Vegetation Dynamics of Sub-Mediterranean Low-Mountain Landscapes under Climate Change (on the Example of Southeastern Crimea)

Forests, Journal Year: 2023, Volume and Issue: 14(10), P. 1969 - 1969

Published: Sept. 28, 2023

In the context of a changing environment, understanding interaction between vegetation and climate is crucial for assessing, predicting, adapting to future changes in different types. Vegetation exhibits high sensitivity external environmental factors, making this particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, investigate dynamics based on remote sensing data climatic variables, including annual air temperature, precipitation, solar radiation. The research methodology encompasses collection, processing, analysis, incorporating multispectral imagery multilayered maps various parameters. calculation normalized difference index serves evaluate cover, identify areas experiencing variations green biomass, establish strategies development During period from 2001 2022, average value Southeastern Crimea region amounted 0.443. highest values were recorded year 2006, reaching magnitude 0.469. Conversely, lowest observed years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend evolution NDVI 2022 apparent, thus implying notable augmentation vegetative biomass. However, adversarial trends manifest discrete locales adjacent cities Sudak Feodosia, along with coastal stretches Black Sea. Correlation employed relationships indicators. findings contribute our vulnerability types ecosystems region. obtained provide valuable insights sustainable resource management change adaptation

Language: Английский

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Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Agronomy, Journal Year: 2023, Volume and Issue: 13(10), P. 2608 - 2608

Published: Oct. 13, 2023

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used enhanced spatial temporal adaptive reflectance fusion model (ESTARFM), flexible spatiotemporal (FSADF), non-local filter (STNLFFM) calculate normalized differential vegetation index (NDVI) of summer maize planting area in Southeast Loess Plateau Sentinel-2 MODIS data. The resolution was 10 m 1 d, respectively. Then, we evaluated adaptability ESTARFM, FSADF, STNLFFM models field from perspectives textural characteristics data, NDVI growing curves, estimation accuracy through qualitative visual discrimination quantitative statistical analysis. results showed that ESTARFM–NDVI, FSDAF–NDVI, STNLFFM–NDVI could precisely represent variation tendency local mutation information during growth period maize, compared with MODIS–NDVI. correlation between Sentinel-2–NDVI favorable, large coefficients a small root mean square error (RMSE). In curve simulation introduced overall weights filtering, which significantly improve poor at seedling maturity stages caused by long gap high-resolution ESTARFM. Moreover, as follows (from high low): (R = 0.742, absolute percentage (MAPE) 6.22%), ESTARFM 0.703, MAPE 6.80%), FSDAF 0.644, 10.52%). FADSF affected heterogeneity semi-humid areas, low. semi-arid had advantages less input faster response.

Language: Английский

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Influência de eventos climáticos extremos na ocorrência de queimadas e no poder de regeneração vegetal

Revista Brasileira de Geografia Física, Journal Year: 2024, Volume and Issue: 17(2), P. 1098 - 1113

Published: March 14, 2024

O fogo é uma ferramenta milenar utilizada pelo homem no meio agrícola. Contudo, essa prática pode causar infortúnios pela destruição da fauna e flora local, principalmente se ocorrido em regiões de clima semiárido baixa pluviosidade. objetivo deste artigo foi verificar as dinâmicas das cicatrizes queimadas, baseado nas técnicas geoprocessamento sensoriamento remoto; além influência fenômenos climáticos extremos temperatura do ar para queimadas mesorregiões Sertão São Francisco Pernambucano. Utilizou-se os dados Instituto Nacional Meteorologia (INMET) a análise climática definição dos meses mais secos ano. Aplicaram-se Sistema Monitoramento Agrometeorológico (AGRITEMPO) obtenção máxima diária. Usou-se National Weather Service (NOAA) verificação El Niño La Niña. As imagens sensor Moderate Resolution Imaging Spectrorradiometer (MODIS) foram utilizadas caracterização também o acompanhamento Normalized Difference Vegetation Index (NDVI). Os ano são agosto novembro, suscetíveis às que apenas não influencia diretamente nessas situações. acarreta um aumento nesses episódios mês na Niña, essas ocorrências evidentes nos outubro. Dessa forma, artifícios mencionados, verificou-se interferência ocorrência sua partir satélites mineração dados.

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Crop yield estimation uncertainties at the regional scale for Saxony, Germany

Agronomy Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Abstract In times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early possible in the event a drought is crucial for decision‐makers politics, government, business. The aim this study was to provide precise predictions at regions with minimum amount weather data. Random forest models were used purpose. Although more than 290,000 datasets available analysis, all tended be heavily overfitting, which can explained by strong fragmentation input data crop, region, prediction time. reacted very differently unknown datasets. It found that regionally trained achieved lower (≥10%) relative root mean square errors (RRMSEs) supra‐regionally models. Rapeseed barley good predictions. Wheat had potential, too. Corn, potatoes, sugar beet often too high RRMSEs. results showed targeted model selection each region extension training time series could enable regional rapeseed cereals future.

Language: Английский

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Determination of Land Degradation in Romania at a Local Scale Using Advanced Analytical Techniques

Land Degradation and Development, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

ABSTRACT Land degradation (LD) threatens the food security and general welfare of many people globally. Degradation Neutrality (LDN) is a pivotal goal within global sustainability agenda, particularly under Sustainable Development Goal (SDG) 15.3.1, which measures proportion land that degraded over total area. Romania, with its diverse landscapes significant agricultural sector, faces notable challenges in this Assessing SDG indicator serves to identify vulnerable areas assists policymakers defining necessary strategic instruments actions. Most previous studies have assessed using low‐ or moderate‐resolution data without clearly identifying triggering factors, thereby limiting ability detect changes at sub‐national level analyze detail influence these factors on environment. This research first examine relationship between 20 potential cities municipalities, aiming key drivers provide insights for urban planning policy‐making. The study integrates series high medium‐resolution statistical, geospatial, EO sources effectively assess 103 municipalities 216 cities. Using multiple regression analysis (MRA) Random Forest Method (RF), analyzed various predictive influencing LD, revealing cover, temperature, multi‐annual average precipitation, along atmospheric pollutants (CO, SO 2 ) Surface Temperature, significantly contribute large These accounted approximately 83.3% 96.7% (RF) variation indicator, underscoring their roles trends. According results, 2.3% Romania's area was during 2015–2022 period, 58% remained stable, 38% showed improvements, remaining 1.7% represented water bodies. By communes counties, supports implementation appropriate combat crucial step efforts achieve LDN by 2030.

Language: Английский

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High Spatial Resolution for Crop Yield Prediction in Large Farming Systems: A Necessity or Additional Overhead

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Journal Year: 2023, Volume and Issue: unknown, P. 3534 - 3537

Published: July 16, 2023

The availability of open-access satellite data and advancements in machine learning techniques has exhibited significant potential crop yield prediction. In the context large farming systems county-level predictions, it is customary to rely on coarse-resolution images. However, these images often lack sufficient textural detail accurately summarise spatial information. This research aims evaluate advantages enhanced resolution by conducting a comparative analysis between coarse-resolution, high-temporal-frequency MODIS relatively high-resolution, low-temporal-frequency Landsat for predicting corn USA. We benchmark this comparison against several models versus non-spatial input context. Our results suggest that, use high-spatial prediction not beneficial explored are unable generalize well drought-struck years.

Language: Английский

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