scholarly journals Consideration of land use in the evaluation of erosive processes in gully erosion

2020 ◽  
Vol 24 ◽  
pp. e25
Author(s):  
Tamara Vieira Pascoto ◽  
Simone Andrea Furegatti ◽  
Anna Silvia Palcheco Peixoto
Keyword(s):  
Land Use ◽  
Visual Analysis ◽  
Aerial Images ◽  
Aerial Photos ◽  
City Hall ◽  
Erosion Processes ◽  
Main Factors ◽  
The City ◽  
Erosive Feature

There are several factors that directly or indirectly influence erosion processes. In order to properly understand the behavior of these processes, some factors need to be analyzed together. Determining them wrongly can compromise the study resulting in wrong actions. For this reason, methodologies are always sought to measure them quantitatively and qualitatively in the most accurate possible way. Land use is one of the main factors liable to inaccuracies in its determination. To use this parameter in mapping erosive processes, researchers need to delimit it, classify it, and measure it. In order to better understand the complexity of considering this parameter, the present study analyzed an erosive feature that, although stabilized, has a component in constant development. Initially, a visual analysis indicated the same classification of land use for both conditions, despite having different behaviors, leading to the need for a detailed analysis. Such analysis comprised a historical survey through aerial photos and interviews with residents and employees of the city hall about the evolution of the feature from 2008 to 2019. It also included the analysis of other influencing factors that could be responsible for this difference in behavior in the area. Two different traces of the contribution areas of the gully and branch were also considered. One considering only aerial images, and the other considering the knowledge acquired during the research about the evolution of the feature. It was concluded, then, that an analysis of the use-only occupation factor based on aerial images can accentuate the inaccuracy of the measurement of this factor.

scholarly journals Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network

2020 ◽  
Vol 12 (24) ◽  
pp. 4149
Author(s):  
Maxim Samarin ◽  
Lauren Zweifel ◽  
Volker Roth ◽  
Christine Alewell
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Soil Erosion ◽  
Convolutional Neural Network ◽  
Temporal Trends ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Quality Data ◽  
Alpine Grasslands ◽  
Erosion Processes

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery.

scholarly journals AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE

2015 ◽  
Vol II-3/W5 ◽  
pp. 369-376 ◽  
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Main Idea ◽  
Aerial Images ◽  
Land Use Classification ◽  
Inference Procedure ◽  
Cover Layer

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a <i>land cover layer</i> and a <i>land use layer</i>. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

Identification of Soil Erosion in Alpine Grasslands on High-Resolution Aerial Images: Switching from Object-based Image Analysis to Deep Learning?

2020 ◽  
Author(s):  
Lauren Zweifel ◽  
Maxim Samarin ◽  
Katrin Meusburger ◽  
Volker Roth ◽  
Christine Alewell
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Soil Erosion ◽  
Soil Degradation ◽  
Aerial Images ◽  
Alpine Grasslands ◽  
Object Based Image Analysis ◽  
Erosion Processes ◽  
Object Based ◽  
Land Use Practices

&lt;p&gt;Soil erosion in Alpine grassland areas is an ecological threat caused by the extreme topography, prevailing climate conditions and land-use practices but enhanced by climate change (e.g., heavy precipitation events, changing snow dynamics) in combination with changing land-use practices (e.g, more intensely used pastures). To increase our understanding of ongoing soil erosion processes in Alpine grasslands, there is a need to acquire detailed information on spatial extension and temporal trends.&lt;/p&gt;&lt;p&gt;In the past, we have successfully applied a semi-automatic method using an object-based image analysis (OBIA) framework with high-resolution aerial images (0.25-0.5m) and a digital terrain model (2m) to map erosion features in the Central Swiss Alps (Urseren Valley, Canton Uri, Switzerland). Degraded sites are classified according to the major erosion process (shallow landslides; sites with reduced vegetation cover affected by sheet erosion) or triggering factors (trampling by livestock; management effects) (Zweifel et al. 2019). We now aim to apply a deep learning (DL) model with the purpose of fast and efficient spatial upscaling(e.g., alpine-wide analysis). While OBIA yields high quality results, there are multiple constraints, such as labor-intensive steps and the requirement of expert knowledge, which make the method unsuitable for larger scale applications. The results of OBIA are used as a training dataset for our DL model. The DL approach uses fully-convolutional networks with the U-Net architecture and is capable of rapid segmentation and classification to identify areas with reduced vegetation cover and bare soil sites.&lt;/p&gt;&lt;p&gt;Results for the Urseren Valley (Canton Uri, Switzerland) show an increase in total area affected by soil degradation of 156 &amp;#177;18% during a 16-year observation period (2000-2016). A comparison of the two methods (OBIA and DL) shows that DL results for the Urseren Valley follow similar trends for the 16-year period and that the segmentations of eroded sites are in good agreement (IoU = 0.83). First transferability tests to other valleys not considered during training of the DL model are very promising, confirming that DL is a well-suited and efficient method for future projects to map and assess soil erosion processes in grassland areas at regional scales.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;L. Zweifel, K. Meusburger, and C. Alewell. Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment. Remote Sensing of Environment, 235, 2019.&lt;/p&gt;

scholarly journals Land Use/Cover Change Detection and Urban Sprawl Analysis in Bandar Abbas City, Iran

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Mohsen Dadras ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Noordin Ahmad ◽  
Biswajeet Pradhan ◽  
Sahabeh Safarpour
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Sprawl ◽  
Large Scale ◽  
Land Use Changes ◽  
Urban Region ◽  
Aerial Photos ◽  
Bandar Abbas ◽  
The City

The process of land use change and urban sprawl has been considered as a prominent characteristic of urban development. This study aims to investigate urban growth process in Bandar Abbas city, Iran, focusing on urban sprawl and land use change during 1956–2012. To calculate urban sprawl and land use changes, aerial photos and satellite images are utilized in different time spans. The results demonstrate that urban region area has changed from 403.77 to 4959.59 hectares between 1956 and 2012. Moreover, the population has increased more than 30 times in last six decades. The major part of population growth is related to migration from other parts the country to Bandar Abbas city. Considering the speed of urban sprawl growth rate, the scale and the role of the city have changed from medium and regional to large scale and transregional. Due to natural and structural limitations, more than 80% of barren lands, stone cliffs, beach zone, and agricultural lands are occupied by built-up areas. Our results revealed that the irregular expansion of Bandar Abbas city must be controlled so that sustainable development could be achieved.

scholarly journals Monitoring urban greenness evolution using multitemporal Landsat imagery in the city of Erbil (Iraq)

2018 ◽  
Vol 62 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Shwan O. Hussein
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Spatiotemporal Analysis ◽  
Assessment Method ◽  
Green Areas ◽  
Green Vegetation ◽  
Spectral Mixture ◽  
The City

Most cities in the world have experienced major developments in the past 20–25 years. However, research has showed that the development aspect of these cities has led to a decrease in green areas. This paper aims to assess the spatiotemporal variations of urban green areas during the period 1990–2015 with special regard to city of Erbil. The study uses a mix of fuzzy functions, linear spectral mixture analysis, and maximum likelihood classification for the classification of Landsat imagery from 1990 to 2015 to extract the four main classes of land use, namely agricultural land, vacant land, built-up land, and green vegetation. Both the classification approaches used in this research produced excellent and reliable results, as an overall accuracy of more than 80% was able to be obtained. The spatiotemporal analysis of land use within the city of Erbil shows a series of major changes between 1990 and 2015. Therefore, the results of the spatiotemporal evolution of urban greenness assessment in the Erbil region can be used both for spatial planning purposes and as an urban greenness assessment method in dry climate areas.

scholarly journals TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 139-146
Author(s):  
C. Yang ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Infrared Band ◽  
Rgb Images ◽  
Geospatial Databases

<p><strong>Abstract.</strong> Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%.</p>

scholarly journals PECULIARITIES OF THE TERRITORY OF THE CITY OF LVIV (UKRAINE) AND THEIR INFLUENCE ON THE HYDROLOGICAL REGIME AND EROSION PROCESSES

2021 ◽  
Author(s):  
Viktor Skrobala ◽  
◽  
Sofiya Marutyak ◽  
Keyword(s):  
Land Use ◽  
Hydrological Regime ◽  
Spatial Arrangement ◽  
Specific Weight ◽  
Topographic Maps ◽  
Practical Significance ◽  
Forest Park ◽  
Erosion Processes ◽  
The Baltic ◽  
The City

The object of research is the territory of the city of Lviv (Ukraine). Lviv (geographical coordinates: 49N50, 24E00) is the largest city in Western Ukraine, with a population of over 720,000 inhabitants. The city is located in the western part of the Volyn-Podilska Upland, on the line of the Main European Ridge of the Baltic and Black Sea basins. Subject of research is relief parameters (maximum, average and minimum heights, vertical dismemberment and steepness of the surface) and land use characteristics (building intensity, phytocenotic cover). The purpose of research is to study the features of the territory of Lviv from the standpoint of influence on the hydrological regime and erosion processes. Methodology. Peculiarities of the territory of Lviv were studied by processing topographic maps using aerial photography materials and route surveys. Morphometric analysis of the relief was performed on topographic maps of scale 1: 10000 by dividing the map into elementary squares with an area of 0.25 km2. The research covers an area of 100.25 km2 (401 elementary squares). Within the elementary squares, the maximum, average and minimum heights of the territory, the depth of local erosion bases, the average surface steepness, the intensity and nature of construction, and the features of vegetation were determined. Results. The territory of Lviv is characterized by a variety of relief conditions and related nature of surface use. On the basis of morphometric maps of maximum and minimum heights, vertical dismemberment and steepness of the surface, we can distinguish the flat peripheral part of the city and the middle band of elevations. The asymmetry of the territory of Lviv in relation to the watershed causes various potential dangers of erosion processes and related unproductive moisture losses. The intensity of construction increases from the periphery to the center, with the exception of modern multi-storey buildings of the Sykhiv massif and industrial areas in the western part of the city. Low specific weight of waterproof coatings is characterized by the eastern and northern parts of the city with a complex relief. The largest amount of greenery is concentrated in the eastern part of the city (Vysokyi Zamok Park, Shevchenkivskyi grove, Pohulyanka Forest Park, Lychakiv), where surfaces with maximum relative heights predominate. The great variety of the underlying surface on the territory of Lviv causes different conditions for the formation of surface runoff and associated unproductive moisture consumption. The high potential danger of erosion processes is primarily noted by the structural-denudation level of Roztochia, which is characterized by the highest values of surface steepness. Complex relief conditions, intensive anthropogenic impact determine the need for anti-erosion organization of the city and measures aimed at optimizing hydrological processes. Scientific novelty. One of the criteria that characterizes the degree of landscape transformation within the city is the intensity of construction, which is determined by the proportion of watertight areas in the overall balance of the territory. Peculiarities of spatial arrangement of elementary plots with different intensity of construction in combination with relief parameters and land use scheme are determined. Practical significance. Knowing the parameters of the terrain and the peculiarities of land use, it is possible to determine the potential intensity of erosion processes in the territory of Lviv, to assess the level of anthropogenic changes in the hydrological regime.

scholarly journals Conformity of pre-design studies on Lakhta-Center influence on the Saint Petersburg historical panorama and scenery spots

2020 ◽  
Vol 164 ◽  
pp. 05013
Author(s):  
Oleg Fedorov ◽  
Yury Lobanov
Keyword(s):  
Visual Analysis ◽  
Reference Model ◽  
Design Studies ◽  
International Council ◽  
Saint Petersburg ◽  
High Rise ◽  
Regional Committee ◽  
The City ◽  
Main City

The present study is aimed to identify influence of Lakhta-Center visibility by means of photofixation results' analysis and their comparison with the results of the landscape and visual analysis performed by the Saint Petersburg Regional Committee of the International Council on Monuments and Sights (ICOMOS), report on research of Lakhta-Center high-rise dominant visibility influence on Saint Petersburg protected panoramas (2012), and assessment analysis of requested deviations' influence on formation of compositional and environmental characteristics of the urban environment based on a 3D reference model of the Saint Petersburg territory (2011). The study is supported by actual photos of cityscapes, thus, allowing assessing the situation reasonably and rationally. The performed work resulted in graphic materials, including photos and maps with reviews and classification of Lakhta-Center visibility upon perception of the main city panoramas from the tourist route combining the most popular sights of the city.

scholarly journals LAND USE CLASSIFICATION FROM VHR AERIAL IMAGES USING INVARIANT COLOUR COMPONENTS AND TEXTURE

2016 ◽  
Vol XLI-B7 ◽  
pp. 311-317
Author(s):  
A. Movia ◽  
A. Beinat ◽  
T. Sandri
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Landscape Patterns ◽  
Aerial Images ◽  
Land Use Classification ◽  
Essential Information ◽  
Structuring Element ◽  
Very High ◽  
Effective Contribution

Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. <br><br> In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. <br><br> To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. <br><br> The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

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