Search Results (4,557)

Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and post-fire conditions based on a wildfire event in 2019 in southern Ecuador. The Revised Universal Soil Loss Equation (RUSLE) was used in combination with earth observation data to detect the fire-induced change in erosion behavior by adapting the cover management factor (C-factor). To understand the spatial accuracy of the predicted erosion-prone areas, high-resolution data from an Unmanned Aerial Vehicle (UAV) served for comparison and visual interpretation at the sub-basin level. As a result, the mean erosion at the basin was estimated to be 4.08 t ha⁻¹ yr⁻¹ in pre-fire conditions and 4.06 t ha⁻¹ yr⁻¹ in post-fire conditions. The decrease of 0.44% is due to the high autonomous vegetation recovery capacity of grassland in the first post-fire year. Extreme values increased by a factor of 4 in post-fire conditions, indicating the importance of post-fire erosion measures such as SWBE in vulnerable areas. The correct spatial location of highly erosive areas detected by the RUSLE was successfully verified by the UAV data. This confirms the effectivity of combining the RUSLE with very-high-resolution data in identifying areas of high erosion, suggesting potential scalability to other fire-prone regions. Full article

Xinjiang is an important forest and fruit production area in China, and Cytospora canker, caused by the genus Cytospora Ehrenb., has caused serious losses to forestry production in Xinjiang. In this study, we constructed ensemble models based on Biomod2 to assess the potential geographical distribution of Cytospora chrysosperma, C. nivea, and C. mali in Xinjiang, China and their changes under different climate change scenarios, using species occurrence data and four types of environmental variables: bioclimatic, topographic, NDVI, and soil. The model performance assessment metrics (AUC and TSS) indicated that the ensemble models are highly reliable. The results showed that NDVI had the most important effect on the distribution of all three species, but there were differences in the response patterns, and bioclimatic factors such as temperature and precipitation also significantly affected the distribution of the three species. C. chrysosperma showed the broadest ecological adaptation and the greatest potential for expansion. C. nivea and C. mali also showed expansion trends, but to a lesser extent. The overlapping geographical distribution areas of the three species increased over time and with an intensification of the climate scenarios, especially under the high-emission SSP585 scenario. The centroids of the geographical distribution for all three species generally shifted towards higher latitude regions in the northeast, reflecting their response to climate warming. C. chrysosperma may become a more prevalent forest health threat in the future, and an increase in the overlapping geographical distribution areas of the three species may lead to an increased risk of multiple infections. These findings provide an important basis for understanding and predicting the distribution and spread of the genus Cytospora in Xinjiang and are important for the development of effective forest disease prevention and control strategies. Full article

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas. Full article

Arid oasis ecosystems are susceptible and fragile ecosystems on Earth. Studying the interaction between deserts, oases, and impervious surfaces is an essential breakthrough for the harmonious and sustainable development of people and land in drylands. Based on gridded data such as land use and NDVI, this article analyzes the interaction characteristics between oases, deserts, and impervious surfaces in northern Egypt and examines their dynamics using modeling and geographic information mapping methods. The results show the following: In terms of the interaction between deserts and oases, the primary manifestation was the expansion of oases and the reduction of deserts. During the study period, the oases in the Nile Delta and Fayoum District increased significantly, with the area of oases in 2020 being 1.19 times the area in 2000, which shows a clear trend of advance of people and retreat of sand. The interaction between oases and impervious surfaces was mainly observed in the form of the spread of impervious surfaces on arable land into oases. During the study period, the area of impervious surfaces increased 2.32 times. The impervious surface expanded over 1903.70 km² of arable land, accounting for 66.67% of the expanded area. The central phenomenon between the impervious surface and the desert was the encroachment of the covered area of the impervious surface into the desert, especially around the city of Cairo. Population growth and urbanization are the two central drivers between northern Egypt’s oases, deserts, and impervious surfaces. The need for increased food production due to population growth has forced oases to move deeper into the desert, and occupation of arable land due to urbanization has led to increasing pressure on arable land, creating a pressure-conducting dynamic mechanism. Finally, countermeasures for sustainable regional development are suggested. Full article

Sahelian countries including Burkina Faso face multiple challenges related to climatic conditions. Setting up effective disaster management plans is essential for protecting livelihoods and promoting sustainable development. Soil and water conservation measures (SWCMs) are emerging as key components of such plans, particularly in Burkina Faso. However, there is an insufficiency of studies exploring their potential as green infrastructures in the Sahelian context and this research aims to contribute to filling this gap. We used national data, remote sensing, and GIS tools to assess SWCM adoption and the potential for climate resilience. Stone ribbons emerged as the most widely adopted SWCM, covering 2322.4 km² especially in the northern regions, while filtering dikes were the least widely adopted, at 126.4 km². Twenty years of NDVI analysis showed a notable vegetation increase in Yatenga (0.075), Oudalan (0.073), and provinces with a high prevalence of SWCM practices. There was also an apparent increase in SWCM percentages from 60% of land degradation. Stone ribbons could have led to a runoff reduction of 13.4% in Bam province, highlighting their effectiveness in climate resilience and flood risk mitigation. Overall, encouraging the adoption of SWCMs offers a sustainable approach to mitigating climate-related hazards and promoting resilience in Sahelian countries such as Burkina Faso. Full article

Agriculture plays a crucial role as a carbon sink in the atmosphere, contributing to a climate-neutral economy, which requires a comprehensive understanding of Earth’s complex biogeochemical processes. This study aims to quantify, for the first time, Gross Primary Productivity (GPP) and ecosystem water use efficiency (eWUE) in almond orchards during their vegetative phase. The study was conducted over six growing seasons (2017–2022) across two drip-irrigated commercial almond groves located in Albacete, SE Spain. Eddy covariance flux tower systems were used to measure Net Ecosystem Exchange (NEE) and evapotranspiration (ET), which were then used to calculate GPP and eWUE. A novel approach was developed to estimate eWUE by integrating the Normalized Difference Vegetation Index (NDVI), reference ET, and air temperature. The results show similar almond orchard carbon-fixing capacity rates to those of other natural and agro-ecosystems. Seasonal and interannual variability in GPP and eWUE were observed. The NDVI-ET combination proved to be effective for GPP estimations (regression coefficient of 0.78). Maximum carbon-fixing values were observed at ET values of around 4–5 mm/d. In addition, a novel method was developed to estimate eWUE from NDVI, reference ET and air temperature (RMSE of 0.38 g·C/kg·H₂O). This study highlights the carbon capture potential of almond orchards during their vegetative phase and introduces a novel approach for eWUE monitoring, with the intention of underscoring their significance in a climate change context and to encourage further research. Full article

Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines the spatial mismatch and representativeness of SWC products’ footprints based on different factors within the following areas: the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information. The results reveal that RS-derived SWC, particularly at T = 2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R = 0.6) and a mean unbiased Root–Mean–Square Difference (ubRMSD) of 14.90%SR. However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R = 0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC and silt loam soil type correlate to decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS pixel variability of the different factors can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison. Full article

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation’s environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes. Full article

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. Full article

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems. Full article

The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio. Full article

In arid and semiarid regions, vegetation provides essential ecosystem services, especially retarding the desertification process. Vegetation assessment through remote sensing data is crucial in understanding ecosystem responses to climatic factors and large-scale human activities. This study analyzed vegetation cover changes in the Ulan Buh Desert from 1989 to 2019, focusing on the impacts of human activities and key meteorological factors. The results showed that both climatic and human activities contributed to an increasing trend in vegetation cover (normalized difference vegetation index (NDVI)) over the 30-year period. Temperature and precipitation significantly affected the NDVI in the desert, with temperature having a more substantial influence. The combined impact of average temperature and precipitation on the NDVI was notable. Human activities and meteorological factors caused the vegetation restoration area in the desert to be approximately 35% from 1989 to 2019. Human activities were the primary influencers, responsible for about 60% of vegetation restoration across the study area. Especially from 2004 to 2019, the conversion to farmland driven by human activities dominated the region’s NDVI increase. The research underscores the importance of considering both climatic and human factors in understanding and managing ecosystem dynamics in arid areas like the Ulan Buh Desert. By integrating these factors, policymakers and land managers can develop more effective strategies for sustainable ecosystem management and combating desertification. Full article

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts. Full article

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents. Full article