A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin

Russian-Ukrainian war impacts on the environment. Evidence from the field on soil properties and remote sensing

The Russian-Ukrainian war is having a dramatic impact on the environment. The effects are still unknown. However, it is expected that the effects will be substantial. Since the conflict is ongoing, it is challenging to have ground-accurate data that could show an idea of the extension of the impact. Remote sensing can support a preliminary analysis in areas without safety to conduct fieldwork. This work aims to assess the impacts of the Russian-Ukrainian war on the environment using field and remote sensing sources. This is the first work that published fieldwork data from this conflict. Different soil properties were studied (e.g., texture and heavy metals) in different places (Novy Korotych and Mala Rohan). Remote sensing (e.g., Normalised Difference Vegetation Index) at different spatial scales (Eastern Ukraine and case study in the Kharkiv region) were assessed between 2021 and 2022. The results showed that the finer sediments increased in bombed areas compared with not bombed ones. Also, there was an increase in the content of heavy metals (e.g., Manganese, Iron, Cobalt, Copper, Cadmium, Chromium, Lead and Nickel) in soils in the shelled areas, compared to the non-shelled. This was mainly observed in the Novy Korotych site. Remote sensing analysis revealed that between 2021 and 2022 at a large scale (Eastern and southeastern Ukraine), vegetation greenness decreased in the areas where the combats are more intense (Luhansk and Donetsk) and increased in the southern area (Zaporizhia and Kherson), likely due to agriculture abandonment. On a small scale (case study in Kharkiv region), the vegetation greenness was affected due to the bombing. Although our results are preliminary, it is important to highlight that shelling is increasing soil pollution and contributing to vegetation greenness reduction where the fighting is intense. The actual impacts of the war still need to be understood entirely.

Monitoring Land Degradation Dynamics to Support Landscape Restoration Actions in Remote Areas of the Mediterranean Basin (Murcia Region, Spain)

This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model.