Climatic variables are more effective on the spatial distribution of oak forests than land use change across their historical range.

The current research is conducted to model the effect of climate change and land use change (LUC) on the geographical distribution of Quercus brantii Lindl. (QB) forests across their historical range. Forecasting was done based on six general circulation models under RCP 2.6 and RCP 8.5 future climate change scenarios for the future years 2050 and 2070. In order to model the species distribution, different modeling methods were used. The results indicated that, in general, climatic variables had a higher influence on the distribution of QB than land use-related attributes. The mean diurnal range (bio2), the precipitation seasonality (bio15), and the mean temperature of the driest quarter (bio9) were the main predictors in the distribution of QB forests, while land use variables were less important in oak species distribution. The GBM, MaxEnt, and RF had higher accuracy and performance in modeling species distribution. The outputs also showed that in the current climate circumstances, 97,608.81 km2 of the studied area has high desirability for the presence of QB, and by 2070, under the pessimistic scenario, 96.29% of these habitats will be lost under the concomitant effect of LUC and climate change. By using the results of this research, it is possible to predict and identify the effective factors in changing the habitat of this oak species with more certainty. Based on the insights obtained from the results of such studies, the protection and restoration planning of the habitat of this key species, which supports diverse species, will be provided more efficiently.

Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems.

Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.

Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data.

Land use/land cover (LULC) change is an important indicator used for assessing the function and health of ecosystems. Understanding the patterns of LULC change assists in managing natural resources effectively, especially for regions where there are minimal or no reported data on the status of LULC. In this study, remotely sensed Landsat satellite imagery from 5 years (i.e., 1988, 1996, 2002, 2008, and 2017), geographic information systems (GIS), and the hybrid cellular automata (CA)-Markov model were used to (i) quantify the past and present LULC changes and (ii) model the future changes in Sulaimani Province in the Kurdistan region of Iraq (KRI). To accomplish these objectives, five LULC maps with various class categories were generated using the maximum likelihood classifier (MCL). The classified maps for 1996, 2002, 2008, and 2017 were used in the hybrid model to simulate LULC maps for 2017 and 2037. The map simulated for 2017 was validated with the classified 2017 LULC map. The change analysis demonstrated that between 1988 and 2017, the built-up areas and agricultural fallow land increased by 419% and 226%, respectively. In the future predictions for 2037, built-up areas and agricultural fallow land showed increasing trends of 5.5% and 26.5%, respectively. In contrast, agricultural land, plantation land, and sparse vegetation areas were predicted to decrease by 29.4%, 65.8%, and 36.9%, respectively. In addition, in 2008, waterbodies shrank by 43.36% in comparison with their status in 1988, suggesting that 2008 was a severe drought year. These findings provide invaluable baseline information with which conservation biologists, agricultural engineers, urban planners, and decision makers can better manage natural resources and monitor environmental changes. Based on these results, sustainable development actions and an early warning system can be established.

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series.

Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.