NDVI Data Research Articles

Cutting the Greenness Index into 12 Monthly Slices: How Intra-Annual NDVI Dynamics Help Decipher Drought Responses in Mixed Forest Tree Species

We studied the correspondence between historical series of tree-ring width (TRW) and the normalized difference vegetation index (NDVI, i.e., greenness index) values acquired monthly over an entire year by unmanned aerial vehicles. Dendrochronological techniques revealed differentiated responses between species and seasonality. Pinus engelmannii Carrière and Juniperus deppeana Steudel were affected by warm temperatures (TMAX) during the winter prior to growth and benefited from precipitation (PP) during the seasons prior to the spring period. The standardized precipitation–evapotranspiration index (SPEI) confirmed the high sensitivity of P. engelmannii to drought (r = 0.7 SPEI). Quercus grisea Liebm. presented a positive association with PP at the beginning and end of its growth season. Monthly NDVI data at the individual tree level in the three species (NDVI ~0.37–0.48) statistically confirmed the temporal differences. Q. grisea showed a drastic decrease during the dry season (NDVI = 0.1) that had no impact on drought sensitivity in the same period, according to the climate-TRW relationship. We conclude that a relationship is plausible between the crown greenness index and radial growth, although more extended temporal windows of the NDVI should be explored. Differences in susceptibility to drought found among the species would presumably have implications for the composition of these forests under drought scenarios.

Temporal—Spatial Changes in Vegetation Coverage under Climate Change and Human Activities: A Case Study of Central Yunnan Urban Agglomeration, China

Analyzing vegetation cover provides a basis for detecting ecological and environmental health in urban areas. We analyzed the temporal and spatial changes in vegetation cover using NDVI data from the central Yunnan urban agglomeration (CYUA). The dimidiate pixel model (DPM) and intensity analysis were used to study changes at three levels: time intervals, category, and transition. Analysis of time series data from 1990–2020 using the Theil–Sen Median with Mann–Kendal test identified the overall trends. Geodetector explored the relationship between natural and human factors in vegetation cover change. The CYUA’s vegetation cover gradually decreases from west to east and south to north, with middle–high and high vegetation occupying over 55%. During 1990–2020, significant improvement was observed in the east and north regions, with an increase of 22.49%. The anthropogenic core area showed severe degradation with nearly 1.56% coverage. The transformation intensity of middle vegetation coverage was dominant from 1990–2010 but was replaced by middle–high vegetation coverage from 2010–2020. Meanwhile, high vegetation coverage became the most prominent gains target, and the conversion of middle–high to high vegetation showed a system tendency to exceed the average in absolute number and relative intensity. Spatial and temporal differences in vegetation cover were mostly affected by land cover (q = 0.4726, p < 0.001), and the most influential topographic factor was the slope (q = 0.1491, p < 0.001). The impact of human activities has increased to 16%, double that of 2000. The CYUA’s vegetation cover improved more than it degraded, but required site-specific forest management due to human activities.

Monitoring of simulated clandestine graves of victims using UAVs, GPR, electrical tomography and conductivity over 4-8 years post-burial to aid forensic search investigators in Colombia, South America

In Colombia there are estimated to be over 121,000 missing people and victims of forced disappearances. Forensic investigators therefore need assistance in determining optimal detection techniques for buried victims, to give victims’ families closure and for the wider community to see that justice is being served. Previous research has created 12 controlled simulated clandestine graves of typical Colombian murder victim scenarios at 0.5 m – 1.2 m depths in savannah and rainforest sites in Colombia. The 0–3 years of geophysical monitoring results of were published, with this paper reporting on 4–8 years monitoring of both UAV drone results and geophysical data. The UAV results from the year 8 survey, published for first time from Colombia, showed that the simulated graves could still be located using NDVI and NIR multi-spectral data, but not using optical or other multi-spectral data. The 0–3 years of geophysical data found the simulated clandestine graves could be detected with electrical resistivity and GPR methods, with the 4–8 year surveys evidencing that they could still be detected using bulk ground conductivity surveys, GPR horizontal time slice datasets and 2D ERT profiles. Research implications suggest initial use of UAV remote sensing technology to pinpoint likely search areas, before subsequent ground reconnaissance, geophysical surveys and their interpretation, before intrusive investigation methods are employed for detecting missing and disappeared persons in Colombia.

Spatiotemporal Evolution and Quantitative Attribution Analysis of Vegetation NDVI in Greater Khingan Mountains Forest-Steppe Ecotone

It is of great significance to explore the dynamic variations in vegetation cover and to identify its driving factors for the restoration and sustainable development of the regional ecological environment. Based on MODIS NDVI data from 2000 to 2020 and contemporaneous meteorological, DEM, land use type, and other data, the spatiotemporal variation characteristics of vegetation in the Greater Khingan Mountains forest-steppe ecotone were deeply analyzed, and its future evolution pattern was predicted by using the methods of Sen+Mann-Kendall trend analysis and Hurst index. At the same time, the influence degree and mechanism of each detection factor and its interaction on vegetation spatial differentiation at the scale of the whole area and different physical geographic divisions were quantitatively revealed by introducing the GeoDetector model. The results showed that:① In terms of spatiotemporal variation, the spatiotemporal heterogeneity of NDVI in the Greater Khingan Mountains forest-steppe ecotone was obvious from 2000 to 2020. Temporally, NDVI fluctuated growth at a rate of 0.002 a-1 (P < 0.05) and underwent an upward mutation in 2011. Spatially, NDVI showed a distribution pattern of "increasing from southwest to northeast," and the NDVI grade transfer was mainly "medium vegetation cover→medium-high vegetation cover" during the 21 years, and the area of vegetation improvement was much larger than that of degradation. ② In terms of trend prediction, the future variation trend of NDVI in the Greater Khingan Mountains forest-steppe ecotone was mainly continuous improvement, accounting for 37%, but was mostly weakly sustained. ③ In terms of driving mechanism, the wind speed, evaporation, and relative humidity had the most significant influence on the spatial differentiation of NDVI over the whole area. The influence of natural factors has been decreasing over the past 21 years, whereas the influence of human factors has been increasing, and the main driving factors of NDVI spatial differentiation were quite different in different vegetation, climate, soil, and geomorphic zones. The synergistic effect between each factor at different spatial scales all showed two-factor or non-linear enhancement relationships, which was significantly enhanced compared with the single-factor effect. This study contributes to clarifying the causes of ecological fragility in the forest-steppe ecotone in the northern cold region and provides scientific support for formulating differentiated protection and management plans for vegetation resources under different environmental conditions.

Assessment of urban heat island using remote sensing and geospatial application: A case study in Sao Paulo city, Brazil, South America

Urban is a term that describes anything related to cities or densely inhabited places. It is often employed to define the traits, way of life, and problems unique to metropolitan locations. An extensive range of social, cultural, and economic activities may be found in urban regions, also noted for their dense population density and robust infrastructure. Tall buildings, congested streets, well-established public transit systems, and a diversity of residential, commercial, and industrial zones are just a few characteristics that set it different from other cities. The most significant urban environmental issue is urban heat island studies, and cities worldwide are experiencing terrible circumstances due to urbanization, industry, population strain on the transportation system, and climate change. Sao Paulo, Brazil, and South America have experienced rapid urbanization and growth over the past few decades. The city has experienced massive infrastructure expansion, modernization, and higher living standards due to a burgeoning population and a booming economy. Given the challenges this rapid expansion has also produced, such as traffic congestion, environmental problems, and resource demands, a sustainable urban planning approach is necessary for a balanced growth trajectory. The detection of the urban heat island in the Sao Paulo area using remote sensing and GIS techniques.Over four decades, from 1992 to 2022, many components have been researched, including NDVI, NDBI, LST, UTFVI, and UHI. According to NDVI statistics, healthy vegetation was generally (0.86, 0.96, 0.58, and 0.58) in 1992, 2002, 2012, and 2022—continuous declines from 1992 to 2022, as determined by NDVI data. Overall, the NDBI analysis shows little habitation in 1992 and a significant increase by 2022. LST research findings indicate that the land surface temperature was lower in 1992 and much higher in 2022. Because of UTFVI, fewer condition zones were occupied in 1992; over the years, this number grew. Overall data point to UHI covering a sizable area in 2022.

Long-term vegetation responses to climate depend on the distinctive roles of rewilding and traditional grazing systems

ContextThe abandonment of traditional practices has transformed agro-pastoral systems, leading to a more frequent occurrence of passive rewilding of Mediterranean landscapes. Reconstructing ecosystem responses to climate under different grazing conditions (i.e., wild, and domestic ungulates) is important to understand the future of these ecosystems.ObjectivesHere we study the different roles of domestic and wild herbivory in defining the climate-vegetation interaction. Specifically, we evaluated (1) the effect of climate on primary productivity at the landscape scale and (2) the long-term trends of vegetation biomass in response to passive rewilding or maintenance of traditional grazing systems.MethodsThis study was carried out in South-eastern Spain. We used satellite images to generate NDVI time series that proxy primary productivity and vegetation biomass. We combined the NDVI and climate data from two key landscapes: one with wild ungulates and another predominantly with domestic ungulates.ResultsWe detected a secondary succession process in areas with only wild ungulates. In domestic herbivory areas, vegetation biomass remained constant throughout time (30 years). In domestic herbivory areas temperature and seasonal precipitation affected primary productivity. In areas with only wild herbivory, primary productivity was mainly driven by annual precipitation, and it was less dependent on seasonal precipitation.ConclusionThese results highlight the distinctive roles of herbivores in defining Mediterranean landscapes' adaptability to climate, through passive rewilding or traditional livestock use. Maintaining both ecosystems can enhance landscape heterogeneity and ecological sustainability in a context of climatic changes.

Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia

Drought occurrence in a certain area can be monitored by remote sensing through cloud-based platform of Google Earth Engine (GEE). The objective this study was to analyze spatially and temporally distribution of drought in Bangkalan Regency between 2017 to 2022 with GEE. This study employed CHIRPS and satellite images of Landsat 8 at Level 2 covering Bangkalan area from 2017 to 2022. Masking and Cloud masking had been carried out before analyzed the satellite images. Data was processed using Java scrip API algorithm in GEE to obtain rainfall, LST, NDVI, NDWI and NDDI data. Result of rainfall analysis from CHIRPS data showed that dry months from 2017 to 2022 occurred from June to October. The value of LST was between 24.75 38.87°C. Drought events in the study area from 2017 to 2022 were dominated by severe and extreme drought. The severe drought covers the area of 83.17% (2017), 57.34% (2018), 67.13% (2019), 84% (2020), 80.93% (2021), and 89.89% (2022). Meanwhile, the extreme drought wraps the area of 14.05% (2017), 40.05% (2018), 30.17% (2019), 13.15% (2020), 16.27% (2021), and 7.03% (2022). The area with severe drought was always over the one with extreme drought, and when the severe drought area increased; the extreme drought area decreased Result of this study could be employed in planning of drought mitigation and adaptation, the use of water and land resources, and public information on risks and actions for drought-affected communities.

Modeling the impacts of 2D/3D urban structure on PM2.5 at high resolution by combining UAV multispectral/LiDAR measurements and multi-source remote sensing images

Understanding the impacts of 2D/3D urban structure on PM2.5 is critical for protecting resident health and sustainable development. However, accurate understanding is limited by the lack of high-resolution PM2.5 simulation in cities with sparse monitoring stations and the simultaneously consideration of different landscape types both in 2D and 3D directions. Using Yinchuan City as a case study, an integrated simulation model combining satellite AOD, NDVI and meteorological data was developed using random forest (RF) algorithm to simulate city-scale PM2.5 variation at 30 m resolution. Then, the most critical 2D/3D urban metrics in altering PM2.5 and their impacts were explored through RF analysis. Finally, optimal urban structure was identified using Bayesian Network and multi-scenario analysis. The results indicated that 1) the established RF model could effectively simulate within-city variation of PM2.5 at 30 m with an R2 of 0.75. Thus, it is feasible to use the abundant temporal information and high-resolution AOD to map city-scale PM2.5 and overcome the limitation of spare monitoring stations. 2) The emission-related metric of distance to polluting enterprise and city center had the largest impacts on PM2.5, followed by NDVI, building height, impervious surface area and shape, and UGS green volume, area, shape and connectivity. Optimized 3D urban structure for reducing PM2.5 was proposed from the three aspects of emission source location, UGS pattern and impervious surface. This study deepens the understanding of how 2D/3D urban structure impact PM2.5 and provides scientific references for optimization possibility of urban air quality in both horizontal and vertical directions.

Assessing changes in vegetation cover using time-series MODIS NDVI data : A case study of Bayantal soum in Govisumber Province

Determining the pattern of changes in vegetation cover is vital for understanding the environmental change and its influencing factors, as well as for planning and implementing optimal management for mitigation and adaptation. This study aimed to determine the changes in the vegetation cover of the Bayantal soum in Govisumber province, located in transition zones between the steppe and the Gobi. We obtained MODIS NDVI data with a spatial resolution of 250 m and spanning June, July, and August of 2000 to 2020 from the NASA EOS. We applied Sen’s slope and Mann-Kendal test to detect spatial-temporal changes in vegetation cover and its trend. According to the results, average NDVI values ranged between 0.2 to 0.4 in Bayantal soum. During the past 21 years, the number of NDVI pixels with values of 0.2-0.3 significantly decreased, whereas, the number of NDVI pixels with values of 0.3-0.4 increased. Long-term NDVI series data indicated that vegetation cover in the soum had a positive change or increasing tendency. A significant increase in the NDVI was observed and accounted for 46.3% of the total area of the soum. The decrease in NDVI was estimated at 0.1 percent of the total area of the soum but it was not significant. The remaining areas of the soum experienced variations in NDVI. The analysis revealed that vegetation cover increased by 0.0056 units per year. Overall, the vegetation cover in the Bayan soum increased over the past 21 years, which conflicts with other studies indicating the deterioration. Therefore, remote sensing data and field monitoring surveys are recommended to detect robust changes in vegetation cover. MODIS хиймэл дагуулын NDVI мэдээг ашиглан ургамал нөмрөгийн өөрчлөлтийг үнэлэх нь: Говьсүмбэр аймгийн Баянтал сумын жишээн дээр ХУРААНГУЙ: Ургамлан нөмрөгийн өөрчлөлтийг судлах нь байгаль, экологид гарч буй өөрчлөлт, түүнд нөлөөлөх хүчин зүйлсийг танин мэдэх, цаашилбал тэрхүү байгаль экологийн өөрчлөлтийг сааруулах, дасан зохицоход чиглэгдсэн менежментийн оновчтой арга хэмжээг төлөвлөх, хэрэгжүүлэхэд чухал ач холбогдолтой юм. Энэхүү судалгааны ажлын хүрээнд, хээрийн бүсээс говийн бүсэд шилжих экотон бүс нутагт орших Говьсүмбэр аймгийн Баянтал сумын ургамал нөмрөгт гарч буй өөрчлөлтийг тодорхойлохыг зорьсон болно. Судалгаанд MODIS хиймэл дагуулын 2000-2020 оны 6, 7, 8-р саруудын 250 м-ийн орон зайн шийдтэй ургамлын нормчлогдсон ялгаврын индекс (NDVI)-ийн бүтээгдэхүүнийг АНУ-ын Сансар судлалын агентлагийн Эх дэлхийг ажиглах системийн (NASA EOS) мэдээллийн серверээс татан авч ашигласан. NDVI-ийн урт хугацааны цуваа мэдээнд тулгуурлан ургамал нөмрөгт гарч буй өөрчлөлтийг Тейл-Сений налуугийн аргаар, өөрчлөлтийн хандлагыг Манн-Кендаллын аргаар тооцсон. Хуурай хээрийн бүсэд орших Баянтал сумын хэмжээнд ургамлын индексийн зонхилох утга 0.2-0.4 хэлбэлзэж байна. Сүүлийн 21 жилийн хугацаанд NDVI-ийн 0.2-0.3 утгатай пикселийн тоо алгуур багасаж, 0.3-0.4 утгатай пикселийн тоо нэмэгдэх хандлага ажиглагдсан. NDVI-ийн урт хугацааны цуваа мэдээнд хийсэн дүн шинжилгээнээс үзвэл, судалгааны талбайн хэмжээнд NDVI-ийн утга өсөх хандлагатай байгаа хэдий ч, p<0.05 статистик үнэмшлийн түвшинд авч үзвэл, нийт нутгийн 46.3%-д статистикийн ач холбогдолтой өсөх, 0.1%-д статистикийн ач холбогдолгүй NDVI-ийн буурах хандлага ажиглагдсан бол сумын бусад нутагт NDVI-ийн өөрчлөлт статистикийн ач холбогдолгүй байна. Судалгааны талбайн хэмжээнд ургамал нөмрөг 0.0056 нэгж/жилээр өссөн дүнтэй байна. Хиймэл дагуулын 21 жилийн цуваа мэдээнд тулгуурлан тооцоход тус сумын ургамал нөмрөгт сайжрах хандлага илэрч байгаа хэдий ч, бусад судлаачдын уламжлалт хээрийн судалгааны аргаар хийж гүйцэтгэсэн судалгааны үр дүн ургамал нөмрөг доройтсон болохыг харуулж байна. Иймээс тандан судалгааны аргаар тооцоолсон ургамал нөмрөгийн төлөв байдал, өөрчлөлт, хандлагыг тодорхойлох судалгааны үр дүнг хээрийн мониторинг судалгаагаар нарийвчлан судлах нь зүйтэй юм. Түлхүүр үгс: Баянтал сум, ургамлын нормчилсон ялгаврын индекс (NDVI), ургамал нөмрөгийн өөрчлөлт

Response of Vegetation Phenology to Climate Change on the Tibetan Plateau Considering Time-Lag and Cumulative Effects

The study of the response of vegetation phenology in the Qinghai Tibet Plateau to various climatic variables is paramount to unveiling the reaction of alpine ecosystems to worldwide climate alterations. Nonetheless, the lagged and cumulative effects of various climatic variables on vegetation phenology in the Qinghai Tibet Plateau remain unclear. Therefore, based on MODIS NDVI data, we extracted vegetation phenological parameters from 2001 to 2020, including the start of the vegetation growing season (SOS) and the end of the vegetation growing season (EOS), and then analyzed the response mechanisms of vegetation phenology to pre-seasonal air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) in the Qinghai Tibet Plateau on the basis of an investigation of the lag and cumulative effects. The results showed that: (1) the multiyear mean values of the SOS mainly occurred from 120 to 160 days, accounting for 86.17% of the study area, while the multiyear mean values of the EOS were mainly concentrated between 260 and 280 days, accounting for 77.05% of the study area; (2) air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) had different degrees of lagging effects on the SOS and the EOS. Among them, the time lag effect of precipitation on vegetation phenology was more pronounced; (3) different climatic variables had distinct cumulative effects on vegetation phenology. In contrast to the insignificant cumulative effects of temperature and nighttime surface temperature on the SOS and the EOS, the cumulative effects of precipitation and daytime land surface temperature on the SOS were more pronounced than those on the EOS; (4) the SOS and air temperature, precipitation, and NLST were mainly negatively correlated, in which the proportion of the negative correlation between SOS and NLST was up to 68.80%, and SOS and DLST were mainly positively correlated with a positive correlation proportion of 73.27%, EOS and air temperature, precipitation, and NLST were positively correlated with a positive correlation proportion of EOS and precipitation of up to 71.52%, and EOS and DLST were mainly negatively correlated with a negative correlation ratio of 55.87%.

Ecosystems dynamics and environmental management: An NDVI reconstruction model for El Alto-Ancasti mountain range (Catamarca, Argentina) from 442 AD through 1980 AD

Vegetation dynamics play a crucial role in understanding the complex interactions between climate variability, ecosystem changes and human land use. In this study, we use the Hindcasting Ecosystems Model (HEMO) to reconstruct the paleo Normalized Difference Vegetation Index (paleo-NDVI), which provides valuable insights into the historical vegetation patterns. Our research focuses on the El Alto Ancasti mountain range, a region characterized by its rich environmental diversity, and archaeological evidence of pre-Hispanic agricultural village landscapes, showcasing the knowledge and management of diverse environments and their fluctuations.To reconstruct the paleo-NDVI, we employed a Neuronal Network trained with two datasets. First, we utilized NDVI data extracted from AVHRR/NOAA satellite images spanning from 1982 to 2015, representing 8 km pixels from the Global Inventory Modeling and Mapping Studies. Second, we incorporated growth ring chronologies from Northwest Argentina sourced from the International Tree Ring Data Bank, enabling the calibration of the NDVI-ring relationship. This methodology allowed us to reconstruct the paleo-NDVI back to 442 AD and to calculate anomalies.Our analysis revealed significant negative anomalies around 600 AD, coinciding with previous research conducted in the nearby Ambato valley. These anomalies may be attributed to the eruption of Tres Cruces volcano in Catamarca during that period. Additionally, extensive positive anomalies emerged from 1600 AD onwards, which align with anomalies detected in earlier studies. The eruption of Huaynaputina volcano in southern Peru in 1600 AD is a possible factor contributing to these positive anomalies.Ongoing investigations focusing on the paleoenvironment and human occupations in the El Alto-Ancasti area aim to provide further insights into how these detected changes could have influenced environmental management decisions. By integrating multidisciplinary approaches, we can better comprehend the complex relationship between vegetation dynamics, climatic events and human activities, ultimately enhancing our understanding of past environmental transformations and their implications for sustainable management strategies.

Nighttime Lights and Urban Expansion: Illuminating the Correlation between Built-Up Areas of Lagos City and Changes in Climate Parameters

The rapid urbanization of Lagos City has resulted in an expansion of urban and nighttime lights, which, in turn, places a significant burden on natural resources. This burden exacerbates the adverse impacts of changes in climate parameters, underscoring the need for measures to mitigate its effects. Therefore, this study examines the relationship between economic development, population growth, urban expansion, and climate change in Lagos City over two decades. GIS and remote sensing methods were used to process nighttime light, Landsat images, changes in climate parameters, and NDVI data to measure Lagos’ sustainability level. The results show that: (1) between 2000 and 2020, nighttime light coverage grew from 175.53 km2 to 631.16 km2. Lagos’ GDP grew by 88.9%, while the population increased from 13.4 million in 2000 to 26 million in 2019. (2) The built-up areas significantly increased from 13.0% in 2000 to 33.6% in 2020, while vegetation land declined, decreasing from 63.7% in 2000 to 46.4% in 2020. (3) Furthermore, Lagos City has experienced changes in climate parameters, with a decrease in annual rainfall from 2954.81 mm in 2000 to 1348.81 mm in 2020 and an increase in the average maximum temperature from 31.56 °C in 2000 to 31.79 °C in 2020. However, the rapid growth of cities has brought about significant environmental impacts. A strong relationship exists between horizontal urban development and nighttime light, indicating that urban areas encroach on natural landscapes as Lagos grows. Then, there is a relationship between urban development and vegetation and between temperature and vegetation. A compact city planning approach, which prioritizes vertical development and efficient land use to mitigate urban sprawl and preserve green spaces, is recommended.

Constructing a high-precision precipitation dataset on the data-limited Qinghai-Tibet Plateau

The primary constraint hindering a comprehensive understanding of the atmospheric, hydrological, and ecological dynamics on the Qinghai-Tibet Plateau (QTP) is the absence of highly accurate precipitation datasets. Given the limited availability of climate observations and the pronounced spatial disparities in this region, traditional interpolation techniques are ill-suited, necessitating the development of novel methods. In this study, we formulated a random forest model leveraging precipitation data from 209 climate stations and multisource satellite datasets spanning the period from 1960 to 2020. These datasets encompassed low spatial resolution TRMM precipitation products and high spatial resolution NDVI data. This model facilitated the creation of a monthly precipitation dataset at a 1 km resolution for the QTP. Our findings showed that (1) the prevailing precipitation datasets for the QTP exhibited notable deficiencies, characterized by low spatial precision (R² = 0.592, p = 0.015) and incongruent temporal trends when compared to observational data (NSE = 0.398); (2) by incorporating multisource satellite data, we achieved a substantial enhancement in precipitation accuracy. For instance, the introduction of NDVI led to a reduction in precipitation estimation errors by 7.9 mm and 15.4 mm in desert and nondesert regions, respectively. The inclusion of TRMM satellite precipitation data not only improved accuracy in areas with limited climate stations by more than 16 % but also reduced cumulative monthly errors by 46.8 mm, and (3) the precipitation dataset generated through this method displayed a 24.8 % increase in accuracy (R² = 0.84) compared to existing datasets for the QTP, aligning well with actual precipitation trends (NSE = 0.806). This study offers an effective approach for acquiring high-precision precipitation datasets in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns. Furthermore, our method significantly enhances the spatial heterogeneity of precipitation products, potentially serving as a model for the development of datasets in data-scarce areas.

Wavelet Analysis of a Sentinel-2 Time Series to Detect Land Use Changes in Agriculture in the Vega Alta of the Guadalquivir River: Cantillana Case Study (Seville)

Historically, the Vega Alta of the Guadalquivir River (southern Spain) has been an anthropized space. Over time, the dominance of latifundia agriculture has evolved towards more intensive citrus-based agriculture. In this study, wavelet algorithms applied to Sentinel-2 time series were used to determine both the expansion of citrus plantations and the level of intensification of these plantations within the municipality of Cantillana. Sentinel-2 provides comprehensive global coverage from March 2017 to the present. Our study applied a 90% power wavelet transformation for the creation of a wavelet-smoothed time series for four years of Sentinel-2 NDVI data. Based on the data, it can be stated that within our research region covering 5000 hectares of agricultural land, over a span of four years (2017 to 2020), more than 980 hectares of native vegetation and pasture were transformed into citrus orchards, giving rise, at the end of 2020, to a total area of 3250 ha. Analyzing unique spatial patterns within a wavelet-smoothed time series data is very useful for land management, as it allows land use changes to be controlled. For this reason, it becomes feasible to assess the reliability of the wavelet method using both remote sensing and GIS tools.

Survival versus sustaining: A multidisciplinary inquiry of the environmental dilemma in rural Uganda

AbstractThe livelihoods of the majority of Uganda's population depend on declining environmental resources. The sustainability of the natural environment requires that the people who are interdependent with it, as well as the structures of governance and leadership around them, understand the implications of resource degradation and take deliberate steps towards restoration. The interdependency of human and environment conditions in Uganda requires multidisciplinary attention and this paper reflects a contribution to this end. Socio‐cultural perceptions and relations with a vulnerable environment are put into dialogue with the physical status of environmental resources in Alebtong District, Uganda. Southern epistemological perspectives are considered in relation to Western scientific paradigms. Culturally responsive socio‐cultural research data are related to MODIS NDVI data, using time series analyses and NDVI as a proxy for productivity. The research confirms the declining availability and condition of natural resources and the acknowledgement of local influence on this condition. Despite this, deliberate community and governance efforts towards conservation and restoration varied from non‐existent to insufficient. The causes for this inconsistency revolve around conflicting priorities. This paper demonstrates and discusses the difficult trade‐offs in decision‐making about natural resources that rural communities face, and supports new partnership models as a route to improvement.

Bioenergy crops monitoring using Sentinel-2 and UAV-derived imagery

Purpose. Establishing the regularities for the monitoring of the bioenergy crop conditions using Sentinel-2 and UAV-derived imagery. Methods. A field experiment was carried out in an experimental field of the Institute of Bioenergy Crops and Sugar Beet National Academy of Agrarian Sciences of Ukraine (50.023194, 30.173895), located in a zone of unstable soil moisture in the Right Bank Forest Steppe, in 2022−2023. Results. Among the studied traditional crops, sugar sorghum, sugar beet, and fodder beet are interesting crops from the standpoint of biomass for energy, as they can produce 110 t/ha, 120 t/ha, and 135 t/ha of biomass, respectively, or 20 t/ha, 26 t/ha, and 24 t/ha of dry matter, respectively. The yield of energy crops can be predicted with an acceptable level of accuracy using many known vegetation indices. However, the relationships between vegetation indices and crop yield are not consistent at every stage of plant growth and development, leading to low accuracy in yield estimation. The combination of vegetation indices related to the structural characteristics of the canopy and chlorophyll content in the aboveground biomass can improve the accuracy of yield estimation. Moreover, combining vegetation indices related to canopy structure, chlorophyll content, and stress indices as input to yield prediction models may provide even higher correlations for yield prediction. Conclusions. Usually, plantations of miscanthus and switchgrass are fertilised annually, while willow and poplar are fertilised every 3–4 years, after each harvesting. Therefore, the nutrient provision of tree species may not be sufficient. It was found that the content of total nitrogen in the soil of bioenergy plantations highly correlates with NDVI. To use NDVI for bioenergy crop prediction, an algorithm for determining the level of plant nutrients should be developed. It was found that at the end of the growing season (September or early October), the use of vegetation indices allows for the accurate estimation of the size and condition of bioenergy plantations since most crops in this period are already harvested. Sentinel-2-derived imagery is useful for monitoring bioenergy crop plantations as it provides images with a resolution of 10 m at 3–5-day revisiting time. We investigated NDVI on a total area of Miscanthus × gigantheus plantations of 2.9 ha. The satellite-derived data accumulated and aggregated by the OneSoil application as of August 20 and September 19 resulted in NDVI values of 0.80 and 0.70, respectively, while the NDVI obtained with UAV imagery was 0.82 and 0.77, respectively. Consequently, the satellite can provide quite acceptable NDVI data for use in the monitoring of bioenergy plantation yield at the national level.

Spatial and temporal analysis of drought resistance of different vegetation in the Ta-pieh Mountains based on multi-source data

Abstract This study focuses on the ecological and environmental safety of Ta-pieh Mountain. Drought episodes can lead to ecological problems such as vegetation damage. Therefore, quantifying the response of vegetation to drought is essential for ecological management. The study utilized normalized difference vegetation index (NDVI) and precipitation datasets from 1999 to 2019 to derive seasonal NDVI and standardized precipitation index (SPI) data. Using Theil-Sen median trend analysis and Mann-Kendall significance test analysis, we initially examined the characteristics of vegetation and drought for the 21-year time series. SPI is used to investigate and assess the occurrence and severity of drought in the research region. Then, the strength and variability of cropland, woodland, and grassland drought resistance in the Ta-pieh Mountains were discussed using the ratio of coefficient of variation (RCV). Finally, the cross-spectrum was used to calculate the vegetation lag time to drought. The study found that NDVI increased across all seasons, while SPI increased in spring and autumn and decreased in summer and winter. The spring drought had the most significant impact on vegetation. Cropland showed the highest improvement in drought tolerance and woodland showed the highest drought tolerance. The lagged response periods of cropland, woodlands, and grassland to drought were 1.62 months, 8.94 months, and 2.49 months, respectively. These findings provide a scientific basis for the management and preservation of the ecology of the Ta-pieh Mountains.

Monitoring of wetland cover changes in protected areas to trade-offs between ecological conservation and food security: A case study from the Dongting Lake, China

The United Nations' Sustainable Development Goals (SDGs) for 2030 underscore the urgency of “eradicating hunger and ensuring food security” (SDG 2: Zero Hunger) and the critical need to “cease and reverse land degradation and biodiversity decline” (SDG 15: Life on Land). The Dongting Lake area in China, serving as a significant wetland nature reserve and grain production zone, is increasingly confronted with complex, interdependent trade-offs between agricultural cultivation and the ecological and environmental preservation of wetlands (SDG 2 and 15). To analyze the trade-off between conservation and food security, it is crucial to rely on long-term, continuous monitoring of land cover changes in the lake area. However, the absence of reliable optical remote sensing has posed a significant challenge due to the prevalence of cloudy and rainy conditions. Our study begins by integrating long-term Landsat and MODIS data using the GF-SG fusion algorithm to generate 8-day NDVI data with a spatial resolution of 30 m. This analysis focuses on the areas outside the embankment of Dongting Lake (referred to as O-DTL) from 2003 to 2020. We then employ a pixel-connected sample migration method to facilitate automated sampling of land cover types and a random forest classification algorithm to analyze long-term dynamic changes. Finally, by combining meteorological and social data, we examine the temporal patterns of land cover changes in the study area and identify the underlying factors influencing them. The results indicate a trade-off between the fluctuation of cropland and forest area from 2003 to 2020. Water body areas have exhibited a significant decrease, while impervious surface areas have consistently increased annually. Impervious surface areas have experienced a substantial increase of approximately 70%, with almost 90% of this expansion resulting from the conversion of cropland. Under the premise of maintaining a stable area for food cultivation, the land consumed by urban expansion eventually encroached on the wetland water bodies of the reserve. The reduction in wetland water area in the O-DTL can be primarily attributed to agricultural cultivation, while urban expansion and urbanization have indirectly contributed to the decrease. This study provides a case in which prioritizing food security (SDG 2) takes precedence over ecological conservation (SDG 15), highlighting its significant implications for guiding subsequent development in the Dongting Lake area. This finding is crucial for minimizing unintended consequences and maximizing co-benefits between sustainable development goals.

Spatiotemporal climate and vegetation trends, and their relationship: A case of Genale Dawa basin, Ethiopia

Understanding the variability of climate and vegetation, and their relationship in space and time are crucial for agricultural planning, flood risk assessment, and drought monitoring. However, most studies depend on scarce and unevenly distributed climate station datasets in most basins of Ethiopia like the Genale Dawa basin. Detailed climate and vegetation variabilities and their relationship have not well studied in the Genale Dawa basin, which is often affected by recurrent drought. Satellite remote sensing-derived high spatial resolution climate and vegetation data can provide detailed information on climate variability and vegetation changes. This study assessed spatiotemporal variability of temperature, rainfall, and vegetation greenness and their relationship in the Genale Dawa basin. For this study, temperature (minimum and maximum), rainfall data (4 × 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (250 × 250 m) were used. Statistical indices like the Mann Kendall (MK) trend test, Coefficient of Variation (CV), Standard Anomaly Index (SAI), Precipitations Concentration Index (PCI), and Pearson correlation techniques were used to examine the trends and variabilities. The result generally indicated increasing trends in rainfall during the dry (“Bega”) season, and maximum and minimum temperatures during Kiremt (Wet season) and annual seasons. Moreover, high rainfall variability in the Dry and Kiremt seasons and low variability of temperature at all timescales were observed in the basin. In addition, anomalies in temperatures (minimum and maximum) and rainfall were observed in the basin during all the timescales. The result further showed a strong correlation of NDVI with rainfall at annual (r = 0.58), dry season (r = 0.9), and the short rainy season (r = 0.7) while weakly correlated at Kiremt (r = 0.31) seasons in the Genale Dawa basin. The result generally showed spatial and temporal variability of climate and vegetation greenness in the Genale Dawa basin, which negatively affects agricultural production, water resource availability, and vegetation growth. Therefore, appropriate management strategies should be implemented to minimize the negative effects.

Spatial variations in the difference in elevational shifts between greenness and temperature isolines across the Tibetan Plateau grasslands under warming

Climate warming has induced widespread isotherm shifts toward higher elevations on the Tibetan Plateau, but elevational shifts of vegetation greenness (indicated by Normalized Difference Vegetation Index, NDVI) do not necessarily keep pace with the isotherm shifts. Thus, there should be spatial variations in the difference between the velocities of vertical movement of greenness isolines (VNDVI) and isotherms (VT) across the Tibetan Plateau grasslands. Using satellite-observed NDVI and gridded climate data during 2000–2017, we found uphill shifts of the isotherms in 81.8 % of the surveyed areas, mainly in the eastern, central, southwestern, and northeastern parts, whereas upward shifts of the greenness isolines were observed only in 49.7 % of these areas, mainly in the southeastern, west-central, and southwestern edge of Tibetan Plateau grasslands. In the areas where both the greenness isolines and isotherms shifted uphill, VDNVI was faster than VT in the west-central and northeastern parts, and VNDVI was smaller than VT in the western, south-central, central, and southeastern regions; the difference between VNDVI and VT was positively related with elevational gradient of NDVI (NDVIEG) in the areas where NDVIEG was negative and the temporal trend of NDVI was positive, and was negatively related with NDVIEG and temporal trends of NDVI and temperature in the areas where NDVIEG was positive and temporal trend of NDVI was negative. Our results revealed spatial heterogeneity in the difference in the elevational shifts between the isotherm and vegetation greenness isoline across the Tibetan Plateau grasslands, which is related with both diverse adaptation to local environment (NDVIEG) and complex responses of vegetation greenness to warming in terms of both direction and magnitude. These findings have important implications for the prediction of vegetation production and carbon cycle and the adaptive management of alpine grasslands under climate change.