The impact of permanent gullies on present-day land use and agriculture in loess areas (E. Poland)

Zgłobicki, Wojciech; Baran-Zgłobicka, Bogusława; Gawrysiak, Leszek; Telecka, Małgorzata

doi:10.1016/j.catena.2014.10.022

Cited by 32 publications

(11 citation statements)

References 31 publications

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“…Gully form of soil erosion causes extensive socioeconomic and environmental impacts, and most of them are negative (Valentin et al 2005;Marzolff et al 2011). According Zgłobicki et al (2015) gully erosion decreases the agricultural land, farm productivity/crop yields and grazing land. Also gully-based valley sediment yield accounted for 10 to 94 % of the total watershed sediment yield (Poesen et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessment of drivers and dynamics of gully erosion in case of Tabota Koromo and Koromo Danshe watersheds, South Central Ethiopia

Hassen

Bantider

2020

Geoenviron Disasters

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Globally erosion has been considered as an environmental and agricultural problem since the first agricultural revolution. In Ethiopia gully erosion has been recorded as a very serious and common socioeconomic and environmental problem of several watersheds. Therefore, the objective of this research was to assess the drivers, dynamics and impacts of gully erosion in case of Tabota Koromo and Koromo Danshe watersheds, South-Central Ethiopia. The intended objectives were achieved by triangulating the image analysis with the data obtained from GPS, group discussion, interview, house hold survey, field observation and field measurement. The result of the study indicated that gully erosion was initiated in the steep slope following the 1970s and 1980s land cover change and other anthropogenic factors. In addition, the soil properties and steep slope (gravity) of the land have contributed in exacerbating the problems. Gully erosion caused physical, social and economic impacts in the area. According to the participants on group discussion and interview, the socioeconomic impacts of gully erosion were loss of life for a 12 year boy, injuries of five live stocks, and decline of yields throughout the year. Result from field measurement and observation depicted that loss of soil (1,080,782.6m 3) and loss of biodiversity are the major physical impacts of gully erosion. Yet there are promising conditions to rehabilitate the gullied area for sustainable ecosystem services in the watersheds. These promising conditions are the availability of high labour forces, access of transportation, civic societies working on natural resource conservation and the green economy development policy of the country. Therefore, concerned government bodies and the local communities at different level need to set plan and work for implementation in a way to use the existing opportunities.

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Section: Introductionmentioning

confidence: 99%

Assessment of drivers and dynamics of gully erosion in case of Tabota Koromo and Koromo Danshe watersheds, South Central Ethiopia

Hassen

Bantider

2020

Geoenviron Disasters

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“…In the long term, soil erosion causes these gullies to change the landscape, resulting in land degradation and geomorphic changes to river systems [7,8]. Gullies can cause a variety of environmental and socio-economic issues, such as decrease in soil productivity and damage to infrastructure [9,10], and in some cases, landslides [11]. In Australia, especially in Queensland, gully erosion is one of the main geo-environmental degradation processes resulting from excessive land clearing, inappropriate land use, and high-intensity rainfall [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Shahabi

Jarihani

Piralilou

et al. 2019

Sensors

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Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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“…There are some land clearing, such as land clearing for farming [12], [14], [15], [47], [48], settlement [49], [50], industry [51], final disposal site [52] and other. The objective of the land clearing is to convert unproductive land into productive land [20], [21], [23], [46], [48].…”

Section: A Land Clearingmentioning

confidence: 99%

A review on fuzzy multi-criteria decision making land clearing for oil palm plantation

Hamdani

Wardoyo

2015

Int. J. Adv. Intell. Informatics

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Our review paper research categorize the methods in the method of Fuzzy Multi-Criteria Decision Making (FMCDM) to find the method is widely used in the case of land clearing for plantation. Model FMCDM is used to assess the parameter in multi-criteria-based decision making. The dominant percentage of the result was obtained using Fuzzy Analytic Hierarchy Process (FAHP) method. While the application of other methods for the same problem are Fuzzy Ordered Weighted Averaging (FOWA), Fuzzy Elimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality (FELECTRE), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), Fuzzy, Artificial Neural Networks (FANNs) has less. Some the research result also implemented hybrid in FMCDM Method to give some weight in the assessment of decision making. There was also a paper which integrates FMCDM to the GIS method on the land clearing. Therefore, it is concluded that the issue on the land clearing can be done through collaboration of several models of FMCDM, so that it can be developed by involving the decision model using multi-stakeholder model

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The impact of permanent gullies on present-day land use and agriculture in loess areas (E. Poland)

Cited by 32 publications

References 31 publications

Assessment of drivers and dynamics of gully erosion in case of Tabota Koromo and Koromo Danshe watersheds, South Central Ethiopia

Assessment of drivers and dynamics of gully erosion in case of Tabota Koromo and Koromo Danshe watersheds, South Central Ethiopia

A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

A review on fuzzy multi-criteria decision making land clearing for oil palm plantation

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