipitation and parameters. Potential urbanization scenarios are generated on the basis of a regional development plan. The calibrated (and validated) model is run under the proposed development scenarios for a 10 year period. Results reveal that increasing development increases total annual runoff and wet season flows, while decreases are observed in existing baseflow and groundwater recharge during both dry and wet seasons. As development increases, medium-sized storms increase in both peak flow and overall volume, while low and high flow events (extremes) appear less affected. Urbanization is also shown to decrease natural recharge and, when considered at the regional scale, may result in a loss of critical water supply to Southern California. The current study provides a coupled framework for a decision support tool that can guide efforts involved in regional urban development planning and water supply management.

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An example of the effects of anthropogenic changes on natural environment in the Apulian karst (southern Italy)

Cited by 34 publications

References 28 publications

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Geomorphological and sedimentological indicators of land degradation (Meghalaya Plateau, NE India)

Integrating hydrologic modeling and land use projections for evaluation of hydrologic response and regional water supply impacts in semi-arid environments

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