Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Leopard subspecies conservation under climate and land-use change

Predicting the effects of global environmental changes on species distribution is a top conservation priority, particularly for large carnivores, that contribute to regulating and maintaining ecosystems. As the most widespread and adaptable large felid, ranging across Africa and Asia, leopards are crucial to many ecosystems as both keystone and umbrella species, yet they are threatened across their ranges. We used intraspecific species distribution models (SDMs) to predict changes in range suitability for leopards under future climate and land-use change and identify conservation gaps and opportunities. We generated intraspecific SDMs for the three western leopard subspecies, the African, Panthera pardus pardus; Arabian, Panthera pardus nimr; and Persian, Panthera pardus tulliana, leopards, and overlapped predictions with protected areas (PAs) coverage. We show that leopard subspecies differ in their environmental associations and vulnerability to future changes. The African and Arabian leopards are predicted to lose ~25% and ~14% of their currently suitable range, respectively, while the Persian leopard is predicted to experience ~12% range gains. We found that most areas predicted to be suitable were not protected, with only 4%-16% of the subspecies' ranges falling inside PAs, and that these proportions will decrease in the future. The highly variable responses we found between leopard subspecies highlight the importance of considering intraspecific variation when modelling vulnerability to climate and land-use changes. The predicted decrease in proportion of suitable ranges falling inside PAs threatens global capacity to effectively conserve leopards because survival rates are substantially lower outside PAs due to persecution. Hence, it is important to work with local communities to address negative human-wildlife interactions and to restore habitats to retain landscape connectivity where PA coverage is low. On the other hand, the predicted increase in range suitability across southern Europe presents opportunities for expansion outside of their contemporary range, capitalising on European rewilding schemes.

Spatio-temporal variability of vegetation and its relation to different hydroclimatic factors in Bangladesh

Bangladesh, known for its remarkable ecological diversity, is faced with the pressing challenges of contemporary climate change. It is crucial to understand how vegetation dynamics respond to different climatic factors. Hence, this study aimed to investigate the spatio-temporal variations of vegetation and their interconnectedness with a range of hydroclimatic factors. The majority of the dataset used in this study relies on MODIS satellite imagery. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), precipitation (PPT), evapotranspiration (ET), and land surface temperature (LST) data from the years 2001 to 2020 have been obtained from Google Earth Engine (GEE). In this study, the temporal variations of the NDVI, EVI, PPT, ET, and LST have been investigated. The findings of the Mann-Kendall trend test indicate noticeable trends in both the NDVI and the EVI. Sen's slope value for NDVI and EVI is 0.00424/year and 0.00256/year, respectively. Compared to NDVI, EVI has shown a stronger connection with hydroclimatic factors. In particular, EVI exhibits a better relationship with ET, as indicated by a r2 value of 0.37 and a P-value of 6.81 × 10-26, whereas NDVI exhibits a r2 value of 0.17 and a P-value of 2.96 × 10-11. Furthermore, ET can explain 17% of the fluctuation in NDVI, and no correlation between NDVI and PPT has been found. The results clarify the significant relationship between the EVI and hydroclimatic factors and highlight the efficiency of the EVI for detecting vegetation changes.

Satellite monitoring of bio-fertilizer restoration in olive groves affected by Xylella fastidiosa subsp. pauca

Xylella fastidiosa subsp. pauca (Xfp), has attacked the olive trees in Southern Italy with severe impacts on the olive agro-ecosystem. To reduce both the Xfp cell concentration and the disease symptom, a bio-fertilizer restoration technique has been used. Our study applied multi-resolution satellite data to evaluate the effectiveness of such technique at both field and tree scale. For field scale, a time series of High Resolution (HR) Sentinel-2 images, acquired in the months of July and August from 2015 to 2020, was employed. First, four spectral indices from treated and untreated fields were compared. Then, their trends were correlated to meteo-events. For tree-scale, Very High Resolution (VHR) Pléiades images were selected at the closest dates of the Sentinel-2 data to investigate the response to treatments of each different cultivar. All indices from HR and VHR images were higher in treated fields than in those untreated. The analysis of VHR indices revealed that Oliarola Salentina can respond better to treatments than Leccino and Cellina cultivars. All findings were in agreement with in-field PCR results. Hence, HR data could be used to evaluate plant conditions at field level after treatments, while VHR imagery could be used to optimize treatment doses per cultivar.

Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers

The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall.

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.

Methane emissions only negligibly reduce the ecosystem service value of wetlands and rice paddies in the mature Ganges Delta

Wetland provides a wide range of ecosystem services with immense value. However, methane (CH₄) emissions adversely affect ecosystem services, and it requires fixation cost. The objective of the present study was to estimate CH₄ emissions and ecosystem services value (ESV) and how much the fixation cost of CH₄ reduces the ESV. Since rice cultivation is a very common practice here, the paddy fields were also incorporated in this study. CH₄ flux and satellite data were employed for estimating the emissions with the help of two-factor (temperature and water availability) model. Global coefficients of ecosystem service value (ESV) that is defined as the monetary valuation of materialistic and non-materialistic services were adapted for estimating the ecosystem service of the CH₄ emitting sources. Results show that during the boro season (pre-monsoon summer paddy cultivation season), average monthly emissions of paddy fields are equal to the wetlands which are 0.16 t/km². During amon season (monsoon paddy cultivation season), this emissions is 0.7 t/km² and 0.53 t/km², respectively, from wetlands and paddy fields. Both wetlands and paddy fields emit a greater amount of CH₄ during amon season than boro season. Behind this seasonal variation, water availability in terms of precipitation-evaporation ratio plays a more vital role than temperature. Total estimated ESV is 928.51 million US$, and CH₄ fixation cost is 6.64 million US$ which is only 0.71% to total ESV. So, considering such huge net ESV, emphasis on wetland conservation and restoration are necessary.

Natural and anthropogenic forcings lead to contrasting vegetation response in long-term vs. short-term timeframes

Understanding vegetation response to natural and anthropogenic forcings is vital for managing watersheds as natural ecosystems. We used a novel integrated framework to separate the impacts of natural factors (e.g. drought, precipitation and temperature) from those of anthropogenic factors (e.g. human activity) on vegetation cover change at the watershed scale. We also integrated several datasets including satellite remote sensing and in-situ measurements for a twenty-year time period (2000-2019). Our results show that despite no significant trend being observed in temperature and precipitation, vegetation indices expressed an increasing trend at both the control and treated watersheds. The vegetation cover was not significantly affected by the natural factors whereas the watershed management practice (as a human activity) had significant impacts on vegetation change in the long-term. Further, the vegetation cover long-term response to watershed management practice was mainly linear. We also found that the vegetation indices values in the 2011-2019 period (as the treated period in treated watershed) were significantly higher than those in the 2000-2010 period. In the short-term, however, the drought condition and decreased precipitation (as natural factors) explained the majority of the change in vegetation cover. For example, the majority of the breakpoints occurred in 2008, and it was related to a widespread extreme drought in the area. The watershed management practice as a human activity along with extreme climatic events could explain a large part of the vegetation changes observed in the treated and control watersheds.

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.

Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.