Archive d’étiquettes pour : Remote sensing

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1211 | | Remote sensing of changing forest ecosystems: methods for risk assessment and adaptive management | Mariano Moreno De Las Heras (1); Antonio J. Molina (2); Guillermo Palacios (3); Eduardo A. Garcia-Braga (1); Antonio Peñalver-Alcalá (1); Xavier Úbeda (1)

Forest ecosystems cover about 35% of the European surface, where they play a key role for the maintenance of biodiversity and the provision of many other ecosystem services, including water cycle regulation, erosion control, habitat creation, carbon storage and sequestration, the production of market goods and both cultural and recreative services. Land abandonment in rural areas and extensive afforestation programs by national forest services have contributed to forest expansion and densification since the 1950’s in many European countries, particularly in the Mediterranean region. However, the increasing occurrence of climate extremes, such as droughts and heat waves, is rapidly changing the organization and function of these forest landscapes, compromising the provision of their ecological services under the influence of climate change. Common effects of climate extremes on forest ecosystems include the reduction of forest productivity, increased canopy defoliation, tree mortality, insect outbreaks and extensive wildfires. For example, the 2022 summer heat wave that recently affected western Europe, combined with prolonged drought conditions, resulted in over 8,500 km2 surface burnt by wildfires, with an important impact on forests ecosystems and protected Natura 2000 areas.
The development of rapid forest surveying methods and large-scale vulnerability assessment tools constitutes an essential challenge for the development of adaptive forest management in the present context of change. Currently, different types of remote sensing data (e.g., optical, multispectral, radar, LiDAR) from satellite, aerial and UAV platforms can be used to evaluate forest structure and dynamics. In fact, the increased availability of remote sensing data with a broad variety of temporal, spatial and spectral resolution can largely facilitate the detection and evaluation of forest ecosystems for environmental monitoring, modeling and description. This session will explore remote sensing methods for climate change risk and forest vulnerability evaluation, including mapping and analysis of, among others, the effects of drought, forest dieback and wildfires as well as forest structure and forest resilience assessment. We also welcome contributions dealing with the integration of remote sensing data with field observations and other spatial geo-information systems for the study of forest dynamics, forest restoration and adaptive forest management. We plan to organize the session in English, as a regular session with a series of oral talks and also some contributions as poster presentations for additional discussion

Mariano Moreno De Las Heras (1); Antonio J. Molina (2); Guillermo Palacios (3); Eduardo A. Garcia-Braga (1); Antonio Peñalver-Alcalá (1); Xavier Úbeda (1)
(1) Department of Geography, University of Barcelona, Barcelona, Spain, (2) Department of Hydraulic Engineering and Environment, Polytechnic University of Valencia, Valencia, Spain, (3) Department of Forest Engineering, University of Cordoba, Spain

 
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1191 | 444 | Remote Sensing Techniques for Monitoring Land degradation due to Soil Erosion: A Case Study in the Nitra District of Slovakia | Tomáš Rusňák, Andrej Halabuk

Land degradation due to soil erosion is one of the major environmental concerns worldwide, affecting the sustainability of agricultural productivity and the health of ecosystems. Data regarding the actual scale of soil erosion is important information for conservation policies. Nevertheless, this kind of data is often unavailable on a large scale. Remote sensing techniques have become a powerful tool to monitor and assess land degradation. In this study, we used Sentinel 2 satellite imagery to detect soil erosion in the Nitra district in the western part of Slovakia. The Nitra district has an agricultural character and is significantly affected by erosion. We used a combination of Sentinel data and soil subtypes for classification. The Random Forest classification has been performed. We classified two classes – eroded soil and non-eroded soil. The results showed that the proposed method is effective in detecting soil erosion in the study area with an overall accuracy of 94.04%. Our findings show the potential of using remote sensing data on actual soil degradation by erosion. Remote sensing can be used as an efficient tool for monitoring and assessing soil erosion, aiding in the development of effective land management strategies. However, there are some limitations related to the variability of soil cover, the effect of clouds, shadows, vegetation, or residues after agricultural crops, and the spectral separability of each class.

Tomáš Rusňák, Andrej Halabuk
Institute of Landscape Ecology v. v. i., Slovak Academy of Sciences

 
ID Abstract: 444

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1191 | 86 | spatiotemporal land cover change and estimation of land degradation factors based on vegetation structural characteristics in the semi- arid zone of algeria | ZEGRAR Ahmed, BENTEKHICI Nadjla, BENSHILA Naima.

Desertification is one of the most important problems due to global climate change. Many factors contribute to the degradation of the environment and the Algerian steppe. The first is related to human activities, such as land use change. Other factors include natural degradation due to changes in temperature, humidity and wind. Analyzing land use change helps decision makers to ensure sustainable development and understand the dynamics of our changing environment. In recent years, the study area, Nâama in Algeria, has undergone many changes due to rapid urban growth and poorly planned infrastructure development. In this study, the work is mainly based on classification criteria and degradation factors for the identification of physical and climatic parameters in the spatial analysis of change in order to determine the vulnerability of steppe formations and their impact on desertification. To this end, we have classified land use by combining several spectral indices in particular, which can be calculated from satellite data on each Land sat satellite spectral band, to construct multiband input data for a supervised classification approach based on a support vector (SVM), By applying this method to Land sat archival imagery in the period 15 years. Vegetation indices combined with classification are used to characterize forest and steppe formations. Using GIS, we integrated different factors, climatic parameters with rainfall and land surface temperature combined with land use, land cover and slope; we adopted proposals from expert judgments to determine weights in order to assign weights to each parameter. This allowed us to determine changes in land use and clarify the situation with regard to desertification. The results of this study provide information about the different components of the steppe, that could help highlight the extent of degradation and change, which affects the environment of the steppe, allowing an analysis of the desertification process in this

ZEGRAR Ahmed, BENTEKHICI Nadjla, BENSHILA Naima.
centre of spaces techniques

 
ID Abstract: 86

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1141 | 85 | forest regeneration assessment by integrated index and remote sensing in semi arid land in the north west of algeria | ZEGRAR Ahmed ; Bentkhici Nadjla ; Ghabi Mohamed

An important fire was occurring in July 2003 in the north west of Algeria, the fire was important and continued. This document presents a new method and combined data of study case, to see the fire severity and monitor vegetation recovery by using new satellite data (2009, 2014 and 2015) and new indices, based on analysis of topographic parameters using Aster DEM (30 m). _x000D_
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The ecological analysis of desertification requires knowledge of post fire regeneration in the mid-step, influenced by topographic conditions and climate parameters. The North West regions of Algeria are affected each summer by violent forest fires which last over several days and affects woodlands, natural forests and reforestation. Forest regeneration in this semi-arid land is conditioned by several factors, climatic, topographic, and linked to the timber species. Remote sensing and geographic information systems (GIS) offer to environmentalists and managers, an opportunity for the evaluation, the monitoring and analysis of the vegetation for mapping fires and observing post-fire regeneration. Usually NDVI is used, other derived index from radiometric data in remote sensing are widely used to monitor vegetation dynamics. The forest domain has benefited greatly from this approach. In this study we use remote sensing data from several dates (2005, 2007, 2009, 2014 and 2015) such as ALSAT data and Land sat, combined with the topographic parameters, and seems promising in the assessment of the spatial and temporal effects of regeneration after fires. The study area is located in the region of Sebdou in the north of Algeria, burned in 2003 allowed to take into account new factors to explain the regeneration and its spatial and temporal variation. The purpose of this study is to show the potential use of remote sensing data (ALSAT and Land sat images with a spatial resolution of 32 and 30 m), to quantify derived index such as the normalized difference vegetation index (NDVI) & Ratio vegetation index

ZEGRAR Ahmed ; Bentkhici Nadjla ; Ghabi Mohamed
CTS, Centre of Spaces Techniques

 
ID Abstract: 85

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