Long-term land cover changes play a significant driver of ecosystem and function of natural biodiversity. Hence, their analysis can be used for evaluating and supporting government plans, especially conservation and management of natural habitats such as sago palm. In Papua Province of Indonesia, sago palm has been stated as one of the priority plants in the Medium-Term Development Plan (R.P.J.M.). However, limited studies have examined this palm in one of the Regencies of Papua Province, namely, Merauke Regency. In this study, we performed remotely sensed data imagery and supervised classification to produce land cover maps from 1990 to 2019. During the study period, twenty-one land cover classes were identified. The six classes of the natural forest consist of primary dryland forest, secondary dryland forest, primary mangrove forest, secondary mangrove forest, primary swamp forest, and secondary swamp forest; thus, fifteen classes of non-forested area. Concerning the sago palm habitat, our study evaluated two different categories (1) based on the land cover scheme from the Ministry of Environment and Forestry and (2) according to the peatland land cover ecosystem in Papua. Based on paired samples t-test, the result indicated statistically significant changes specifically at primary dryland (p-value = 0.015), grassland (p-value = 0.002) and swamp (p-value = 0.007). Twelve from 20 districts of Merauke Regency tend to lose the forecasted natural habitat of the sago palm. Therefore, this study suggests the further need to recognize and estimate the yield of sago palm area in these various ecosystems.

Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now widely developed, it is rarely applied for detection and classification purposes, specifically in Indonesia. Considering the potential use of the plant, local farmers identify the harvest time by using human inspection, i.e., by identifying the bloom of the flower. Therefore, this study aims to detect sago palms based on their physical morphology from Unmanned Aerial Vehicle (UAV) RGB imagery. Specifically, this paper endeavors to apply the transfer learning approach using three deep pre-trained networks in sago palm tree detection, namely, SqueezeNet, AlexNet, and ResNet-50. The dataset was collected from nine different groups of plants based on the dominant physical features, i.e., leaves, flowers, fruits, and trunks by using a UAV. Typical classes of plants are randomly selected, like coconut and oil palm trees. As a result, the experiment shows that the ResNet-50 model becomes a preferred base model for sago palm classifiers, with a precision of 75%, 78%, and 83% for sago flowers (SF), sago leaves (SL), and sago trunk (ST), respectively. Generally, all of the models perform well for coconut trees, but they still tend to perform less effectively for sago palm and oil palm detection, which is explained by the similarity of the physical appearance of these two palms. Therefore, based our findings, we recommend improving the optimized parameters, thereby providing more varied sago datasets with the same substituted layers designed in this study.

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R² above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

Information technologies including information systems play an important role to support education systems specifically in remote area such as Papua. However, based on World’s most literate Nations (WMLN) study found that Indonesia is the second-least literate nation in the World. This study focuses on the empowering of open source android software to facilitate students or teachers to borrow, share or exchange books. The system consists of two modules, i.e. the Backend as a Service (BaaS) is obtained through firebase web service, and client android system. The findings may be helpful for both borrowers and owners using this application in selecting, borrowing, reviewing, applying, adding book profile, and viewing. To end this, the application also able in displaying maps and routes, private messages as well as notification.