IDENTIFICATION OF POTENTIAL SITES FOR HOOP PINE PLANTATIONS IN THE ATHERTON TABLELANDS, NORTH QUEENSLAND, USING GIS AND EXPERT KNOWLEDGE

Abstract: This study modeled the suitability of sites to establish hoop pine plantations in the Atherton Tablelands, North Queensland (NQ). The study was conducted to provide information regarding potential sites resulted from a broad level site assessment. Potential sites for hoop pine were identified using GIS which the criteria were derived from literature search and expert opinion which then were used to construct suitability criteria. Mean annual rainfall and soil types were used to assess the ecological suitability for hoop pine growth. These suitabilitycriteria were then combined with availability criteria for determining possible expansions of hoop pine plantations on private lands, which comprise the land size, land status, land cover, land use and slope limit. The model was then validated using hoop pine site index records as a surrogate for hoop pine potential growth. From the results, the region was found to be edaphically and climatically suitable encompassing around 35,567 ha of land was identified as highly suitable and 4,680 ha as moderately suitable. It was also revealed that suitability classes derived from spatial modeling can only produce indicative locations of lands suitable for supporting hoop pine growth. While datasets came from various scales and precision, the results of the study have limited applicability for planning at individual farm but are useful to gain initial consideration at the regional level to target areas for plantation expansion.Keywords: Hoop pine, land suitability, land-availability, GISbased modelin

Path Analysis of the Effect of Biological and Social Factors on the Case of Breast Cancer

Breast cancer is the number one cancer as the cause of death in women in developed and developing countries. Breast cancer has the highest case in women in 161 countries. The objective of this study is to analyze the influence of biological and social factors on breast cancer cases in the Public Hospital of Kediri, East Java. The research design was analytic with a retrospective cohort approach. The research sample of 105 respondents used simple random sampling. Data collection was with medical records in January-December, 2017. Data analysis used the path analysis test. The test results obtained breast cancer is influenced by a history of hereditary breast cancer (b=0.17, p=0.001); menopause age (b=0.17, p=0.001); family planning history (b=0.11, p=0.014); parity (b=0.08, p=0.031); age (b=0.21, p=0.001); income (b=0.21, p=0.001). Parity was influenced by income (b=0.45, p <0.001). The age of menopause was influenced by a history of hereditary breast cancer (b= 0.31, p <0.001); family planning history (b=0.13, p=0.13); and age (b=0.10, p=0.01). It can be concluded that the case of breast cancer was directly influenced by hereditary cancer, family history of birth control, age, parity, menopausal age, and income. Also, breast cancer was indirectly affected by income through parity; and hereditary history of breast cancer, family planning history, and age through menopause.Kanker payudara adalah kanker nomor satu sebagai penyebab kematian oleh kanker pada seorang wanita di negara maju dan negara berkembang. Kanker payudara memiliki insiden yang tertinggi pada wanita di 161 negara. Tujuan penelitian ini adalah untuk menganalisis pengaruh faktor biologis dan sosial terhadap kejadian kanker payudara di RSUD Kabupaten Kediri, Jawa Timur. Desain penelitian yaitu analitik dengan pendekatan kohort retrospektif. Sampel penelitian sebanyak 105 responden menggunakan simple random sampling. Pengumpulan data dengan rekam medik pada bulan Januari-Desember 2017. Analisa data menggunakan uji path analysis. Hasil uji didapatkan kanker payudara dipengaruhi oleh riwayat keturunan kanker payudara (b=0.17, p=0.001); usia menopause (b=0.17, p=0.001); riwayat KB (b=0.11, p=0.014); paritas (b=0.08, p=0.031); usia (b=0.21, p=0.001); pendapatan (b=0.21, p=0.001). Paritas dipengaruhi oleh pendapatan  (b= 0.45, p < 0.001). Usia menopause dipengaruhi oleh riwayat keturunan kanker payudara (b= 0.31, p < 0.001); riwayat keluarga berencana (KB) (b= 0.13, p=0.13); dan usia (b= 0.10, p = 0.01). Dapat disimpulkan bahwa kejadian kanker payudara dipengaruhi secara langsung oleh keturunan kanker, riwayat KB, usia, paritas, usia menopause dan pendapatan. Selain itu, kanker payudara juga dipengaruhi secara tidak langsung oleh pendapatan melalui paritas; serta riwayat keturunan kanker payudara, riwayat KB, dan usia melalui usia menopause

Assessment of Urban Mapping Index Accuracy in Relation to Physical Land Characteristics in Humid Tropical Areas

Settlements and built-up areas can lead to the degradation of ecological systems. Good quality and efficient regional planning is therefore needed for urban areas. Spatial data and satellite imagery can be used for mapping and monitoring urban growth. Unfortunately, mapping urban areas can sometimes be difficult due to local variations, and different algorithms can provide varying results. Urban indices often rely on remote sensing reflectance, the accuracy of which can be influenced by land characteristics. No studies have examined the impact of land characteristics on the accuracy of remote sensing urban indices in the humid tropics. The purpose of this study was to compare urban and built area indices, namely EBBI, NDBI, UI, and IBI, in two climatically and topographically different cities. This study also examined the stability and relationship between these indices with environmental factors such as slope, elevation, and temperature. The results showed that EBBI was the index with the highest accuracy in both study areas: 85% for Batu City and 89.17% for Pasuruan City. Also, EBBI was the most stable index for the temporal studies. Environmental factors, especially slope and elevation, had a strong relationship with the index value applied. Therefore, these findings need to be considered in applying the index in areas that have topographical variations. Keywords: built-up land, landsat, EBBI, NDBI, UI, IBI, topograph

Drought Indices to Map Forest Fire Risks in Topographically Complex Mountain Landscapes

Drought has the potential to lead to forest fires. Forest fires generally occur during the dry season when the mountain slope forest experiences a water deficit. Drought identification based on remote sensing is useful for mapping potential fires in Arjuno- Welirang Forest and TNBTS Forest (in Bromo Tengger Semeru National Park). This research used Landsat-8 images in 118/065 and 118/066 in August and November 2015-2018. Validation data were obtained using high resolution planet scope images and rainfall data. Three drought indices were tested to identify fires, namely TVDI, VHI and NDDI. The indices were tested visually using high resolution images and tested meteorologically using SPI. From the results of the accuracy test and correlation, TVDI had the highest accuracy in the Arjuno-Welirang forest (96% accurate), while the best index for TNBTS was the VHI index (96% accurate). Keywords: drought indices, TVDI, VHI, NDDI, forest fires, Indonesi

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

Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review

Rubber (Hevea brasiliensis) is a tropical tree crop cultivated for the industrial production of latex. The trees are tall, perennial and long-lived, and are typically grown in plantations. In most rubber-producing countries, smallholders account for more than 85% of plantation area. Traditional practices mean that it can be difficult to monitor rubber plantations for management purposes. To overcome issues associated with monitoring traditional practices, remote sensing approaches have been successfully applied in this field. However, information on this is lacking. Therefore, this study aims to document the current status, history, development and prospects for remote sensing applications in rubber plantations by using the PRISMA framework. The review focuses on the application of optical remote sensing data in rubber. In this paper, we discuss the current role of remote sensing on specific subject areas, namely mapping, change detection, stand age estimation, carbon and biomass assessment, leaf area index (LAI) prediction and disease detection. In addition, we elaborate on the benefits gained and challenges faced while adapting this technology. These include the availability and free access to satellite imagery as the greatest benefit and the presence of clouds as one of the toughest challenges. Finally, we highlighted four potential areas where future work can be done: (1) Advancements in remote sensing data, (2) algorithm enhancements, (3) emerging processing platforms, and (4) application to less studied subject areas. This paper gives insight into strengthening the potential of remote sensing for delivering efficient and long-term services for rubber plantations

Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling

A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates of monthly flow data and water balance components, namely runoff, groundwater, and water yield. GPM has a better ability for rainfall event detections, especially during high rainfall events or extremes (>40 mm/day). The errors of the satellite products are generally linked to slope, wind, elevation, and evapotranspiration. Hydrologic simulations using SWAT modelling and the three satellite rainfall products show that CHIRPS slightly has the daily best performance, with R2 of 0.59 and 0.62, and NSE = 0.54, and the monthly aggregated improved at a monthly level. The water balance components generated at an annual level, using three satellite products, show that CHIRPS outperformed with a ration closer to one, though with a tendency to overestimate up to 3–4× times the data generated from the rainfall gauges. The findings of this study are beneficial in supporting efforts for improving satellite rainfall products and water resource implications

Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling

A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates of monthly flow data and water balance components, namely runoff, groundwater, and water yield. GPM has a better ability for rainfall event detections, especially during high rainfall events or extremes (>40 mm/day). The errors of the satellite products are generally linked to slope, wind, elevation, and evapotranspiration. Hydrologic simulations using SWAT modelling and the three satellite rainfall products show that CHIRPS slightly has the daily best performance, with R2 of 0.59 and 0.62, and NSE = 0.54, and the monthly aggregated improved at a monthly level. The water balance components generated at an annual level, using three satellite products, show that CHIRPS outperformed with a ration closer to one, though with a tendency to overestimate up to 3–4× times the data generated from the rainfall gauges. The findings of this study are beneficial in supporting efforts for improving satellite rainfall products and water resource implications

Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses

Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses