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

<p>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.</p><p>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.</p><p>Results for the Urseren Valley (Canton Uri, Switzerland) show an increase in total area affected by soil degradation of 156 ±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.</p><p> </p><p><strong>References</strong></p><p>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.</p>

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 The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Landscape dynamics in the northeast part of Andhikhola watershed, Middle hills of Nepal

2019 ◽  
Vol 12 ◽  
pp. 41-56
Author(s):  
Chhabi Lal Chidi ◽  
Wolfgang Sulzer ◽  
Pushkar Kumar Pradhan
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
High Resolution ◽  
Land Use Change ◽  
Land Abandonment ◽  
Cultivated Land ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Hill Slope

 Depopulation and increasing greenery due to agriculture land abandonment is general scenario in many highlands of Nepal in recent decades. High resolution remote sensing image is used in land use change analysis. Recently, object based image analysis technique has helped to improve the land use classification accuracies using object based image analysis. Thus, this study was carried out with high resolution image data sources and innovative technique of land use classification in the northeast part of Andhikhola watershed, in the Middle Hill of Nepal. Increasing greenery due to agriculture land abandonment in the hill slope is the major land use change. Secondly, increasing built-up area in lowland along the highway is another. Decreasing hill farmers is the major drivers of converting cultivated land into vegetated area and increasing built-up area is due to urbanization and shift of rural people from hill slope to lowland and accessible area. Converting cultivated land into forest, shrubs and grassland is at marginal land and remote areas which is mostly controlled by altitude, slope gradient and slope aspect. Additionally, land suitability and accessibility are also other important controlling factors.

scholarly journals MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020

2021 ◽  
Vol 13 (1) ◽  
pp. 109
Author(s):  
Latifa Melani Putri ◽  
Pramaditya Wicaksono
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Sand Dune ◽  
Sand Dunes ◽  
Land Use Changes ◽  
Dry Land ◽  
Core Zone ◽  
Object Based Image Analysis ◽  
The Core ◽  
Object Based

Indonesia has many types of unique and rare landforms, one of which is sand dunes, which is located in Parangtritis. Sand dune has the main function as a conservation area, natural wall for the tsunami disaster, water catchment area, and habitat for sand dune flora and fauna. However, the existence of sand dunes is currently threatened with extinction due to the decrease in their area, which is caused by changes in land use. Every year, the land use in the Parangtritis sand dune changes. Therefore, it is important to map land use changes to determine the changes that occur in the sand dune core zone. This study aims to map land use change in the core zone of sand dunes using small format aerial images and the OBIA (Object-Based Image Analysis) method. Land use in the study area is classified into nine classes, namely sand dunes, dry land forest, shrubs, coastal shoals, open field, built-up area and settlements, dry land agricultural fields, roads, and fishponds. The results showed that there were changes in all land use classes. Based on the accuracy assessment, the overall accuracy for 2020 was 68.95%, while the classification results for 2015 were 61.81%.Keywords: land use changes, OBIA, Small Format Aerial PhotographyIndonesia memiliki banyak jenis bentuklahan yang unik dan langka, salah satunya adalah gumuk pasir yang terletak di wilayah Parangtritis, Daerah Istimewa Yogyakarta. Gumuk pasir memiliki fungsi utama sebagai kawasan konservasi, tembok alami bencana tsunami, kawasan resapan air, serta habitat untuk flora fauna gumuk pasir. Namun, keberadaan gumuk pasir saat ini terancam punah oleh adanya penurunan luasannya, yang disebabkan oleh perubahan penggunaan lahan. Setiap tahun, penggunaan lahan di gumuk pasir Parangtritis mengalami perubahan, yang akhirnya menyebabkan luasan gumuk pasir selalu berkurang setiap tahunnya. Oleh karena itu, pemetaan perubahan penggunaan lahan penting untuk dilakukan untuk mengetahui perubahan yang terjadi di zona inti gumuk pasir. Penelitian ini bertujuan untuk memetakan perubahan penggunaan lahan di zona inti gumuk pasir menggunakan foto udara format kecil dan metode OBIA (Object-Based Image Analysis). Penggunaan lahan di wilayah kajian diklasifikasikan menjadi sembilan kelas yaitu gumuk pasir, hutan lahan kering, semak belukar, beting pantai, lahan terbuka, lahan terbangun dan permukiman, ladang, jalan dan tambak. Hasil penelitian menunjukkan adanya perubahan pada semua kelas penggunaan lahan. Berdasarkan uji akurasi, akurasi keseluruhan (overall accuracy) hasil klasifikasi penggunaan lahan tahun 2020 sebesar 68,95%, sedangkan hasil klasifikasi penggunaan lahan tahun 2015 sebesar 61,81%.Kata kunci: Perubahan Penggunaan Lahan, OBIA, Foto Udara Format Kecil 

scholarly journals UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

2016 ◽  
Vol 10 (3-4) ◽  
pp. 169-178 ◽  
Author(s):  
László Bertalan ◽  
Zoltán Túri ◽  
Gergely Szabó
Keyword(s):  
Image Analysis ◽  
Manual Control ◽  
Surface Model ◽  
Digital Surface Model ◽  
Rill Erosion ◽  
Segmentation Method ◽  
Object Based Image Analysis ◽  
Erosion Processes ◽  
Object Based ◽  
Multiresolution Segmentation

A remarkable badland valley is situated near Kazár, NE-Hungary, where rhyolite tuff outcrops as greyishwhite cliffs and white barren patches. The landform is shaped by gully and rill erosion processes. Weperformed a preliminary state UAS survey and created a digital surface model and ortophotograph. Theflight was operated with manual control in order to perform a more optimal coverage of the aerial images.The overhanging forests induced overexposed photographs due to the higher contrast with the baretuff surface. The multiresolution segmentation method allowed us to classify the ortophotograph andseparate the tuff surface and the vegetation. The applied methods and final datasets in combination withthe subsequent surveys will be used for detecting the recent erosional processes of the Kazár badland

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