Journal articles on the topic 'Rangelands – Alberta, Southern – Remote sensing'

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

Warming, i.e., increments of temperature, is evident at the global, regional, and local level. However, understanding the dynamics of local warming at high spatial resolution remains challenging. In fact, it is very common to see extremely variable land cover/land use within built-up environments that create micro-climatic conditions. To address this issue, our overall goal was to generate a local warming map for the period 1961–2010 at 15 m spatial resolution over the southern part of the Canadian province of Alberta. Our proposed methods consisted of three distinct steps. These were the: (i) construction of high spatial resolution enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) maps; (ii) conversion of air temperature (Ta) normal (i.e., 30 years average) at higher spatial resolution using vegetation indices (VI); and (iii) generation of a local warming map at 15m spatial resolution. In order to execute this study, we employed MODIS-driven air temperature data, EVI and NDVI data, and Landsat-driven vegetation indices. The study uncovered that around 58% (up to positive 1 °C) of areas in the considered study region were experiencing increased temperature; whereas only about 4% of areas underwent a cooling trend (more than negative 0.25 °C). The remaining 38% did not exhibit significant change in temperature. We concluded that remote sensing technology could be useful to enhance the spatial resolution of local warming maps, which would be useful for decision-makers considering efficient decisions in the face of increments in local temperature.

Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.

Many birds of Australia's arid and monsoonal regions are characterized by dispersive or nomadic movements and large population fluctuations in response to variable climatic conditions. These characteristics, compounded by our generally limited knowledge of bird movements and population dynamics (in part due to limited research effort in these sparsely-populated rangelands), complicate population monitoring and conservation for such species. Here we employ mail surveys of landholders across continental Australia to assess the distribution and movement patterns of the Australian Bustard Ardeotis australis. We combine data from these simple mail surveys with more sophisticated techniques that allow for tracking the flux in rainfall patterns and vegetation greenness over broad spatial and temporal scales, to identify and describe the responses of bustards to seasonai and climatic variability. Our results demonstrate that residency patterns of bustards vary widely across Australia. The seasonality of bustard occurrence is generally more pronounced in regions characterized by predictable seasonal conditions. Seasonal patterns are also evident in more climatically unpredictable regions, although here they may be increasingly overlaid by more idiosyncratic movements as a result of longer term variation in rainfall and associated patterns of primary productivity. We found limited evidence that bustards respond to inter-regional irregularities in rainfall events, suggesting that nomadic movements are generally not continental, but rather intra-regional. However, longer term data sets that cover several more, or more extreme, climatic fluctuations than that considered here, are needed to assess these relationships adequately. To a lesser degree and differently between regions, respondents reported that bustards are also associated with fire, grasshopper outbreaks, crop agriculture and drought. They are most abundant across the savannahs of northern Australia extending to parts of the Pilbara and recently cleared regions of the Brigalow Belt in eastern Queensland. In southern Australia, bustards are perceived as shor-term, irregular visitors whereas more permanent populations persist in northern and northeastern regions. While there are inherent limitations to such data, the study illustrates the utility of incorporating rangeland users into the types of large-scale monitoring programmes required to assess the distribution and movement patterns of highly mobile birds or species characterized by large population fluctuations.

To understand the health effects of wildfire smoke, it is important to accurately assess smoke exposure over space and time. Particulate matter (PM) is a predominant pollutant in wildfire smoke. In this study, we develop land-use regression (LUR) models to investigate the impact that a cluster of wildfires in the northwest USA had on the level of PM in southern Alberta (Canada), in the summer of 2015. Univariate aerosol optical depth (AOD) and multivariate AOD-LUR models were used to estimate the level of PM2.5 in urban and rural areas. For epidemiological studies, it is also important to distinguish between wildfire-related PM2.5 and PM2.5 originating from other sources. We therefore subdivided the study period into three sub-periods: (1) Pre-fire, (2) during-fire, and (3) post-fire. We then developed separate models for each sub-period. With this approach, we were able to identify different predictors significantly associated with smoke-related PM2.5 verses PM2.5 of different origin. Leave-one-out cross-validation (LOOCV) was used to evaluate the models’ performance. Our results indicate that model predictors and model performance are highly related to the level of PM2.5, and the pollution source. The predictive ability of both uni- and multi-variate models were higher in the during-fire period than in the pre- and post-fire periods.

Rangelands which consist of grasslands, shrublands and savannahs are used by wildlife for habitat and are the main source of forage for livestock. The assessment and monitoring of rangeland condition is one of the most important factors for rangeland scientists in order to calculate the carrying capacity of livestock with consideration for coexisting wildlife. This study assessed the potential of Landsat 8 multispectral bands and broadband vegetation indices to model woody vegetation parameters such as tree equivalents (TE) and total leaf mass (LMASS) for the Thanda Private Game Reserve using partial least squares regression (PLSR). The PLSR model predicted TE with an R<sup>2</sup> value of 0.76 and a root mean square error (RMSE) of 1411 TE/ha using an independent test dataset. LMASS was predicted with an R<sup>2</sup> value of 0.67 and a RMSE of 853 kg/ha on an independent test dataset. The predictive models were then inverted to map TE and LMASS over the study area. The modelled TE and LMASS layers were integrated with conventional grazing and browse capacity models to map carrying capacity for the Game Reserve. The study indicates the potential of Landsat 8 multispectral data in carrying capacity modelling. The result is significant for rangeland monitoring in Southern Africa using remote sensing technologies.

Drought severity and impact assessments are necessary to effectively monitor droughts in semi-arid contexts. However, little is known about the influence land use-land cover (LULC) has—in terms of the differences in annual sizes and configurations—on drought effects. Coupling remote sensing and Geographic Information System techniques, drought evolution was assessed and mapped. During the growing season, drought severity and the effects on LULC were examined and whether these differed between areas of land change and persistence. This study used areas of economic importance to Botswana as case studies. Vegetation Condition Index, derived from Normalised Difference Vegetation Index time series for the growing seasons (2000–2018 in comparison to 2020–2021), was used to assess droughts for 17 constituencies (Botswana’s fourth administrative level) in the Central District of Botswana. Further analyses by LULC types and land change highlighted the vulnerability of both human and natural systems to drought. Identified drought periods in the time series correspond to declared drought years by the Botswana government. Drought severity (extreme, severe, moderate and mild) and the percentage of land areas affected varied in both space and time. The growing seasons of 2002–2003, 2003–2004 and 2015–2016 were the most drought-stricken in the entire time series, coinciding with the El Niño southern oscillation (ENSO). The lower-than-normal vegetation productivity during these growing seasons was evident from the analysis. With the above-normal vegetation productivity in the ongoing season (2020–2021), the results suggest the reversal of the negative vegetation trends observed in the preceding growing seasons. However, the extent of this reversal cannot be confidently ascertained with the season still ongoing. Relating drought severity and intensities to LULC and change in selected drought years revealed that most lands affected by extreme and severe drought (in descending order) were in tree-covered areas (forests and woodlands), grassland/rangelands and croplands. These LULC types were the most affected as extreme drought intersected vegetation productivity decline. The most impacted constituencies according to drought severity and the number of drought events were Mahalapye west (eight), Mahalapye east (seven) and Boteti west (seven). Other constituencies experienced between six and two drought events of varying durations throughout the time series. Since not all constituencies were affected similarly during declared droughts, studies such as this contribute to devising appropriate context-specific responses aimed at minimising drought impacts on social-ecological systems. The methodology utilised can apply to other drylands where climatic and socioeconomic contexts are similar to those of Botswana.

Climate change induced sea-level rise (SLR) is on its increase globally. Regionally the lowlands of China, Vietnam, Bangladesh, and islands of the Malaysian, Indonesian and Philippine archipelagos are among the world’s most threatened regions. Sea-level rise has major impacts on the ecosystems and society. It threatens coastal populations, economic activities, and fragile ecosystems as mangroves, coastal salt-marches and wetlands. This paper provides a summary of the current state of knowledge of sea level-rise and its effects on both human and natural ecosystems. The focus is on coastal urban areas and low lying deltas in South-East Asia and Vietnam, as one of the most threatened areas in the world. About 3 mm per year reflects the growing consensus on the average SLR worldwide. The trend speeds up during recent decades. The figures are subject to local, temporal and methodological variation. In Vietnam the average values of 3.3 mm per year during the 1993-2014 period are above the worldwide average. Although a basic conceptual understanding exists that the increasing global frequency of the strongest tropical cyclones is related with the increasing temperature and SLR, this relationship is insufficiently understood. Moreover the precise, complex environmental, economic, social, and health impacts are currently unclear. SLR, storms and changing precipitation patterns increase flood risks, in particular in urban areas. Part of the current scientific debate is on how urban agglomeration can be made more resilient to flood risks. Where originally mainly technical interventions dominated this discussion, it becomes increasingly clear that proactive special planning, flood defense, flood risk mitigation, flood preparation, and flood recovery are important, but costly instruments. 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Abstract Background The gradual conversion of rangelands into other land use types is one of the main challenges affecting the sustainable management of rangelands in Teltele. This study aimed to examine the changes, drivers, trends in land use and land cover (LULC), to determine the link between the Normalized Difference Vegetation Index (NDVI) and forage biomass and the associated impacts of forage biomass production dynamics on the Teltele rangelands in Southern Ethiopia. A Combination of remote sensing data, field interviews, discussion and observations data were used to examine the dynamics of LULC between 1992 and 2019 and forage biomass production. Results The result indicate that there is a marked increase in farm land (35.3%), bare land (13.8%) and shrub land (4.8%), while the reduction found in grass land (54.5%), wet land (69.3%) and forest land (10.5%). The larger change in land observed in both grassland and wetland part was observed during the period from 1995–2000 and 2015–2019, this is due to climate change impact (El-Niño) happened in Teltele rangeland during the year 1999 and 2016 respectively. The quantity of forage in different land use/cover types, grass land had the highest average amount of forage biomass of 2092.3 kg/ha, followed by wetland with 1231 kg/ha, forest land with 1191.3 kg/ha, shrub land with 180 kg/ha, agricultural land with 139.5 kg/ha and bare land with 58.1 kg/ha. Conclusions The significant linkage observed between NDVI and LULC change types (when a high NDVI value, the LULC changes also shows positive value or an increasing trend). In addition, NDVI value directly related to the greenness status of vegetation occurred on each LULC change types and its value directly linkage forage biomass production pattern with grassland land use types. 64.8% (grass land), 43.3% (agricultural land), 75.1% (forest land), 50.6% (shrub land), 80.5% (bare land) and 75.5% (wet land) more or higher dry biomass production in the wet season compared to the dry season.