scholarly journals How Can Geomorphology Inform Ecological Restoration? A Synthesis of Geophysical and Biological Assessment to Determine Restoration Priorities

2021 ◽  
Author(s):  
◽  
Leicester Cooper
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Reference Site ◽  
Slope Angle ◽  
Slope Aspect ◽  
Total N ◽  
Olsen P ◽  
Restoration Site ◽  
Soil Variables ◽  
Indigenous Forest

<p>The central concern that this study addresses is how an understanding of geomorphological processes and forms may inform ecological restoration; particularly practical restoration prioritisation. The setting is that of a hill country gully system covered in grazing pasture which historically would have been cloaked in indigenous forest. The study examines theory in conjunction with an application using a case study centred on Whareroa Farm (the restoration site) and Paraparaumu Scenic Reserve (the reference site) on the southern Kapiti Coast, north of Wellington. The impact that the change of land use has had on the soil and geomorphic condition of Whareroa and the influence the changes may have on the sites restoration is investigated. The thesis demonstrates a method of choosing reference sites to be used as templates for rehabilitating the restoration site. Geographical Information Systems and national databases are used and supplemented with site inspection. The reference site chosen, Paraparaumu Scenic Reserve, proved to be a good template for the restoration site particularly given that it is located in the midst of a heavily modified area. On-site inspection considering dendritic pattern and floristic composition confirms the database analysis results. Soil variables (bulk density, porosity, soil texture, pH, Olsen P, Anaerobic Mineralisable N, Total N (AMN), Total C and C:N ratio) are investigated and statistical comparisons made between the sites to quantify changes due to land-use change, i.e. deforestation and subsequent pastoral grazing. Factors investigated that may explain the variation in the soil variables were site (land use), hillslope location, slope aspect, and slope angle. Permutation tests were conducted to investigate the relationships between the independent factors and the SQI (dependent soil variables). Land use and slope angle were most frequent significant explanatory factors of variation, followed by hillslope location whilst slope aspect only influenced soil texture. A number of soil variables at Whareroa were found to be outside the expected range of values for an indigenous forest soil including AMN, Total N, Olsen P, and pH ...</p>

scholarly journals How Can Geomorphology Inform Ecological Restoration? A Synthesis of Geophysical and Biological Assessment to Determine Restoration Priorities

2021 ◽  
Author(s):  
◽  
Leicester Cooper
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Reference Site ◽  
Slope Angle ◽  
Slope Aspect ◽  
Total N ◽  
Olsen P ◽  
Restoration Site ◽  
Soil Variables ◽  
Indigenous Forest

<p>The central concern that this study addresses is how an understanding of geomorphological processes and forms may inform ecological restoration; particularly practical restoration prioritisation. The setting is that of a hill country gully system covered in grazing pasture which historically would have been cloaked in indigenous forest. The study examines theory in conjunction with an application using a case study centred on Whareroa Farm (the restoration site) and Paraparaumu Scenic Reserve (the reference site) on the southern Kapiti Coast, north of Wellington. The impact that the change of land use has had on the soil and geomorphic condition of Whareroa and the influence the changes may have on the sites restoration is investigated. The thesis demonstrates a method of choosing reference sites to be used as templates for rehabilitating the restoration site. Geographical Information Systems and national databases are used and supplemented with site inspection. The reference site chosen, Paraparaumu Scenic Reserve, proved to be a good template for the restoration site particularly given that it is located in the midst of a heavily modified area. On-site inspection considering dendritic pattern and floristic composition confirms the database analysis results. Soil variables (bulk density, porosity, soil texture, pH, Olsen P, Anaerobic Mineralisable N, Total N (AMN), Total C and C:N ratio) are investigated and statistical comparisons made between the sites to quantify changes due to land-use change, i.e. deforestation and subsequent pastoral grazing. Factors investigated that may explain the variation in the soil variables were site (land use), hillslope location, slope aspect, and slope angle. Permutation tests were conducted to investigate the relationships between the independent factors and the SQI (dependent soil variables). Land use and slope angle were most frequent significant explanatory factors of variation, followed by hillslope location whilst slope aspect only influenced soil texture. A number of soil variables at Whareroa were found to be outside the expected range of values for an indigenous forest soil including AMN, Total N, Olsen P, and pH ...</p>

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression

2013 ◽  
Vol 13 (12) ◽  
pp. 3339-3355 ◽  
Author(s):  
M. C. Mărgărint ◽  
A. Grozavu ◽  
C. V. Patriche
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Spatial Variability ◽  
Landslide Susceptibility ◽  
Scientific Literature ◽  
Characteristic Curve ◽  
Slope Angle ◽  
Slope Aspect ◽  
Hazard Mapping ◽  
Correct Identification

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant contributing factors, which leads to the improvement of predictions regarding this type of geomorphologic processes. In the scientific literature, different weightings are assigned to these factors, but contain large variations. This study aims to identify the spatial variability and range of variation for the coefficients of landslide predictors in different geographical conditions. Four sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both on the hilly areas (the Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, land use, and lithology. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in a georeferenced environment. The logistic regression was applied separately for the four study sectors as the statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were produced, the values of which were classified by using the natural breaks method (Jenks). The accuracy of the logistic regression outcomes was evaluated using the ROC (receiver operating characteristic) curve and AUC (area under the curve) parameter, which show values between 0.852 and 0.922 for training samples, and between 0.851 and 0.940 for validation samples. The values of coefficients are generally confined within the limits specified by the scientific literature. In each sector, landslide susceptibility is essentially related to some specific predictors, such as the slope angle, land use, slope height, and lithology. The study points out that the coefficients assigned to the landslide predictors through logistic regression are capable to reveal some important characteristics in landslide manifestation. The study also shows that the logistic regression could be an alternative method to the current Romanian methodology for landslide susceptibility and hazard mapping.

scholarly journals Assessing the spatial variability of weights of landslide causal factors in different regions from Romania using logistic regression

2013 ◽  
Vol 1 (2) ◽  
pp. 1749-1774 ◽  
Author(s):  
M. C. Mărgărint ◽  
A. Grozavu ◽  
C. V. Patriche
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Spatial Variability ◽  
Landslide Susceptibility ◽  
Scientific Literature ◽  
Slope Angle ◽  
Slope Aspect ◽  
Correct Identification ◽  
Channel Network ◽  
Causal Factors

Abstract. In landslide susceptibility assessment, an important issue is the correct identification of significant causal factors, which leads to the improvement of predictions regarding this type of geomorphological processes. In the scientific literature, different weightings are assigned to these factors, but with large variations. This study aims to identify the spatial variability and range of variation of landslide causal factors in different geographical conditions. Four square sectors of 15 km × 15 km (225 km2) were selected for analysis from representative regions in Romania in terms of spatial extent of landslides, situated both in hilly areas (Transylvanian Plateau and Moldavian Plateau) and lower mountain region (Subcarpathians). The following factors were taken into consideration: elevation, slope angle, slope height, terrain curvature (mean, plan and profile), distance from drainage network, slope aspect, surface lithology and land use. For each sector, landslide inventory, digital elevation model and thematic layers of the mentioned predictors were achieved and integrated in georeferenced environment. The logistic regression was applied separately for the four study sectors, as statistical method for assessing terrain landsliding susceptibility. Maps of landslide susceptibility were achieved, the values of which were classified using the natural breaks method (Jenks). The accuracy of logistic regression outcomes was evaluated using the ROC curve and AUC parameter, which show values between 0.852 and 0.922. The values of factor weights are generally placed within the limits specified by the scientific literature. For all study sectors, the prevailing factors for landslide susceptibility are slope angle, land use and slope height above channel network. The study points out that the weights assigned to the causal factors through logistic regression are capable to reveal some important regional characteristics in landslides manifestation.

scholarly journals Land use and land cover geoinformation properties and its influence on the landslide susceptibility zonation of road network

2017 ◽  
Author(s):  
Bruno M. Meneses ◽  
Susana Pereira ◽  
Eusébio Reis
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Road Network ◽  
Slope Angle ◽  
Information Value ◽  
Slope Aspect ◽  
The Road ◽  
Landslide Susceptibility Zonation ◽  
On The Road

Abstract. This paper evaluates the influence of land use and land cover (LUC) geoinformation with different properties on landslide susceptibility zonation of the road network in Zêzere watershed (Portugal). The Information Value method was used to assess landslide susceptibility using two models: one including detailed LUC geoinformation (Portuguese Land Cover Map – COS) and other including more generalized LUC geoinformation (Corine Land Cover – CLC). A set of six fixed independent layers were considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope over area ratio, soil, and lithology), while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as dependent layer, including 259 shallow landslides obtained from photo-interpretation of orthophotos of 2005 and further validated in three sample areas (128 landslides). The landslide susceptibility maps were merged into road network geoinformation, and resulted in two landslide susceptibility road network maps. Models performance was evaluated with success rate curves and area under the curve. Landslide susceptibility results obtained in the two models are very good, but in comparison the model obtained with more detailed LUC geoinformation (COS) produces better results in the landslide susceptibility zonation and on the road network detection with the highest landslide susceptibility. This last map also provides more detailed information about the locals where the next landslides will probably occur with possible road network disturbances.

scholarly journals Landslide susceptibility assessment using the maximum entropy model in a sector of the Cluj–Napoca Municipality, Romania

2018 ◽  
Vol 20 (1) ◽  
pp. 130-146 ◽  
Author(s):  
Anna–Hajnalka KEREKES ◽  
Szilárd Lehel POSZET ◽  
Andrea GÁL
Keyword(s):  
Land Use ◽  
Maximum Entropy ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Large Degree ◽  
Influential Factors ◽  
Slope Aspect ◽  
Accurate Information ◽  
Susceptibility Map ◽  
Entropy Model

The administrative territory of Cluj–Napoca, due to its specific geological and geomorphological characteristics and anthropic activities, has been affected for a long time by landslides. Thus, it becomes necessary to analyse affected areas with different spatial methods, with the aim of generating landslide susceptibility maps. In this research, we studied the most prone area of the city, the Becaș stream watershed, situated in the Southern part of the municipality. The aim of this paper is to generate a valid susceptibility map, to be able to raise awareness about the existing situation: due to human induced activities and rapid urban growth, the peripheral part of Cluj–Napoca becomes more and more prone to mass–movements. We used the maximum entropy (MaxEnt) model, which was fed with accurate information on the existing landslides and seven landslide–causing factors: slope, aspect, land–use, depth of fragmentation, geology and plan– and profile curvature. The results confirm that the most influential factors are the land use and slope–angle, affected in a large degree by human activities. The accuracy of the generated map was verified using the AUC method, proving a very good performance (0.844) of the applied model.

scholarly journals Changes in Soil C, N, and P Concentrations and Stoichiometry in Karst Trough Valley Area under Ecological Restoration: The Role of Slope Aspect, Land Use, and Soil Depth

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 144
Author(s):  
Tianyang Li ◽  
Jiangmin Zeng ◽  
Binghui He ◽  
Zhanpeng Chen
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Soil Depth ◽  
Interactive Effects ◽  
Slope Aspect ◽  
Soil C ◽  
Soil P ◽  
Soil C And N ◽  
C And N ◽  
Valley Area

This study aims to investigate the roles of slope aspect, land use and soil depth in altering the soil organic carbon (C), total nitrogen (N), and total phosphorus (P) traits in the karst trough valley area experiencing extensive ecological restoration. A total of 54 soil samples were collected at 0–10, 10–20, and 20–30 cm soil depths from secondary forest, plantation forest, and grassland on the relatively more shaded east-facing slope and the contrasting west-facing slope, respectively. The independent and interactive effects of slope aspect, land use, and soil depth on soil C, N, and P concentrations and stoichiometry were determined. The results show that soil C and N concentrations were markedly higher on the east-facing slope than on the west-facing slope, and soil P concentrations showed an opposite trend, leading to significant differences in soil C:P and N:P but not in C:N ratios between the two aspects. Soil C and N concentrations were not affected by land use, and soil P concentration was significantly higher in plantation forest than in secondary forest and grassland. Soil C and N concentrations significantly decreased with increasing soil depth, but soil P concentration presented no significant changes with soil depth. Both the land use and soil depth did not differ in terms of their elemental stoichiometry. There were no significant interactive effects of slope aspect, land use and soil depth on soil C, N, and P traits. Our results indicate that soil C, N, and P changes are more sensitive to slope aspect rather than land use and soil depth in the karst trough valley area under ecological restoration.

scholarly journals Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

2019 ◽  
Vol 19 (3) ◽  
pp. 471-487 ◽  
Author(s):  
Bruno M. Meneses ◽  
Susana Pereira ◽  
Eusébio Reis
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Road Network ◽  
Slope Angle ◽  
Information Value ◽  
Slope Aspect ◽  
The Road ◽  
Landslide Susceptibility Zonation ◽  
On The Road

Abstract. This work evaluates the influence of land use and land cover (LUC) data with different properties on the landslide susceptibility zonation of the road network in the Zêzere watershed (Portugal). The information value method was used to assess the landslide susceptibility using two models: one including detailed LUC data (the Portuguese Land Cover Map – COS) and the other including more generalized LUC data (the CORINE Land Cover – CLC). A set of fixed independent layers was considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope-over-area ratio, soil, and lithology) while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as a dependent layer, including 259 shallow landslides obtained from the photointerpretation of orthophotos from 2005, and further validated in three sample areas. The landslide susceptibility maps were assigned to the road network data and resulted in two landslide susceptibility road network maps. The models' performance was evaluated with prediction and success rate curves and the area under the curve (AUC). The landslide susceptibility results obtained in the two models present a high accuracy in terms of the AUC (>90 %), but the model with more detailed LUC data (COS) produces better results in the landslide susceptibility zonation on the road network with the highest landslide susceptibility.

scholarly journals Landslide susceptibility assessment and mapping using statistical and data mining models in Iran

2021 ◽  
Author(s):  
Abdulaziz Hanifinia ◽  
Habib Nazarnejad ◽  
Saeed Najafi ◽  
Aiding Kornejady ◽  
Hamid Reza Pourghasemi
Keyword(s):  
Data Mining ◽  
Land Use ◽  
Shannon Entropy ◽  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Support Vector ◽  
The Village

Abstract This study applied to evaluate landslide susceptibility using four data mining models including, “Generalized Linear Model (GLM)”, “Maximum Entropy (ME)”, “Artificial Neural Network (ANN)”, and “Support Vector Machine (SVM)” in Cherikabad Watershed in Urmia City, Iran. In particular, Shannon entropy was used to assess the intercomparison of factors’ classes. Eleven factors including, elevation, slope angle, slope aspect, geological formation, annual mean rainfall, land use/ land cover, distance to the village, distance to faults, distance to roads, distance to streams, and NDVI used in the current study. Landslide inventory map was identified using Google Earth imagery, extensive field surveys, and scrutinizing archived data. The produced landslide susceptibility maps were evaluated by the AUROC index. The results of performance metrics revealed that the Shannon entropy with an AUROC of 0.879 proved highly reliable and so is the intercomparison analysis of factors’ classes derived from it. Additionally, the goodness-of-fit of the GLM, ME, ANN, and SVM models were 0.763, 0.740, 0.926, and 0.924, while their predictive powers were 0.751, 0.727, 0.917, and 0.935, respectively. Hence, the results indicated that the SVM model can be introduced as the superior model for the study area based on which the most critical factors affecting landslides were found to be elevation, annual mean rainfall, and distance to the village. The results of this work are of great use for land use planning in landslide-prone areas with similar geo-topological, geomorphological, and climatic conditions.

LAND USE TYPES AND SOIL CHARACTERISTICTS: A CASE STUDY IN HUONG VINH COMMUNE, HUONG TRA TOWN, THUA THIEN HUE PROVINCE

2017 ◽  
Vol 126 (4B) ◽  
Author(s):  
Trần Thanh Đức
Keyword(s):  
Land Use ◽  
Sweet Potato ◽  
Soil Analysis ◽  
Soil Characteristics ◽  
Low Ph ◽  
Clay Loam ◽  
Total N ◽  
Land Use Types ◽  
Total P

This research carried out in Huong Vinh commune, Huong Tra town, Thua Thien Hue province aimed to identify types of land use and soil characteristics. Results showed that five crops are found in Huong Vinh commune including rice, peanut, sweet potato, cassava and vegetable. There are two major soil orders with four soil suborders classified by FAO in Huong Vinh commune including Fluvisols (Dystric Fluvisols<em>, </em>Gleyic Fluvisols and Cambic Fluvisols) and Arenosols (Haplic Arenosols). The results from soil analysis showed that three soil suborders including Dystric Fluvisols<em>, </em>Gleyic Fluvisols and Cambic Fluvisols belonging to Fluvisols were clay loam in texture, low pH, low in OC, total N, total P<sub>2</sub>O<sub>5</sub> and total K<sub>2</sub>O. Meanwhile, the Haplic Arenosols was loamy sand in texture, poor capacity to hold OC, total N, total P<sub>2</sub>O<sub>5</sub> and total K<sub>2</sub>O

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