Evaluate the Effect of Topographic Factors and Lithology on Forest Cover Distribution: a Case Study of the Moroccan High Atlas

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

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Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

Journal of the Indian Society of Remote Sensing ◽

10.1007/bf03015117 ◽

1994 ◽

Vol 22 (1) ◽

pp. 21-29 ◽

Cited By ~ 5

Author(s):

S. Sudhakar ◽

R. K. Das ◽

D. Chakraborty ◽

B. K. Bardhan Roy ◽

A. K. Raha ◽

...

Keyword(s):

Forest Cover ◽

Forest Cover Type ◽

Cover Type

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The Risk of Water Erosion on the Foothills of the Middle High Atlas (Morocco): Tagueleft Basin Case Study : خطر التعرية المائية على السفوح بالأطلس الكبير الأوسط (المغرب): دراسة حالة حوض تاكلفت

Journal of natural sciences, life and applied sciences - مجلة العلوم الطبيعية و الحياتية والتطبيقية ◽

10.26389/ajsrp.m130620 ◽

2020 ◽

Vol 4 (3) ◽

Author(s):

Rizki Mohamed

Keyword(s):

Land Degradation ◽

Local Development ◽

Remote Sensing Data ◽

Potential Method ◽

Water Erosion ◽

High Atlas ◽

Negative Impacts ◽

The Impact

The Tagueleft basin is geographically located in the northern edges of the Middle High Atlas, which is a geomorphological fragile area. The impact of human activity has accelerated water erosion in this mountains area. This is reflected in dynamic and unstable foothills, a decrease in forests density and degradation in the production of the land. On the other hand, land degradation due to human overexploitation of natural resources has increased land degradation in the area. The interest in the risk of erosion on the foothills in the area under study comes in the context of our contribution to clarify the role of geomatical and geomorphological approaches in explaining and identifying the mechanisms responsible for current foothills dynamism through water erosion and its negative impacts on the environment and local development. The aim of the study was to use the EPM (Erosion Potential Méthod) which is formulated by Slobodan Gavrilovic for erosion in mountainous areas and to test the reliability of its results based on fieldwork and remote sensing data. The results of the erosion assessment and its quantification by applying the coefficient (W) for the theoretical model in the area under study have shown that erosion is very important and it touches on wide areas as it appears through the domain classification of the distribution erosion in Tagueleft basin.

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Monitoring Land Cover Changes in the Disaster-Prone Area: A Case Study of Cangkringan Sub-District, the Flanks of Mount Merapi, Indonesia

Forum Geografi ◽

10.23917/forgeo.v31i2.5324 ◽

2017 ◽

Vol 31 (2) ◽

pp. 209-219 ◽

Cited By ~ 2

Author(s):

Ronggo Sadono ◽

Hartono Hartono ◽

Mochammad Maksum Machfoedz ◽

Setiaji Setiaji

Keyword(s):

Land Cover ◽

Forest Cover ◽

Land Cover Changes ◽

Annual Crops ◽

The North ◽

Detection Analysis ◽

Prone Area ◽

Cover Class ◽

Forest Cover Types

Volcanic eruption is one of the natural factors that affect land cover changes. This study aimed to monitor land cover changes using a remote sensing approach in Cangkringan Sub-district, Yogyakarta, Indonesia, one of the areas most vulnerable to Mount Merapi eruption. Three satellite images, dating from 2001, 2006 and 2011, were used as main data for land cover classification based on a supervised classification approach. The land cover detection analysis was undertaken by overlaying the classification results from those images. The results show that the dominant land cover class is annual crops, covering 40% of the study area, while the remaining 60% consists of forest cover types, dryland farming, paddy fields, settlements, and bare land. The forests were distributed in the north, and the annual crops in the middle of the study area, while the villages and the rice fields were generally located in the south. In the 2001–2011 period, forests were the most increased land cover type, while annual crops decreased the most, as a result of the eruption of Mount Merapi in 2010. Such data and information are important for the local government or related institutions to formulate Detailed Spatial Plans (RDTR) in the Disaster-Prone Areas (KRB).

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Resilient pastures and sediment: a case study in Hawke's Bay

NZGA: Research and Practice Series ◽

10.33584/rps.17.2021.3432 ◽

2021 ◽

Vol 17 ◽

Author(s):

Dave Read

Keyword(s):

Forest Cover ◽

Single Species ◽

Independent Research ◽

Bare Ground ◽

Hill Country ◽

Regulatory Issue ◽

Sediment Loss ◽

Return On Capital

For many hill-country farms sediment will be a bigger regulatory issue than nitrates over the next decade. A dense, resilient pasture can reduce the risk of insidious sediment loss. Any ecosystem that relies on a few species is fragile. Sowing a single species leads to repeated re-sowing and increasing bare ground to remove competition, increasing the risk of sediment flows. An important issue during regulatory consultation will be establishing a natural, pre-human baseline for forest cover and documenting more recent changes in sediment flows. Hill country cropping and pasture renewal is incompatible with resilient pasture. This is a farmer’s perspective on a diverse and persisting hill country pasture-based system that can make a good return on capital without re-grassing or fodder cropping. Funding of independent research on pasture and fodder systems is essential if farmers are to make good decisions.

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Interdependent effects of climate variability and forest cover change on streamflow dynamics: a case study in the Upper Umvoti River Basin, South Africa

Regional Environmental Change ◽

10.1007/s10113-019-01521-8 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1963-1971

Author(s):

Karen Lebek ◽

Cornelius Senf ◽

David Frantz ◽

José A. F. Monteiro ◽

Tobias Krueger

Keyword(s):

South Africa ◽

Climate Variability ◽

River Basin ◽

Forest Cover ◽

Forest Cover Change

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Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy

Geosciences ◽

10.3390/geosciences10080309 ◽

2020 ◽

Vol 10 (8) ◽

pp. 309

Author(s):

Federico Valerio Moresi ◽

Mauro Maesano ◽

Alessio Collalti ◽

Roy C. Sidle ◽

Giorgio Matteucci ◽

...

Keyword(s):

Forest Cover ◽

Shallow Landslides ◽

Susceptibility Map ◽

Infinite Slope ◽

South Italy ◽

Physically Based ◽

Root Strength ◽

Forested Areas ◽

Forest Mountain

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards.

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Regional assessment of forest effect on watershed hydrology: Slovakia as a case study 405

Journal of Forest Science ◽

10.17221/55/2013-jfs ◽

2013 ◽

Vol 59 (No. 10) ◽

pp. 405-415

Author(s):

HlásnyT ◽

SitkováZ ◽

I. Barka

Keyword(s):

Water Regime ◽

Forest Cover ◽

Stand Density ◽

Watershed Hydrology ◽

Natural Conditions ◽

Tree Species Composition ◽

Weak Limits ◽

Spring Area ◽

Regulatory Capacity

Recently, the importance of forest effect on watershed hydrology has been increasingly recognized due to an elevated threat of floods and expected alterations of water regime in watersheds induced by climate change. We assessed the trade-off between natural conditions of 61 basic watersheds in Slovakia and expected water-regulatory capacity of forest in these watersheds. A multi-criteria decision-making scheme was proposed to calculate a coefficient for each watershed indicating the need to regulate its water regime as given by natural conditions, and another coefficient indicating the magnitude of forest water-regulatory capacity given by forest structure and distribution. Factors indicating the forest water-regulatory capacity were extent of forest cover, forest fragmentation and distribution in watersheds relative to the spring area, forest stand density and vertical structure, and tree species composition. The results indicate that the present structure and distribution of forests in Slovakia has potential to moderately regulate the water regime at the scale of basic watersheds. We identified critical watersheds where natural conditions imply the unfavourable water regime and/or the forest water-regulatory capacity is weak. Limits of forest effect on watershed hydrology and caveats for interpreting the presented findings are discussed. 

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Scale-Optimized Surface Roughness for Topographic Analysis

Geosciences ◽

10.3390/geosciences9070322 ◽

2019 ◽

Vol 9 (7) ◽

pp. 322 ◽

Cited By ~ 5

Author(s):

John B. Lindsay ◽

Daniel R. Newman ◽

Anthony Francioni

Keyword(s):

Surface Roughness ◽

Forest Cover ◽

Agricultural Land ◽

Spatial Scales ◽

Grid Cell ◽

Topographic Analysis ◽

Digital Elevation ◽

Locally Adaptive ◽

Elevation Model

Surface roughness is a terrain parameter that has been widely applied to the study of geomorphological processes. One of the main challenges in studying roughness is its highly scale-dependent nature. Determining appropriate mapping scales in topographically heterogenous landscapes can be difficult. A method is presented for estimating multiscale surface roughness based on the standard deviation of surface normals. This method utilizes scale partitioning and integral image processing to isolate scales of surface complexity. The computational efficiency of the method enables high scale sampling density and identification of maximum roughness for each grid cell in a digital elevation model (DEM). The approach was applied to a 0.5 m resolution LiDAR DEM of a 210 km2 area near Brantford, Canada. The case study demonstrated substantial heterogeneity in roughness properties. At shorter scales, tillage patterns and other micro-topography associated with ground beneath forest cover dominated roughness scale signatures. Extensive agricultural land-use resulted in 35.6% of the site exhibiting maximum roughness at micro-topographic scales. At larger spatial scales, rolling morainal topography and fluvial landforms, including incised channels and meander cut banks, were associated with maximum surface roughness. This method allowed for roughness mapping at spatial scales that are locally adapted to the topographic context of each individual grid cell within a DEM. Furthermore, the analysis revealed significant differences in roughness characteristics among soil texture categories, demonstrating the practical utility of locally adaptive, scale-optimized roughness.

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