Assessing long-term rainfall trends and changes in a tropical watershed Brantas, Indonesia: an approach for quantifying the agreement among satellite-based rainfall data, ground rainfall data, and small-scale farmers questionnaires

The agreement between meteorological data and societal perception is essential in supporting a robust policy making and its implementation. In humid tropic watersheds like Brantas, such consensus is important for water resources management and policies. This study exemplifies an effort to understand the long-term rainfall characteristics within the watershed and to build a common link among the differing data sources: CHIRPS rainfall satellite data, rain gauge data, and farmers perceptions. Six rainfall characteristics were derived using statistical measures from the scientific data and then were translated to a series of structured questionnaires given to small-scale farmers. A consensus matrix was built to examine the level of agreement among three data sources, supporting the spatial pattern of the meteorological data and farmers perception. Two rainfall attributes were classified with high agreement, four with moderate and one with low agreement. The agreements and discrepancies of rainfall characteristics were found in the study area. The discrepancies originated from the accuracy in translating scientific measurements to practical meanings for farmers, complexity of the farming system, the nature of phenomena in questions, and farmers' ability to record long-term climatic events. This study shows an implication that a combined approach to link scientific data and societal data is needed to support powerful climate policy making.

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO₂, CO, and O₃ in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO₂ gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O₃ gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.

ArcGIS story maps in improving teachers’ Geography awareness

The purpose of this study is to examine the use of story maps in increasing sustainable Geography awareness among Geography teachers. The advent of story maps has altered the current Geography education in the digital era. ArcGIS story maps are a type of user-friendly geospatial technology renewal. This story map is believed capable of helping students learn Geography more independently, transforming Geography education. This belief should be reinforced by implementing story maps on their own Geography teachers, who have low Geography literacy rates in general. This action research involved 67 Geography teachers who were members of the East Java Geography Teacher Working Group, with various backgrounds, ages, and teaching experiences. Learning is implemented using blended learning and the in-on-in model. With blended project-based learning, this research was conducted to solve problems related to high school teachers' low Geography awareness. To identify the effects of the treatment, the obtained data were analyzed using a different test with paired t-test. The findings showed that story maps could increase long-term geographic awareness, illustrated by the obtained significant level of more than 0.05. This success is influenced by teachers' knowledge and experience with geospatial technology, as well as their age. Although the ability to create story projects is limited, the use of story maps provides a meaningful experience for teachers to think, reason, and act geographically.

Spatio-Temporal Dynamics of Inherent Optical Properties in Oligotrophic Northern Gulf of Mexico Estuaries

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3's Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m^-1 (13.95% NRMSE) for total absorption (a _t ), 0.21 m^-1 (7.61% NRMSE) for detritus-gelbstoff absorption (a _dg ), and 0.09 m^-1 (22.77% NRMSE) for phytoplankton pigment absorption (a_phi). Results showed that absorption by detritus and gelbstoff (a_dg) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.