Spatiotemporal Analysis and War Impact Assessment of Agricultural Land in Ukraine Using RS and GIS Technology

Ma, Yue; Lyu, Dongmei; Sun, Kaixuan; Li, Sijia; Zhu, Bingxue; Zhao, Ruxing; Zheng, Miao; Song, Kaishan

doi:10.3390/land11101810

Cited by 33 publications

(20 citation statements)

References 72 publications

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“…Through the above analyses, we demonstrate the superiority of DAE in the recognition of NLOS in urban forest environments. DAE can effectively reduce the data dimension and weaken the noise of the data, so we can try to apply it to analyze the current hotspots of high-resolution remote sensing modern change detection [ 44 ] and the spatial–temporal distribution study of farmland [ 45 ]. In this article, we have analyzed and compared the proposed CDAENet method from various perspectives, such as convergence, classification performance, anti-interference ability, and normalization performance.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

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The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model’s robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

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Section: Discussionmentioning

confidence: 99%

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

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“…The study emphasized the importance of considering the specific characteristics of different ecosystems and their responses to military-technogenic loads. Another studies proposed a methodological approach for assessing the impact includes both qualitative and quantitative indicators [5,14]. The study [5] highlighted the importance of analyzing changes in vegetation cover at different spatial scales to better understand the impact of military activities on natural ecosystems.…”

Section: Related Workmentioning

confidence: 99%

“…Another studies proposed a methodological approach for assessing the impact includes both qualitative and quantitative indicators [5,14]. The study [5] highlighted the importance of analyzing changes in vegetation cover at different spatial scales to better understand the impact of military activities on natural ecosystems. The study [15] shows a tool to monitor environmental dynamics and plan military training activities, but its limitation lies in that the obtained values of the indicator vary and are subjective to the experts' knowledge and experience.…”

Section: Related Workmentioning

confidence: 99%

“…Another study [3] evaluated the impact of military activities on the state of natural ecosystems in Ukraine and proposed a system for monitoring the environmental situation in the areas affected by military operations. Furthermore, studies [4,5] examined the impact of military activities on the environment in Ukraine and proposed a set of measures to reduce the negative impact of military activities on the environment. These studies suggest that developing appropriate methods for assessing the impact of hostilities on the environment is crucial for mitigating the negative effects of military activities on natural ecosystems.…”

Section: Introductionmentioning

confidence: 99%

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Development of a scientific and methodological approach to assessing losses from warfare in natural ecosystems on the territory of Ukraine

Chumachenko,

Dudkin,

Honcharenko

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The assessment of the impact of hostilities on the environment remains an important issue for predicting changes resulting from military-technogenic activities and assessing the losses incurred by ecosystems in Ukraine. This article proposes an integrated approach for predicting the possible level of hostilities’ impact by utilizing aggregated environmental information on ecosystem composition, indicators of military-technogenic load, organization of trophic networks in relevant biogeographic zones, and biodiversity composition. The concept of environmental safety of hostilities is introduced as a projection into the military technosphere of the ecosystem sustainability concept, where the target function is the conservation of the natural biota of operational zones and areas of hostilities. The article presents a block diagram of the procedure for assessing the state of ecosystems in war zones and proposes a classification of levels of military-technogenic disturbance of natural ecosystems based on the state of edifier sinusia.

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“…Cropland abandonment due to conflict was apparent in countries such as Bosnia and Herzegovina (Witmer, 2008;Boudinaud and Orenstein, 2021), Sudan (Olsen et al, 2021) and Ukraine (Ma et al, 2022) while conflict promoted expansion of cropland in relative safer zones and hindered cultivation in other areas in Syria (Li et al, 2022). Although the context-specific variation in the relationship between conflict and cropland abandonment is evident, their individual respective impact on local food supply (Witmer, 2008;Skakun et al, 2019;Yin et al, 2019) and the repercussion of conflict induced cropland abandonment in the affected regions is poorly understood (Olsen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Eyes in the sky on Tigray - Monitoring the impact of armed conflict on cultivated land using satellite imagery in Ethiopia

Weldegebriel¹,

Negash

Nyssen

et al. 2023

Preprint

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The war in Tigray, Ethiopia has displaced millions of people and created a humanitarian crisis. However, the impacts of the conflict on cultivated land area and local food production remains poorly quantified, hindering aid efforts. We used Sentinel-2 satellite imagery and developed multiple cultivation detection criteria based on Normalized Difference Vegetation Index time series validated using field observations from 2021 and 2022. Significant net losses of well cultivated land in highland croplands (elevation ≥ 1200 m) were observed between 2019/20 (pre-war) and 2021 (in-war), with greater losses in areas with higher density of conflict incidents. Sub-regions with high estimated loss of well cultivated land also exhibited high numbers of internally displaced people (IDP), consistent with a causal effect of the conflict on land abandonment. Our study estimated that the kilocalories lost due to abandonment of croplands (excluding Western Tigray zone) could have supported about 1.2 times the recorded IDPs in the region.

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Spatiotemporal Analysis and War Impact Assessment of Agricultural Land in Ukraine Using RS and GIS Technology

Cited by 33 publications

References 72 publications

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Development of a scientific and methodological approach to assessing losses from warfare in natural ecosystems on the territory of Ukraine

Eyes in the sky on Tigray - Monitoring the impact of armed conflict on cultivated land using satellite imagery in Ethiopia

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