SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor)

Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality. Crop yield estimation is part of the management of tea plucking, affecting tea quality and quantity. The constraint in estimating crop yields requires technology that can make the process more effective and efficient. Remote sensing technology and machine learning have been widely used in precision agriculture. Recently, big data processing, especially remote sensing data, machine learning, and deep learning have been carried out using a cloud computing platform. Therefore, we propose using GeoAI, a combination of Sentinel-2A imagery, machine learning, and Google Collaboratory, to predict ready for plucking tea leaves at optimal plucking time at Gunung Mas Plantation Bogor. We used selected bands of Sentinel-2A and extracted more features (i.e., NDVI) as a training set. Then we utilized the tea blocks boundary and tea plucking data to generate labels using Random Forest (RF) and Support Vector Machine (SVM). The classification results were further used to estimate the production of crop tea yield. The RF classifier is able to achieve overall accuracy at 51% and SVM at 54%. Meanwhile, accuracy at optimally aged tea blocks is able to achieve at 75.62% for RF and 52.88% for SVM. Thus, the SVM classifier is better in terms of overall accuracy. Meanwhile, the RF classifier is superior in predicting ready for plucking tea at optimally aged tea blocks

TEA PLANT HEALTH RESEARCH USING SPECTROMETER

Tea leaves are the most important part for consumption. Leaves that are healthy have a distinct color, while leaves that are not healthy have a color that is very different from the original. Chlorophyll in leaves effects the reflection of infrared light, allowing healthy plants to reflect more infrared light than unhealthy plants. Leaf color and chlorophyll have an important role in showing the growth and health of tea plants. Remote sensing consists of collecting information about objects and features without contacting the equipment. The Normalized Difference Vegetation Index (NDVI), one of the first remote sensing analysis products used to simplify the complexity of multispectral imaging, is now the most commonly used index for botanical assessment. inconsistencies in NDVI depending on sensor-specific spatial and spectral resolutions. Different parts of the leaf have discolored spots due to health conditions or nutritional stress, so there are different spectral values on different parts of the leaf. Unhealthy tea leaves have low NIR values due to disease, insects, and sunburn, which damage the chloroplast structure of the leaves, weaken the absorption of the appropriate band, and increase reflectance. There is a difference between the measurement results of the NDVI spectrometer and the sentinel image. This is due to the fact that the Sentinel-2 image can only retrieve image pixels with a resolution and not diseased leaf parts, as with the use of a spectrometer, which directly extracts the value of the infected area from the normal part of the plan

Zone-Based Tourism Planning Using Satellite Imagery

Tourism planning serves as a strategic approach to mitigate and address the damage incurred by tourist attractions, such as the Tangkolak Bahari Center (TMC) mangrove ecosystem, which has experienced a loss of 2 hectares. The primary objective of this research is to formulate a zone-based tourism plan utilizing PlanetScope Dove-R sensor satellite imagery to provide spatial information specific to its application in December 2022. The methodology encompasses various techniques, including observation, structured interviews with tourists, focus group discussions involving tourism managers and local government representatives, digitization, and delineation. The result of research is Zones within the TMC tourist attractions, comprising Main and Supporting Space Plans, Primary and Secondary Circulation Plans, Avicennia, Rhizopora stylosa, and Sonneratia Conservation Vegetation Plans, as well as Plans for Nature, Conservation, Culinary Activities and Facilities, and Green Planning. Notably, the TMC tourist attraction remains viable, covering an area of 2.73 hectares in the West TMC and 1.79 hectares in the East TMC. It is imperative to underscore the importance of considering the sustainability of the mangrove ecosystem in utilizing these areas

Generating Evacuation Route for Tsunami Evacuation Based on Megathrust Scenario Hazard Model in Palabuhanratu Village, Sukabumi, West Java

Palabuhanratu Village is one of the villages in Sukabumi, West Java, that is susceptible to earthquake and tsunami risks. This research intends to revise the tsunami hazard map, undertake a spatial analysis of the distribution of evacuation sites, and identify optimal tsunami evacuation routes. The tsunami hazard map was updated using tsunami modeling with COMCOT based on the worst-case scenario of potential magnitude moment 8.8 for the Megathrust segment in the south of West Java from PuSGeN. This modeling was used to predict the worst probable tsunami impact. On the basis of field survey data regarding the location of evacuation sites, evaluation of the distribution of evacuation sites was conducted. In addition, service area analysis is utilized to assess the service area of the present evacuation site in relation to each hamlet in Palabuhanratu village. Approximately 57.33 percent of the town could be affected by a tsunami, according to the findings of this study. The greatest tsunami height along the coast is expected to be between 18 and 22 meters, and the arrival time is 22 minutes. From a total of 35 hamlets, we determined that two hamlets in the Palabuhanratu village area were not harmed by the tsunami. Because not everyone can reach the evacuation location in time, the findings of this study show the need for an additional vertical evacuation site

Spatial Dynamics Model of Land Availability and Level of Income and Education in Parangtritis Coastal Village, Bantul Regency

Parangtritis coastal village is located on the southern coast of Bantul Regency that popular with tourism and capture fisheries activities. The advantages of the tourism and capture fisheries sector make Parangtritis Village seen as a field to earn a living and causes healing in population or people income of Parangtritis Village. This situation can affect the need for space and land, which can have an impact on decreasing the carrying capacity of the environment so that predictions are needed on land availability using a model of spatial dynamics. This study aims to build a model of spatial dynamics for land availability and analyze the relationship among these models with the education level and income of Parangtritis Village. The methods that used in this study is a spatial dynamics modeling method which using population data for 2008-2018 and Google Earth imagery in 2008, 2013, and 2018, and interview with grid area used for the level of education and income. The development of the built area observed through a spatial dynamics model of the relationship between population growth and land availability in the period 2008-2100. The model prediction shows that the developed land has developed from the appropriate area to meet the regional capacity that is not appropriate in 2039. The analysis results showed that the fastest growth of the built-up area was in areas with high levels of education and high-income levels

Kerentanan Wilayah Terhadap Covid-19 di Kota Pariaman

Hampir seluruh wilayah di Indonesia terpapar virus Covid-19 termasuk Kota Pariaman. Kota Pariaman merupakan salah satu destinasi wisata seperti pariwisata pantai sehingga banyak didatangi wisatawan saat hari libur sehingga menyebabkan tingginya tingkat interaksi manusia dan kontak langsung antara manusia terjadi secara intensif. Penelitian ini bertujuan untuk memprediksi kerentanan wilayah terhadap Covid-19 di Kota Pariaman. Adapun data yang digunakan sebagai variabel dalam penelitian ini berupa data sekunder dan data primer. Data sekunder berupa penduduk usia rentan dan kerapatan jalan, sedangkan data primer berupa jarak dari Rumah Sakit rujukan dan persebaran lokasi vital. Teknik pengumpulan data tersebut yaitu dengan mencari berbagai referensi dari penelitian sebelumnya dan juga pengamatan langsung di lapangan. Metode yang digunakan yaitu analisis spasial deskriptif dengan metode overlay terhadap variabel-variabel yang digunakan. Hasilnya menunjukkan bahwa kerentanan tertinggi terhadap Covid-19 berada di bagian barat Kota Pariaman tepatnya di Kecamatan Pariaman Tengah karena terdapat kelompok usia rentan paling banyak, dan persebaran lokasi vital seperti cafe, pasar tradisional, dan stasiun berada disana, meskipun rumah sakit rujukan berada di kawasan tersebut

DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS

For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping

CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

Land cover change is a prevalent thing in Indonesia. This phenomenon often causes deforestation rates to continue to increase every year, which can cause various natural disasters. This study will look at changes in land cover, make land cover prediction models, and see the relationship between land cover changes and the flood disaster that occurred in Banjarmasin City and its surroundings. Remote sensing is used to see changes in land cover from year to year with GlobeLand30 satellite imagery. Satellite imagery processing is carried out using the Cellular Automata – Markov Chain method to see the land cover prediction. The results show that the most significant land cover change from 2000 to 2020 is experienced by built-up land and forests, while in 2030, forests are predicted to experience deforestation of 356 km2 from 2020. The deforestation will cause catastrophic flooding in 2021, where flooding extends to areas that are not estimated to be high flood hazards, with 111 flood points located in the plantation area

Spatial Distribution of Coral Reef Degradation with Human Activities in the Coastal Waters of Samatellu Lompo Island, South Sulawesi

A healthy coral reef ecosystem can be beneficial for the survival of fish habitats and aquatic ecosystems. This study aims to analyze the influence of human activities on the spatial distribution of coral reefs in the coastal waters of Samatellu Lompo Island, Pangkajene Islands Regency, South Sulawesi in 2000, 2014, 2018, and 2021. The spatial distribution of coral reefs was obtained through a field survey using the underwater transect photo method. Then, satellite images were processed by using the Lyzenga algorithm for water column correction, and aquatic objects were classified by using unsupervised classification. Human activities that affect coral reef destruction were obtained through interviews and it was strengthened with related literature studies. The results showed that the coral reefs in the coastal waters of Samatellu Lompo decreased from 2000-2021. In 2000, the live coral area was 13.53 ha, whereas in 2021 it was 8,031 ha. Destructive fishing activities such as using bombs and poison in catching fish are the main factors of coral reef destruction. In addition, destructive fishing activities commonly occur in the western and northern waters of Samatellu Lompo that causing the live coral into dead coral or rubble