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Vegetation ecosystems are sensitive to large-scale climate variability in climate transition zones. As a representative transitional climate zone in Northwest China, Gansu is characterized by a sharp climate and vegetation gradient. In this study, the spatiotemporal variations of vegetation over Gansu are characterized using the satellite-based normalized difference vegetation index (NDVI) observations during 2000–2020. Results demonstrate that a significant greening trend in vegetation over Gansu is positively linked with large-scale climate factors through modulating the water and energy dynamics. As a climate transition zone, the northern water-limited and southern energy-limited regions of Gansu are affected by water and energy dynamics, differently. In the water-limited region, a weakening Asian monsoon along with colder Central Pacific (CP) and warmer North Pacific (NP) Oceans enhances prevailing westerlies which bring more atmospheric moisture. The enhanced atmospheric moisture and rising temperature promote the local vegetation growth. In contrast, large-scale climate variations suppress the southwest monsoon moisture fluxes and reduce precipitation in southern energy-limited regions. In these energy-limited regions, temperature has more effects on vegetation growth than precipitation. Therefore, the greenness of vegetation is because of more available energy from higher temperatures despite overall drying conditions in the region. Based on the above mechanism, future scenarios for climate impacts on vegetation cover over Gansu region are developed based on the two latest generation from coupled climate models (Coupled Model Intercomparison Project Phase 5 and Phase 6; CMIP5 and CMIP6). In the near-term future (2021–2039), the vegetation is likely to increase due to rising temperature. However, the vegetation is expected to decrease in a long-term future (2080–2099) when the energy-limited regions become water-limited due to increasing regional temperatures and lowering atmospheric moisture flux. This study reveals an increasing desertification risk over Gansu. Similar investigations will be valuable in climate transition regions worldwide to explore how large-scale climate variability affects local ecological services under different future climate scenarios.

[EN] Mexico s vast archaeological research tradition has increased with the use of remote sensing technologies; however, this recent approach is still costly in emerging market economies. In addition, the scales of prospection, landscape, and violence affect the type of research that heritage-culture ministries and universities can conduct. In Central Mexico, researchers have studied the pre-Hispanic Settlement Pattern during the Mesoamerican Postclassic (900-1521 AD) within the scope of the Aztec Empire and its conquests. There are settlements indications before and during the rule of the central empire, but the evidence is difficult to identify, particularly in the southwest of the capital, in the transition between the Lerma and Balsas River basins and their political-geographical complexities. This research focuses on a Geographic Information System (GIS)-based processing of multiple source data, the potential prospection of archaeological sites based on spatial data integration from Sentinel-2 optical sensors, Unmanned Aerial Vehicle (UAV), Digital Terrain Model (DTM), Normalized Difference Vegetation Index (NDVI) and field validation. What is revealed is the relationship between terrain morphologies and anthropic modifications. A binary map expresses possible archaeological remnants as a percentage; NDVI pixels and the morphometry values were associated with anthropic features (meso-reliefs with a tendency to regular geometries: slope, orientation, and roughness index); they were then interpreted as probable archaeological evidence. Within archaeological fieldwork, with limited resources (time, funding and staff), this approach proposes a robust method that can be replicated in other mountainous landscapes that are densely covered by vegetation., [ES] México tiene una vasta tradición de investigación arqueológica que, en las últimas décadas, se ha incrementado con el uso de tecnologías de percepción remota; sin embargo, este enfoque sigue siendo costoso en el contexto de las economías emergentes. Además, las escalas de prospección, paisaje e inseguridad influyen en el tipo de investigación que realizan los ministerios de patrimonio cultural y las universidades. En el Centro de México, el Patrón de Asentamiento Prehispánico durante el Posclásico Mesoamericano (900-1521 d.C.), ha sido estudiado dentro del alcance del Imperio Azteca y sus conquistas. Hay indicios de asentamientos antes y durante el dominio del Imperio central, pero la evidencia es difícil de identificar; particularmente en el suroeste de la capital, en la transición entre las cuencas de los ríos Lerma y Balsas y sus complejidades político-geográficas. Esta investigación se centra en el procesamiento basado en GIS de datos de múltiples fuentes, la prospección de sitios arqueológicos apoyada en la integración de datos espaciales de los sensores ópticos Sentinel-2, el vehículo aéreo no tripulado (UAV), el modelo digital del terreno (MDT), el índice de vegetación de diferencia normalizada (NDVI) y la validación de campo, que revelan la relación entre las morfologías del terreno y las modificaciones antrópicas. Un mapa binario expresa los posibles remanentes arqueológicos como un porcentaje; los píxeles del NDVI y los valores de morfometría se asociaron a características antrópicas (mesorrelieves con tendencia a geometrías regulares: pendiente, orientación e índice de rugosidad), y se interpretaron como probable evidencia arqueológica. Dentro del trabajo de campo arqueológico, con recursos limitados (tiempo, finanzas y auxiliares), este enfoque sugiere un método robusto que puede ser replicado en otros paisajes montañosos que están densamente cubiertos por vegetación.

Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year−1). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

In the EU, aquaculture ponds cover an area of 360,000 ha and are a crucial part of the rural landscape. As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environmental studies to be able to accurately estimate the extent and the temporal dynamics of reed cover. Here, we propose a method for mapping reed cover in fish ponds from freely available Sentinel-2 imagery using the normalized difference vegetation index (NDVI), which we applied to Hungary, the third largest carp producer in the EU. The dynamics of reed cover in Hungarian fish ponds mapped using satellite imagery show a high degree of agreement with the ground-truth points, and when compared with data reported in the annual aquaculture reports for Hungary, it was found that the calculation of reed cover based on the NDVI-based approach was more consistent than the estimates provided in the report. We discuss possible applications of this remote sensing technique in estimating reed-like vegetation cover in fish ponds and the possible use of the results for climate change studies and ecosystem services assessment.

Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns.

Observing vegetation normalized difference vegetation index (NDVI) changes, climate change characteristics, and their response relationship have a great significance to the ecosystem's regulation and improvement the human settlements. Based on GIMMS AVHRR NDVI and MODIS NDVI datasets as well as temperature, precipitation, and sunshine duration data, this study used unitary linear trend analysis, correlation analysis, RS, and GIS data to analyze the spatiotemporal variation characteristics of vegetation NDVI in the middle and upper reaches of the Yellow River between 1989 and 2018. It also analyzed the spatiotemporal response between vegetation NDVI and climate factors (temperature, precipitation, and sunshine duration). The results showed that the vegetation NDVI in the study area had an increasing trend over the past 30 years, growing by 31.28%, and the NDVI change in 81.83% of the pixels was positive, the highest being 0.025. The temperature in the middle and upper reaches of the Yellow River showed an obvious upward trend, showing an overall distribution pattern of low temperature in the southwest and high temperature in the southeast. The precipitation showed a gentle upward trend and a spatial distribution pattern of a gradual decrease from southeast to northwest. The sunshine duration showed an obvious decreasing trend and a spatial distribution pattern of gradually increasing from southeast to northwest. In the past 30 years, the annual mean NDVI in the study area had a positive correlation with temperature and precipitation and a negative correlation with sunshine duration.

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

Recently, attention has been drawn to the need to integrate sex/gender more comprehensively into environmental health research. Considering theoretical approaches, we define sex/gender as a multidimensional concept based on intersectionality. However, operationalizing sex/gender through multiple covariates requires the usage of statistical methods that are suitable for handling such complex data. We therefore applied two different decision tree approaches: classification and regression trees (CART) and conditional inference trees (CIT). We explored the relevance of multiple sex/gender covariates for the exposure to green spaces, measured both subjectively and objectively. Data from 3742 participants from the Cooperative Health Research in the Region of Augsburg (KORA) study were analyzed within the INGER (Integrating gender into environmental health research) project. We observed that the participants’ financial situation and discrimination experience was relevant for their access to high quality public green spaces, while the urban/rural context was most relevant for the general greenness in the residential environment. None of the covariates operationalizing the individual sex/gender self-concept were relevant for differences in exposure to green spaces. Results were largely consistent for both CART and CIT. Most importantly we showed that decision tree analyses are useful for exploring the relevance of multiple sex/gender dimensions and their interactions for environmental exposures. Further investigations in larger urban areas with less access to public green spaces and with a study population more heterogeneous with respect to age and social disparities may add more information about the relevance of multiple sex/gender dimensions for the exposure to green spaces.

Topographic effect in remote sensing images is severe in high mountainous areas. Efficiently to reduce the effects, several topographic normalization models have been proposed. Since the performance of the models is largely dependent on the spectral band and land surface type, the best performance model can vary from image to image in an area as well as from band to band in an image. The normalized difference vegetation index (NDVI) map has been widely used for the vegetation monitoring and assessment. An efficient reduction of the topographic effect in the NDVI map must be required for the spatial analysis of the vegetation monitoring and assessment. In this paper, we propose an efficient method to select the best topographic normalization model in each band to reduce the topographic effect of NDVI maps. The histogram structural similarity (HSSIM) index was used for the model selection because the index allows to select the best model in each band of an image. Five topographic normalization models were used for the test, which include the sun-canopy-sensor (SCS), statistical-empirical, C-correction, Minnaert, and Minnaert + SCS. The performance of the proposed method was validated by using two different season Landsat-8 OLI images including the forest area of northern Malaysia. The standard deviations of the two NDVI maps generated from the test images were reduced by about 53.1% and 28.6% after correction in profile analysis. The coefficient of determination (R2) between the two different NDVI maps increased from 0.626 to 0.759. It indicates that the proposed method effectively reduced the topographic effect of the NDVI maps. This result implies that the proposed method can work well in the topographic normalization. Furthermore, the proposed method would be successfully applied to index maps including the normalized difference snow index (NDSI), normalized difference water index (NDWI), etc.

Precision agriculture (PA) practices based on the use of sensors and vegetation indices have great potential for optimizing herbicide use and improving weed management in field crops. The objective of this research was to evaluate the efficacy of commercial herbicide products and their selectivity in durum wheat by measuring the Normalized Difference Vegetation Index (NDVI). Field trials were conducted in Velestino and Kozani, Greece (2020–2021 and 2021–2022) in four site-years with the following treatment list: untreated control (T1), 2,4-D at 300 and 600 g a.e. ha−1 (T2 and T3, respectively), pyroxsulam + florasulam at 18.82 + 3.71 g a.i. ha−1 + cloquintocet-mexyl at 18.82 g a.i. ha−1 (T4), and mesosulfuron-methyl + iodosulfuron-methyl-sodium at 15 + 3 g a.i. ha−1 + mefenpyr-diethyl at 45 g a.i. ha−1 (T5). Site-years and treatments affected weed NDVI, weed biomass, crop NDVI, and grain yield (p ≤ 0.05). At Kozani, weed NDVI was lowest in T4 plots in 2020–2021 (0.31) and 2021–2022 (0.33). Treatments T4 and T5 resulted in lowest weed biomass in 2020-2021 (14–16 g m−2) and 2020-2021 (19–22 g m−2). At Velestino, T3 reduced weed biomass by 92 and 87% when compared to T5 in 2020–2021 and 2021–2022, respectively. Approximately, 67% and 73% of the variability in weed biomass in 2020–2021 and 2021–2022, respectively, at Kozani could be explained by weed NDVI. These parameters were strongly correlated in Velestino (R2 ≥ 90%). Low crop NDVI at Kozani indicated herbicide injury in T3 plots, confirmed by yield losses. During 2020-2021, yield was 30, 38, and 40% higher in T4 plots than in T2, T1, and T3 plots, respectively. At Velestino, yield in T1 plots was 25, 27, 27, and 29% lower than in T2, T4, T5, and T3 plots, respectively, in 2020–2021. Similar results were obtained in 2021–2022. The current study indicates that NDVI can be used as a reliable, non-subjective indicator of herbicide efficacy and selectivity in winter cereals. The methodology used in this work should also be evaluated in other crops and under different soil and climatic conditions.

Quantitative assessment of the contributions of climate change and human activities to vegetation change is important for ecosystem planning and management. To reveal spatial differences in the driving mechanisms of vegetation change in the Qinling Mountains, the changing patterns of the normalized difference vegetation index (NDVI) in the Qinling Mountains during 2000–2019 were investigated through trend analysis and multiple regression residuals analysis. The relative contributions of climate change and human activities on vegetation NDVI change were also quantified. The NDVI shows a significant increasing trend (0.23/10a) from 2000 to 2019 in the Qinling Mountains. The percentage of areas with increasing and decreasing trends in NDVI is 87.96% and 12.04% of the study area, respectively. The vegetation change in the Qinling Mountains is caused by a combination of climate change and human activities. The Tongguan Shiquan line is a clear dividing line in the spatial distribution of drivers of vegetation change. Regarding the vegetation improvement, the contribution of climate change and human activities to NDVI increase is 51.75% and 48.25%, respectively. In the degraded vegetation area, the contributions of climate change and human activities to the decrease in NDVI were 22.11% and 77.89%, respectively. Thus, vegetation degradation is mainly caused by human activities. The implementation of policies, such as returning farmland to forest and grass, has an important role in vegetation protection. It is suggested that further attention should be paid to the role of human activities in vegetation degradation when formulating corresponding vegetation protection measures and policies.

Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R2 = 0.86, RMSE = 0.18 t ha−1). RF models for individual paddocks in VIC (R2 = 0.89, RMSE = 0.15 t ha−1) and NSW (R2 = 0.87, RMSE = 0.07 t ha−1) performed well, but moderate performance was seen for SA (R2 = 0.45, RMSE = 0.25 t ha−1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.

Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting data over large areas; remote sensing compensates for these shortcomings. In this study, the time-series variation characteristics of different vegetation types were analyzed, and the extraction method of alfalfa mapping was established according to different spatial- and temporal-resolution remote sensing data. The results provided the following conclusions: (1) when using the wave peak and valley number of normalized difference vegetation index (NDVI) curves, in the study area, the number of wave peak needed to be greater than 2 and the number of wave valley needed to be greater than 1; (2) 91.6% of alfalfa sampling points were extracted by moderate resolution imaging spectroradiometer (MODIS) data using the wave peak and valley method, and 5.0% of oats sampling points were extracted as alfalfa, while no other vegetation types met these conditions; (3) 85.3% of alfalfa sampling points were identified from Sentinel-2 multispectral instrument (MSI) data using the wave peak and valley method; 6.0% of grassland vegetation and 8.7% of oats satisfied the conditions, while other vegetation types did not satisfy this rule; and (4) the temporal phase selection was very important for alfalfa extraction using single-time phase remote sensing images; alfalfa was easily separated from other vegetation at the pre−wintering stage and was more difficult to separate at the spring regreening stage due to the variability in the alfalfa overwintering rate; the overall classification accuracy was 92.9% with the supervised classification method using support vector machine (SVM) at the pre-wintering stage. These findings provide a promising approach to alfalfa mapping using different remote sensing data.

Separating the effects of human activities/climate change on lotic ecosystems is one of the important components of environmental management as well as water resources maintenance. A Mann–Kendall analysis of hydro-climatic parameters and vegetation cover (VC), calculated using normalized difference vegetation index (NDVI), during the period 1985–2014 suggested a significant decrease and increase of river flow and temperature at p < 0.01, as well as an insignificant decline and increment of precipitation and VC, respectively within the arid and semi-arid region, that is, Zilbier River basin in north-western Iran. A separation of human activities/climate change effects on the reduction of river flow was carried out using three alternative approaches: a simple eco-hydrological method (coupled water-energy budget (ECH)), elasticity-based analysis (Budyko framework (EBA)), and a process-based watershed model based on the Soil and Water Assessment Tool (SWAT). The efficiency of these approaches was assessed over the periods 1985–1994, 1995–2014, and under five potential near-future human activities/climate change scenarios (S1–S5) by 2030. The results indicated that the climate change impacts on river flow was more severe than those of human activities. Climate change contributed to an average of 83.6% and 77.0% reduction in river flow in the past and the realistic future scenarios (i.e., S4 and S5), respectively, while human activities accounted for 16.4% and 30%. According to our findings, despite the fact that ECH results are more in line with the SWAT model, in case of physical characterization inaccessibility, ECH and EBA (as simple descriptive and quantitative models, respectively) can be used to separate, simulate and project the impacts of human activities and climate changes on river flow.

Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI) related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus) daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx) of Spain and the Fifth National Forest Inventory (IFN5) from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN) was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77) between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.

One of the key steps that would lead winegrowers to implement precision viticulture as a management tool would be the clear demonstration of the agronomic and oenological significance of the zones delineated within a vineyard based, totally or partially, on remote-acquired information. To perform this analysis, it is necessary to compare image-derived variables to crop characteristics. Classical ordinary least square (OLS) regression is not well suit for spatially structured data, while Moran’s index (MI) and local indicators of spatial autocorrelation (LISA) take autocorrelation into account. The aim of this work was to evaluate the performance of statistical methods to compare different maps of a vineyard, some including variables derived from UAV acquired imagery, and some from in situ ground characterization. The study was conducted during 2015 and 2016 seasons in an adult 7.5 ha cv. ‘Tempranillo’ vineyard located in Traibuenas, Navarra, Spain. The maps obtained out of UAV-imagery, volume index (VI) and normalized difference vegetation index (NDVI) were compared to the maps obtained for the agronomic variables measured (yield, berry weight and total soluble solids). The bivariate MI and the bivariate LISA cluster map obtained using Geoda software indicate depict the spatial cluster association between variables in 2015 and 2016 with different types of local spatial autocorrelation. The use of these methods that take into account data spatial structure, to compare ground autocorrelated data and spectral and geometric information derived from UAV-acquired imagery has been proved to be highly necessary and advisable.

The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands’ wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages.

The knowledge of soil apparent thermal diffusivity (k) is important for investigating soil surface heat transfer and temperature. Long-term k determined using the near-surface soil temperature is limited on the Tibetan Plateau (TP). The main objective of this study is to determine k with a conduction–convection method using the near-surface soil temperature measured at three sites during 2014–2016 on the TP. The hourly, daily, and monthly k values of the 0.0 m to 0.20 m layer were obtained. The hourly and daily k values ranged from 0.3 × 10−6 m2 s−1 to 1.9 × 10−6 m2 s−1 at the wet site, and from 1.0 × 10−7 m2 s−1 to 4.0 × 10−7 m2 s−1 at the two dry sites. For the monthly timescale, k ranged from 0.4 (±0.0) × 10−6 m2 s−1 to 1.1 (±0.2) × 10−6 m2 s−1 at the wet site, and varied between 1.7 (±0.0) × 10−7 m2 s−1 and 3.3 (±0.2) × 10−7 m2 s−1 at the two dry sites. The k was not constant over a day, and it varied seasonally to different degrees at different sites and years. The variation of k with soil moisture (θ) appeared to be roughly similar for unfrozen soil at these sites and years, namely, k increased sharply before reaching the peak as θ increased, and then it tended to be stable or varied slightly with further increases in θ. This variation trend was consistent with previous studies. However, the relationship between k and θ changed when soil temperature was below 0 °C, because ice had higher k than water. The correlation coefficients (r) between k and θ ranged from 0.37 to 0.80, and 0.80 to 0.92 on hourly and monthly timescales, respectively. The monthly and annual k values were significantly correlated (r: 0.73~0.93) to the Normalized Difference Vegetation Index (NDVI). The results broaden our understanding of the relationship between in situ k and θ. The presented values of k at various timescales can be used as soil parameters when modeling land–atmosphere interactions at these TP regions.

In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal–spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000–2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (p < 0.01); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69° and to the northwest by 1.81°, respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development.

Haemosporidians are among the most common parasites of birds and often negatively impact host fitness. A multitude of biotic and abiotic factors influence these associations, but the magnitude of these factors can differ by spatial scales (i.e., local, regional and global). Consequently, to better understand global and regional drivers of avian-haemosporidian associations, it is key to investigate these associations at smaller (local) spatial scales. Thus, here, we explore the effect of abiotic variables (e.g., temperature, forest structure, and anthropogenic disturbances) on haemosporidian prevalence and host–parasite networks on a horizontal spatial scale, comparing four fragmented forests and five localities within a continuous forest in Papua New Guinea. Additionally, we investigate if prevalence and host–parasite networks differ between the canopy and the understory (vertical stratification) in one forest patch. We found that the majority of Haemosporidian infections were caused by the genus Haemoproteus and that avian-haemosporidian networks were more specialized in continuous forests. At the community level, only forest greenness was negatively associated with Haemoproteus infections, while the effects of abiotic variables on parasite prevalence differed between bird species. Haemoproteus prevalence levels were significantly higher in the canopy, and an opposite trend was observed for Plasmodium. This implies that birds experience distinct parasite pressures depending on the stratum they inhabit, likely driven by vector community differences. These three-dimensional spatial analyses of avian-haemosporidians at horizontal and vertical scales suggest that the effect of abiotic variables on haemosporidian infections are species specific, so that factors influencing community-level infections are primarily driven by host community composition.

Habitat degradation and hunting pose the most proximate threats to many primate species, while climate change is expected to exacerbate these threats (habitat and climate change combined henceforth as "global change") and present new challenges. Madagascar's lemurs are earth's most endangered primates, placing added urgency to their conservation in the face of global change. My dissertation focused on the critically endangered golden-crowned sifaka (Propithecus tattersalli; hereafter, "sifaka") which is endemic to fragmented forests across a gradient of dry, moderate, and wet forest types in northeastern Madagascar. I surveyed sifakas across their global range and investigated factors affecting their densities. I explored sifaka diets across different forest types and evaluated if nutritional factors influenced sifaka densities. Lastly, I investigated sifaka range-wide genetic diversity and conducted a connectivity analysis to prioritize corridor-restoration and other potential conservation efforts. Sifaka densities varied widely across forest fragments (6.8 (SE = 2.0-22.8) to 78.1 (SE = 53.1-114.8) sifakas/km²) and populations have declined by as much as 30-43% in 10 years, from ~18,000 to 10,222-12,631 individuals (95% CI: 8,230-15,966). Tree cutting, normalized difference vegetation index (NDVI) during the wet season, and Simpson's diversity index (1-D) predicted sifaka densities range-wide. Sifakas consumed over 101 plant species and spent 27.1% of their active time feeding on buds, flowers, fruits, seeds, and young and mature leaves. Feeding effort and plant part consumption varied by season, forest type, and sex. Minerals in sifaka food items (Mg (β = 0.62, SE = 0.19) and K (β = 0.58, SE = 0.20)) and wet season NDVI (β = 0.43, SE = 0.20) predicted sifaka densities. Genetic measures across forest fragments indicated that sifaka populations are becoming more isolated (moderate FIS values: mean = 0.27, range = 0.11-0.60; high M-ratios: mean = 0.59, range = 0.49-0.82; low overall effective population size: Ne = 139.8-144 sifakas). FST comparisons between fragments (mean = 0.12, range = 0.01-0.30) supported previous findings that sifakas still moved across the fragmented landscape. Further validation of these genetic results is needed. I identified critical corridors that conservation managers could protect and/or expand via active reforestation to ensure the continued existence of this critically-endangered lemur. Doctor of Philosophy Worldwide, many species of primates are threatened with extinction due to habitat degradation, hunting, and climate change (habitat and climate combined threats, henceforth, "global change"). These threats work at different time scales, with hunting being the most immediate and climate change likely to have its fullest impact experienced from the present to a longer time frame. Lemurs are a type of primate found only on Madagascar, an island experiencing rapid global change, which puts lemurs at a heightened risk of extinction. My dissertation research focused on the critically endangered golden-crowned sifaka (Propithecus tattersalli; hereafter, "sifaka"), a species of lemur found only in a few isolated forests across a dry to wet gradient in northeastern Madagascar. To better understand their extinction risk, I conducted surveys to estimate the number of sifakas remaining and investigated several factors that might determine how many sifakas can live in one place. I then explored how sifaka diets varied depending on the forest type that they inhabit and tested whether nutrients in their food might determine sifaka numbers. Lastly, I calculated sifaka genetic diversity to assess their ability to adapt to new environmental conditions and to determine whether sifakas can move across the landscape to find new mates and to potentially colonize new areas of habitat. Sifaka densities varied widely across their range (6.8-78.1 sifakas/km² ). Only 10,222-12,631 sifakas remain, which is 30-43% less than the range of estimates obtained 10 years ago (~18,000 sifakas). Tree cutting, normalized difference vegetation index (NDVI; a measure of plant health or "greenness" obtained from satellite data), and a tree species diversity index were useful measures to predict sifaka densities. Sifakas ate different plant parts (buds, flowers, fruits, seeds, and leaves) from over 101 plant species. The amount of time they spent eating each day varied by the time of year, forest type, and sex. On average, they spent a quarter of their day eating. Magnesium and potassium concentrations in sifaka food items also were useful nutrition-related measures to predict sifaka densities. Genetic analyses suggested that sifaka populations are becoming more isolated and inbred, meaning sifakas are breeding with other sifakas to which they are closely related. However, it appears that sifakas still can move between forest patches to find new mates and to potentially colonize new areas, if such areas are created. Further validation of these genetic results is needed. I also identified critical areas that will be important to protect and reforest to ensure that movements between populations can continue.

Double-skin green facades using deciduous climbing plants are easy-to-implement construction systems stated to be effective energy-saving tools for buildings during cooling periods. Although the leaf area index (LAI) has been identified as a key parameter for characterizing foliar density and, consequently, the green facade's potential as a passive tool for energy savings, a lack of knowledge still remains on this index's values and measurement methods. The present paper aims to characterize the annual LAI evolution of a Boston ivy double-screen green facade under Mediterranean continental climate (Csa), by using an original non-destructive methodology during two consecutive years. Moreover, the influence of the green facade's foliage density, characterized by LAI, on the external building wall temperatures and the energy consumption by season and orientation was addressed. From the results it can be noticed that LAI changed seasonally over the course of five periods with a related differentiated energy performance: early summer (LAI of 4.8; 54% savings for cooling), late summer (LAI of 4.4; 30% savings for cooling), autumn (LAI of 1.7; 5.4% increase for heating), winter (LAI of 0.9; 5.4% increase for heating), and spring (LAI of 3.6; 11.9% increase for heating). The increase of energy consumption during leaf-off stage was directly linked to woody material and remaining leaves. Two crucial effects were identified and characterized: firstly, the influence of facade orientation and, secondly, a slight 'insulation effect' at night, with the green screen acting as a thermal barrier. The authors at GREiA research group would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme.

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.

Recent studies have examined how the environment can influence obesity in young people. The research findings are conflicting: in some studies, green spaces have shown a protective association with obesity and urbanization has turned out to worsen this condition, while other studies contradicted these results. The aim of the study was to examine the relationships between greenness, urbanization, and weight status among Italian adolescents. Student data (11–13 years old) on weight and height, physical activity (PA), and demographic characteristics were extracted from the 2018 Health Behaviour in School-aged Children (HBSC) survey in Piedmont, Northwest of Italy. Data on Normalized Difference Vegetation Index (NDVI) and urbanization were obtained from satellite images and the National Institute of Statistics (ISTAT). A multilevel regression model was used to assess the association between NDVI, urbanization, and obesity, controlling for PA. Students living in greener areas reported a lower likelihood of being obese [OR = 0.11, 95% CI 0.02–0.56, p = 0.007], while students living in areas with a higher level of urbanization showed a significantly increased risk of obesity [OR = 2.3, 95% CI:1.14–4.6, p = 0.02]. Living surrounded by higher amounts of greenness and lower levels of urbanization may positively influence health status through lower risk of obesity among youth.

The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images.

Urban vegetation can generate social and ecological benefits, so vegetation abundance is commonly treated as a proxy for greater benefits. A repeated finding in environmental justice research related to urban vegetation is that commonly marginalized populations live in neighborhoods with less vegetation. However, urban vegetation can function as amenity or disamenity depending on the context and the characteristics of the vegetation. In areas of disinvestment, overgrown vegetation may indicate neglect and lead to negative social outcomes. For example, previous research in the shrinking city of Toledo, Ohio, showed that areas with concentrated residential vacancy and high representation of traditionally marginalized populations also had relatively high vegetation abundance. This can be largely attributed to spontaneous, weedy vegetation in areas of concentrated vacancy. Equal vegetation cover therefore should not necessarily be equated with environmentally just outcomes. Here, we used several high-resolution data sets to study the relationships among vegetation abundance, vegetation quality, and property parcel occupancy on residential land in Toledo. Our results demonstrate that vacant residential land had more abundant vegetation than comparable occupied parcels according to two common metrics (tree canopy cover and the normalized difference vegetation index). Compared to occupied parcels, vacant parcels also had higher rates of blight associated with overgrown vegetation, as recorded during a citywide ground-based survey of property conditions. There were more vacant parcels overall in areas of disinvestment, and on a per-parcel basis, vacant parcels in these high-vacancy areas were also greener relative to nearby occupied parcels than vacant parcels in low-vacancy areas. This indicates that vacant parcels play a disproportionately large role in greening on residential land in areas of disinvestment. These results reinforce the idea that simply quantifying vegetation abundance may be insufficient for understanding urban social-ecological outcomes. Incorporating parcel occupancy data along with multiple strands of information about vegetation type and condition provides context to understand where abundant vegetation functions as amenity versus disamenity. These perspectives are especially relevant in shrinking cities like Toledo where legacies of urban socioeconomic change have produced widespread areas of disinvestment and land abandonment.

The multiple spatial and temporal parameters affecting cattle herd distribution and activity dynamics can significantly affect resource utilization but are not fully understood. The aim of this study was to determine whether current animal tracking technology and spatio-temporal analysis tools can be used to integrate multi-scale information on herd distribution patterns as a function of seasonal forage production, periods of the day, animal activity, and landscape features. Positional and activity information of 11 free-ranging cows within a 31-member herd was obtained at 5-min intervals by using GPS collars for 1 year within a 457-ha ranch in the semi-arid rangelands of South Texas. Forage biomass was calculated with satellite imagery. Spatial analysis of cattle distribution and landscape features was conducted with GIS.Herd spread was greatest during the growing season. Throughout the year, during midday, the herd showed smaller spread and greater use of shade patches than any other time of day. Cattle also aggregated under trees in winter, particularly during the night. There was no statistically significant overall pattern of seasonal changes in the use of water and supplemental feeding areas, but a trend toward highest use during the winter. However, significantly different diurnal patterns in the use of supplemental feed and water were observed within each season.This study found a strong influence of shade patches relative to the influence of water and supplemental feeding areas on the diurnal and seasonal movement patterns of cattle in shrub-dominated rangeland. Although this study used only 11 tracked cows in a 31-member herd, the results indicated that techniques such as seasonal and diurnal GPS tracking, GIS, and remote sensing data enable evaluation of multiple spatial and temporal dynamics of cattle distribution and activity patterns. The smaller spread during the dry winter season associated with the observed aggregation of individuals in water and supplemental feeding areas, may aid in determining the most critical times for providing supplemental resources and guide the allocation of those resources to areas not frequently used by cattle, thus stimulating the animals to visit unused sites during the non-growing season.

Introduction/aims Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. Methods NDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. Results For the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. Conclusions LSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification.

Pembangunan infrastruktur dan jumlah penduduk yang terus meningkat di wilayah Kota Makassar memberikan dampak negatif berupa penurunan kualitas lingkungan dan peningkatan suhu udara yang dikenal dengan fenomena Urban Heat Island (UHI). Peningkatan suhu udara akibat fenomena UHI berdampak pada kualitas hidup manusia, kesehatan, kenyamanan masayarakat kota, dan peningkatan kekritisan lingkungan/Environmental Criticality Index (ECI). Penelitian ini bertujuan untuk menganalisis secara spasial dan multitemporal ECI di Kota Makassar menggunakan algoritma Land Surface Temperature dan Normalized Difference Vegetation Index. Penelitian ini menggunakan citra satelit Landsat 8 OLI/TIRS perekaman tahun 2013 – 2018 path 114 row 64 untuk menghitung suhu permukaan dan kerapatan vegetasi menggunakan algoritma Land Surface Temperature (LST) dan Normalized Difference Vegetation Index (NDVI). Indeks kekritisan lingkungan dihitung dan dianalisis menggunakan persamaan deduktif ECI dan pendekatan Sistem Informasi Geografis (SIG). Hasil penelitian menunjukkan adanya tren peningkatan LST pada wilayah dengan tutupan lahan bangunan dan indeks kerapatan vegetasi NDVI rendah dan membentuk formasi pulau bahang perkotaan (UHI). Hasil analisis ECI juga menunjukkan adanya tren peningkatan area lahan teridentifikasi kritis di Kota Makassar yaitu sebesar 20,69 Km2 dalam rentang waktu 2013 - 2018.Kata kunci; Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Environmental Criticality Index (ECI), Sistem Informasi Geografis (SIG)

Flood-fed aquifers along the sandy lower reach of the Kuiseb River sustain a 130-km-long green belt of lush oases across the hyperarid Namib desert. This oasis is a year-round source for water creating dense-tall woodland along the narrow corridor of the ephemeral river valley, which, in turn, supports human activity and fauna including during the long dry austral winters and multi-year droughts. Occasional floods, originating at the river's wetter headwaters, travel ∼280 km downstream, before recharging these aquifers. We analyzed the flood-aquifer-vegetation dynamics at-a-site and along the river, determining the relative impact of floods with diverse magnitude and frequency on downstream reaches. We find that flood discharge that feeds the alluvial aquifers also affects vegetation dynamics along the river. The downstream aquifers are fed only by the largest floods that allow the infrequent germination of plants; mean annual recharge volume is too low to support the aquifers level. These short-term vegetation cycles of green-up and then fast senescence in-between floods are easily detected by satellite-derived vegetation index. This index identifies historical floods and their magnitudes in arid and hyperarid regions; specifically, it determines occurrences of large floods in headwater-fed, ephemeral Namib streams as well as in other hyperarid regions. Our study reveals the importance of flood properties on the oasis life cycle, emphasizing the impact of drought and wet years on the Namib's riparian vegetation., This researc hwas funded by agrant from EU-WADE (GOCE-CT-2003-506680-WADE) and additional support from US-AIDCDRC24-026TA-MOU-04-C24-026.

Paper description: Understanding interactions between species traits and changing environmental conditions can contribute to better conservation planning and management of threatened open grassland ecosystems. Grassland specialist species, such as the European ground squirrel Spermophilus citellus L. 1766, are currently facing severe population decline. Human-induced conditions and abiotic factors are the main drivers of the species’ responses at individual and population levels to the environmental conditions in the Central Banat area (Serbia) Strategies aimed at stopping population decline should focus on both species traits and behavioral flexibility, ongoing changes in spatial heterogeneity, weather conditions and climate predictions. Abstract: Understanding the spatial and temporal effects of variable environmental conditions on demographic characteristics is important in order to stop the decline of endangered-species populations. To capture interactions between a species and its environment, in this work the demographic traits of the European ground squirrel (EGS), Spermophilus citellus, were modeled as a function of agricultural landscape structure. The habitat suitability index was determined for 20 localities within the study area based on habitat use, management and type. After mapping the habitat patch occupancy in the field, crop cover maps, the average normalized difference vegetation index (NDVI) and automated water extraction index (AWEI) were obtained from satellite images covering the period 2013-2015. This data was used to develop population-level generalized linear models (GLMs) and individual-level conditional mixed-effects models (GLMMs) in R package Ime4, focusing on the key demographic traits of the EGS. The land composition and patch carrying capacity (PCC) are the key determinants of the endangered EGS population size, while system productivity is the main factor influencing individuals’ body condition after monitoring for variations across sampling years and age classes. The proposed landscape structural models show that human activities and abiotic factors shape the demographic rates of the EGS. Thus, to conserve threatened species, an appropriate focus on the spatial adaptation strategies should be employed. https://doi.org/10.2298/ABS190715061N Received: July 15, 2019; Revised: September 2, 2019; Accepted: September 9, 2019; Published online: September 13, 2019 How to cite this article: Nikolic T, Arok M, Radisic D, Mirc M, Velaja L, Milic D, Cirovic D.Endangered species’ trait responses to environmental variability in agricultural settings. Arch Biol Sci. 2020;72(1):13-21.

The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.

The study uses 30 years of the third generation of Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g monthly data from 1982 to 2012 to identify the natural clusters and important driving factors of the upstream watersheds in Taiwan through hierarchical cluster analysis (HCA) and redundancy analysis (RDA), respectively. Subsequently, as a result of HCA, six clusters were identified based on the 30 years of monthly NDVI data, delineating unique NDVI characteristics of the upstream watersheds. Additionally, based on the RDA results, environmental factors, including precipitation, temperature, slope, and aspect, can explain approximately 52% of the NDVI variance over the entire time series. Among environmental factors, nine factors were identified significantly through RDA analysis for explaining NDVI variance: average slope, temperature, flat slope, northeast-facing slope, rainfall, east-facing slope, southeast-facing slope, west-facing slope, and northwest-facing slope, which reflect an intimate connection between climatic and orthographic factors with vegetation. Furthermore, the rainfall and temperature represent different variations in all scenarios and seasons. With consideration of the characteristics of the clusters and significant environmental factors, corresponding climate change adaptation strategies are proposed for each cluster under climate change scenarios. Thus, the results provide insight to assess the natural clustering of the upstream watersheds in Taiwan, benefitting future sustainable watershed management.

Understanding changes in urban vegetation is essential for ensuring sustainable and healthy cities, mitigating disturbances due to climate change, sustaining urban biodiversity, and supporting human health and wellbeing. This study investigates and describes the distribution and dynamic changes in urban vegetation over a 15-year period in Greater Melbourne, Australia. The study investigates how vegetation cover across Melbourne has changed at five-yearly intervals from 2001 to 2016 using the newly proposed dynamic change approach that extends the net change approach to quantify the amount of vegetation gain as well as loss. We examine this question at two spatial resolutions: (1) at the municipal landscape scale to capture broadscale change regardless of land tenure, and (2) at the scale of designated public open spaces within the municipalities to investigate the extent to which the loss of vegetation has occurred on lands that are intended to provide public access to vegetated areas in the city. Vegetation was quantified at four different times (2001, 2006, 2011, 2016), using the normalized difference vegetation index (NDVI). Dynamic changes of gain and loss in urban vegetation between the three periods were quantified for six local government areas (LGAs) and their associated public open spaces using a change matrix. The results showed an overall net loss of 64.5 square kilometres of urban vegetation from 2001 to 2016 in six LGAs. When extrapolated to the Greater Melbourne Area, this is approximately equivalent to 109 times the size of Central Park in New York City.

Remotely-sensed normalized difference vegetation index (NDVI) and land surface temperature (LST) are significant indicators for evaluating the environmental consequences of climate changes in arid regions. Understanding of the relationship between these changes and land surface properties of the arid regions is important in land planning. The Gavkhoni playa is a closed basin in the arid zone of Iran that has recently undergone significant environmental changes due to decreasing precipitations. The objective of this study was to explore the spatiotemporal changes of its LST and NDVI before and after the drought event of 2014–2018. Through the application of an integrated method based on cellular fractal model and continuous wavelet transform (CWT), we investigated the relationship between variation of surface fractal dimension and distribution of LST and NDVI changes before and after the drought. The results indicated substantial changes in LST and NDVI values associated with the onset of drought conditions. Changes in the spatial distribution of LST and NDVI were particularly high in the central playa area, the western and southern sides of the playa, including the salt lake, the wetlands, and the clay flat. These areas correspond to the zone 4 of the fractal dimension map where extreme topographic anomaly and complexity. Wavelet analysis confirmed the relationship between the surface fractal dimension pattern and anomalies in LST and NDVI variations. The investigation highlighted the ability of the integrated application of the cellular fractal model and wavelet analysis to quantify the relationship between land surface properties and the spatial variation of LST and NDVI in changing environmental conditions.

Northern wetlands and their productive tundra vegetation are of prime importance for Arctic wildlife by providing high-quality forage and breeding habitats. However, many wetlands are becoming drier as a function of climate-induced permafrost degradation. This phenomenon is notably the case in cold, ice-rich permafrost regions such as Bylot Island, Nunavut, where degradation of ice wedges and thermo-erosion gullying have already occurred throughout the polygon-patterned landscape resulting in a progressive shift from wet to mesic tundra vegetation within a decade. This study reports on the application of the normalized difference vegetation index to determine the extent of permafrost ecosystem disturbance on wetlands adjacent to thermo-erosion gullies. The analysis of a GeoEye-1 image of the Qarlikturvik valley, yielding a classification with five classes and 62% accuracy, resulted in directly identifying affected areas when compared to undisturbed baseline of wet and mesic plant communities. The total wetland area lost by drainage around the three studied gullies approximated to 95 430 m2, which already represents 0.5% of the total wetland area of the valley. This is worrisome considering that 36 gullies have been documented in a single valley since 1999 and that permafrost degradation by thermal erosion gullying is significantly altering landscape morphology, modifying wetland hydrology, and generating new fluxes of nutrients, sediments, and carbon in the watershed. This study demonstrates that remote sensing provides an effective means for monitoring spatially and temporally the impact of permafrost disturbance on Arctic wetland stability.

The “resource availability hypothesis” predicts occurrence of larger rodents in more productive habitats. This prediction was tested in a dataset of 1,301 rodent species. We used adult body mass as a measure of body size and normalized difference vegetation index (NDVI) as a measure of habitat productivity. We utilized a cross-species approach to investigate the association between these variables. This was done at both the order level (Rodentia) and at narrower taxonomic scales. We applied phylogenetic generalized least squares (PGLS) to correct for phylogenetic relationships. The relationship between body mas and NDVI was also investigated across rodent assemblages. We controlled for spatial autocorrelation using generalized least squares (GLS) analysis. The cross-species approach found extremely low support for the resource availability hypothesis. This was reflected by a weak positive association between body mass and NDVI at the order level. We find a positive association in only a minority of rodent subtaxa. The best fit GLS model detected no significant association between body mass and NDVI across assemblages. Thus, our results do not support the view that resource availability plays a major role in explaining geographic variation in rodent body size.

To conserve small and fragmented populations, we need an understanding of their population dynamics. With a global population estimate of < 120,000, Bicknell's Thrush (Catharus bicknelli) is considered one of the Nearctic-Neotropical migrants at greatest risk of extinction. This range-restricted songbird breeds in high-elevation fir (Abies balsamea) forests of the northeastern United States and eastern Canada, and primarily overwinters in forests of the Dominican Republic. The Conservation Action Plan for Bicknell's Thrush identifies numerous actions that may help stem population declines and promote recovery of the species, yet the empirical data needed to prioritize among these actions are lacking. We fit Cormack-Jolly-Seber models with mark-recapture data to test a series of hypotheses about the factors that limit apparent survival in 178 adult Bicknell''s Thrush (50 females and 128 males) captured on the breeding grounds in Vermont (June-July, 2001-2015). We focused on putatively important factors from throughout their annual cycle: cyclical population dynamics of nest predators, and weather effects on food abundance on the breeding and wintering grounds. Apparent survival of Bicknell's Thrush was relatively stable (mean Φ = 0.61, 95% CI: 0.54, 0.68) over our 15-year study, and most strongly associated with fir mast production. Apparent survival was higher following years during which fir trees produced large mast crops (mean Φ = 0.67, 95% CI: 0.55, 0.79), compared to following nonmast years (Φ = 0.56 ± 0.06, 95% CI: 0.43, 0.68). These results are likely driven by the reduced red squirrel density and increased nesting success and site fidelity of adult thrushes following nonmast years. Apparent survival of Bicknell's Thrush was also associated with relatively wet conditions on the wintering grounds in Hispaniola as assessed via the El Niño-Southern Oscillation (ENSO) precipitation index (ESPI). These relatively wet December-March periods are likely linked to greater primary productivity and the local availability of fruits and arthropods consumed by Bicknell's Thrush. Our research provides the most comprehensive examination of potentially limiting factors on Bicknell's Thrush populations to date and suggests future avenues of research exploring the relationship between food availability, survival, and climate change induced reductions in rainfall for the Greater Antilles.

Land surface temperature (LST) is an important parameter for the biosphere, cryosphere, and climate change studies. In this study, we estimate LST, NDMI, and NDWI over the Ravi basin, India, and parts of Pakistan, using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data. The study develops an ERDAS IMAGINE image processing method by using the LANDSAT 8 band 3(Green), band 4(Red), band 5(NIR), band 6(SWIR 1), and band 10 (TIR) data for determining the various spectral indices. The LST results show that most of the areas experienced extreme anomalies ranging from −350Cand 36 ​°C. The normalized difference moisture index (NDMI) value ranges from 0.685 to - 0.154. The normalized difference water index (NDWI) value ranges from 0.146 to - 0.444. Further, the LST result validated with in situ temperature observations at six locations in the study area, providing excellent correlation.

Current satellite broadband emissivity (BBE) products do not correctly characterize the seasonal variation of vegetation abundance. This paper proposes a new method to estimate the BBE of vegetated surfaces to better describe the seasonal variation of vegetation abundance. The method takes advantage of the radiative transfer models' ability to calculate multiple scattering with a physical basis and uses the 4SAIL model to construct a lookup table (LUT) of BBE for vegetated surfaces. The BBE of the vegetated surface was derived from the LUT using three inputs: leaf BBE, soil BBE, and leaf area index (LAI). The validation results show that the accuracy of the new method exceeds 0.005 over fully vegetated surfaces. As a case study, this method was applied to data from 2003 to generate global vegetated surface BBE products for that year. An analysis of the results indicated that the derived BBE can correctly reflect seasonal variations in vegetation abundance that the data converted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS spectral emissivity products have been unable to reveal. The new method was also compared to the vegetation cover method (VCM). The VCM can correctly characterize seasonal variations in vegetation abundance. However, the classification of bare soil and vegetation in the VCM may produce step discontinuity in the calculated BBE. The new method is being implemented to produce a new version of the Global LAnd Surface Satellite (GLASS) BBE product over vegetated surfaces.

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.

The Three Gorges Reservoir (TGR) intervening basin is one of the most important, ecologically fragile and sensitive areas in the upper reaches of the Yangtze River. Since the completion and operation of the TGR, the change of the ecological environment in this region&mdash, with vegetation as an indicator&mdash, has been a consistent focus of attention. Based on the six phases of land use data and normalized difference vegetation index (NDVI), temperature and precipitation data from 1998 to 2017, the change and trend of land use and vegetation cover in the TGR intervening basin were analyzed quantitatively by using a transition matrix, linear regression and partial correlation analysis. The area of unchanged land use type is 56,565 km2, accounting for 97.27% of the total area of the basin. The vegetation coverage with NDVI as the indicator showed a significant upward trend, with a growth rate of 7.5%/10a. The impact of temperature on vegetation was greater than that of precipitation on vegetation. The non-linear fitting curve of NDVI to temperature and precipitation rose with the time course of TGR impoundment, although the mechanism remains to be studied further. In general, climate change, ecological restoration measures, urbanization and reservoir impoundment did not significantly change the spatial distribution pattern of land use and the climate driving mechanism of vegetation growth in the TGR intervening basin.

In recent decades, climate change has affected vegetation growth in terrestrial ecosystems. We investigated spatial and temporal patterns of vegetation cover on the Loess Plateau’s Shaanxi-Gansu-Ningxia region in central China using MODIS-NDVI data for 2000–2014. We examined the roles of regional climate change and human activities in vegetation restoration, particularly from 1999 when conversion of sloping farmland to forestland or grassland began under the national Grain-for-Green program. Our results indicated a general upward trend in average NDVI values in the study area. The region’s annual growth rate greatly exceeded those of the Three-North Shelter Forest, the upper reaches of the Yellow River, the Qinling–Daba Mountains, and the Three-River Headwater region. The green vegetation zone has been annually extending from the southeast toward the northwest, with about 97.4% of the region evidencing an upward trend in vegetation cover. The NDVI trend and fluctuation characteristics indicate the occurrence of vegetation restoration in the study region, with gradual vegetation stabilization associated with 15 years of ecological engineering projects. Under favorable climatic conditions, increasing local vegetation cover is primarily attributable to ecosystem reconstruction projects. However, our findings indicate a growing risk of vegetation degradation in the northern part of Shaanxi Province as a result of energy production facilities and chemical industry infrastructure, and increasing exploitation of mineral resources.

The provenance of observations from a Sensor Web enabled remote sensing application represents a great challenge. There are currently no representations or tracking methods. We propose a provenance method that represents and tracks remote sensing observations in the Sensor Web enabled environment. The representation can be divided into the description model, encoding method, and service implementation. The description model uses a tuple to define four objects (sensor, data, processing, and service) and their relationships at a time point or interval. The encoding method incorporates the description into the Observations &amp, Measurements specification of the Sensor Web. The service implementation addresses the effects of the encoding method on the implementation of Sensor Web services. The tracking method abstracts a common provenance algorithm and four algorithms that track the four objects (sensor, data, processing, and service) in a remote sensing observation application based on the representation. We conducted an experiment on the representation and tracking of provenance information for vegetation condition products, such as the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI). Our experiments used raw Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce daily NDVI, weekly NDVI, and weekly VCI for the 48 contiguous states of the United States, for May from 2000 to 2012. We also implemented inverse tracking. We evaluated the time and space requirements of the proposed method in this scenario. Our results show that this technique provides a solution for determining provenance information in remote sensing observations.

In this paper, five national meteorological stations in Anhui province are taken as typical examples to explore the effects of local urbanization on their thermal environment by using Landsat data from 1990 to 2010. Satellite-based land use/land cover (LULC), land surface temperature (LST), normalized difference vegetation index (NDVI) are used to investigate the effects. The study shows that LULC around meteorological stations changed significantly due to urban expansion. Fast urbanization is the main factor that affects the spatial-temporal distribution of thermal environment around meteorological stations. Moreover, the normalized LST and NDVI exhibit strong inverse correlations around meteorological stations, so the variability of LST can be monitored through evaluating the variability of NDVI. In addition, station-relocation plays an important role in improving representativeness of thermal environment. Notably, the environment representativeness was improved, but when using the data from the station to study climate change, the relocation-induced inhomogeneous data should be considered and adjusted. Consequently, controlling the scale and layout of the urban buildings and constructions around meteorological stations is an effective method to ameliorate observational thermal environment and to improve regional representativeness of station observation. The present work provides observational evidences that high resolution Landsat images can be used to evaluate the thermal environment of meteorological stations.

The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

Se evaluó la relación de los índices de vegetación de diferencia normalizada (IVDN) y de agua de diferencia normalizada (IADN) con la temperatura de la superficie terrestre (TST), por medio de la utilización de imágenes Landsat de la cuenca del río La Vieja, en la región Andina colombiana. Se evaluaron las coberturas de selva Andina, plantación forestal, café, pasto y zona urbana. Se identificaron correlaciones negativas entre los índices y la TST, y se encontraron diferencias significativas (p

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation. Keywords: Land surface temperature (LST), Normalized difference vegetation index (NDVI), Mono window, Split window algorithm, Spectral radiance model