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Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province Ardiansyah Ardiansyah; Revi Hernina; Weling Suseno; Faris Zulkarnain; Ramadhani Yanidar; Rokhmatuloh Rokhmatuloh
Indonesian Journal of Geography Vol 50, No 2 (2018): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.

A Preliminary Study of the Physico-Chemical Parameters and Potential Pollutant Sources in Urban Lake Rawa Besar, Depok, Indonesia Mangapul Parlindungan Tambunan; Kuswantoro Marko; Ratna Saraswati; Rokhmatuloh Rokhmatuloh; Revi Hernina
Indonesian Journal of Geography Vol 53, No 2 (2021): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.60420

Lake Rawa Besar is an urban lake surrounded by dense settlements and commercial areas that are currently experiencing physical and ecological pressures due to uncontrolled land-use change around the lake. Therefore, this preliminary study aimed to investigate the sustainable management of the lake in order to create a recreational destination area. It was carried out by ascertaining the lake water quality status through the analysis of the physical and chemical parameters and identifying the potential pollutant sources due to land use and human activities. The physical parameters include TDS, TSS, Turbidity, while the chemical parameters include Nitrate-N, Total Phosphate-P, and BOD. Furthermore, field surveys on 30 water samples were conducted once at noon and statistical analysis was used to ascertain the correlation between the physical and chemical parameters. Finally, Geographic Information System (GIS) tools were used to investigate the spatial distribution of the Physico-chemical parameters and the potential pollutant sources. The results showed that based on the six parameters of the water quality status, the lake was lightly polluted. It also showed that three parameters such as Turbidity, BOD, and TSS exceed the permissible limit with 93.3, 66.7, 43.7% of the total samples, respectively. Additionally, a strong correlation existed between BOD and Turbidity with r=0.95, while a medium correlation existed between Nitrate-N and Phosphate-P with r=0.40. The spatial distribution of the concentration of the physico-chemical parameters generally had a varied pattern, however, Turbidity and BOD had a similar pattern, especially in the bank areas. Finally, domestic and organic wastes were indicated as pollutant sources, which increased eutrophication in the lake.

Analisis peringkat lokasi potensial taman Kota Depok di tingkat kecamatan menggunakan sistem informasi geografis dan topsis Revi Hernina; Yusuf Abdurrohim; arif wicaksono
Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi Vol 25, No 2 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um017v25i22020p145

This article provides rank alternatives of 113 potential urban park locations at sub-district level in Depok Municipality. These potential locations were selected because they have areas between ≥24.000 m2 and <144.000 m2 based on urban park classification in sub-district level. Three spatial criteria in form of vector datasets, namely road length, potential green open space (GOS) and park area were processed using Geographic Information System (GIS) and then weighted using ranking method. In every column of each location within spatial attribute table, those 3 criteria were processed to provide positive ideal (r+) and negative ideal (r -) solutions using Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. These solutions were computed to generate the relative closeness (ci+) of each location. When input into TOPSIS, road length and GOS status were classified as benefit indices while park area as cost indices. Results show the first rank has ci+ 0.83, while lowest rank has ci+ 0.039. For assessment of TOPSIS results’ performance, the demand for park total unit and area for every sub-district were computed from predicted population of 2030 and 2040. Simulated urban park development percentage from TOPSIS results were classified into 3 scenarios, namely scenario I (100% development), scenario II (50%) and scenario III (10%). It can be concluded that scenario I successfully meet the urban park area demand in 2040 for every sub-district. Moreover, scenario I sufficiently serve park unit demand in 8 sub-districts equal to 72.72% of the whole sub-districts in Depok Municipality.Artikel ini menyajikan alternatif peringkat dari 113 lokasi potensial taman Kota Depok di tingkat kecamatan. Lokasi potensial ini dipilih karena memiliki luas ≥24.000 m2 dan <144.000 m2 berdasarkan klasifikasi taman kecamatan. Tiga kriteria spasial berbentuk data vektor, yaitu panjang jalan, wilayah potensial ruang terbuka hijau (RTH), dan luas lokasi diproses dengan Sistem Informasi Geografis (SIG) kemudian diberi bobot menggunakan metode peringkat. Dalam setiap kolom atribut spasial, 3 kriteria tersebut diproses dengan metode Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) untuk menghasilkan solusi ideal positif (r+) and ideal negatif (r -). Nilai solusi ini dikomputasi menjadi nilai relative closeness (ci+) untuk setiap lokasi taman. Kriteria panjang jalan dan wilayah RTH potensial diklasifikasikan sebagai benefit indices sedangkan luas lokasi taman sebagai cost indices. Hasil studi ini menunjukkan bahwa lokasi taman kecamatan potensial dengan peringkat pertama memiliki nilai ci+ 0,83, sementara peringkat terakhir memiliki nilai ci+ 0,039. Untuk menganalisis kinerja lokasi taman potensial yang dihasilkan TOPSIS terhadap target kebutuhan taman, maka dilakukan prediksi kebutuhan jumlah unit dan luas taman kecamatan berdasarkan proyeksi penduduk tahun 2030 dan 2040. Selanjutnya simulasi prosentase pembangunan taman kecamatan hasil metode TOPSIS diklasifikasi menjadi 3 skenario yaitu skenario I (100%), skenario II (50%), dan skenario III (10%). Dapat disimpulkan untuk penerapan skenario I, hasil studi ini diprediksikan mampu melayani kebutuhan luas taman kecamatan di Kota Depok sampai tahun 2040. Sedangkan untuk memenuhi kebutuhan unit taman kecamatan sampai tahun 2040, skenario I masih bisa diaplikasikan pada 8 kecamatan atau 72,72% dari seluruh kecamatan di Kota Depok.

SURFACE URBAN COOL ISLAND ASSESSMENT IN VILLAGE-LEVEL TOURISM DESTINATIONS USING GIS AND PRINCIPAL COMPONENT ANALYSIS Hernina, Revi; Wicaksono, Arif
Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)
Publisher : UI Scholars Hub

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This study describes the process to generate classification of village-level tourism destinations known as kampung tematik in Bogor Municipality based on values from Surface Urban Cool Island (SUCI), Urban Index (UI) < -0.2, Normalized Difference Vegetation Index (NDVI) >0.3, and Normalized Difference Built-up Index (NDBI) < 0. SUCI values represent the cool temperature which usually associated with heavy greenery and dense vegetation, while UI < - 0.2, NDVI > 0.3, and NDBI < 0 are associated with dense and healthy vegetation. Herein, Band 10 from Landsat 8 was processed to derive land surface temperature (LST) and further, SUCI image was derived from LST image by employing SUCI algorithm. All three variables namely NDVI, NDBI, and UI were also processed from Landsat 8 dated to 24 May 2020. These four images of SUCI, NDVI, NDBI, and UI were superimposed with 39 kampung tematik locations which resulted in 14 locations having non-zero values. The values from four variables in 14 locations were inputted into Principal component Analysis (PCA) using R software version 4.1.3. PCA result shows there are four groups of locations based on variables representing cool island and dense vegetation namely, locations with area percentage of SUCI < 10% (Group 1), low values in three variables (Group 2), high values in two variables (Group 3), and high values in three variables (Group 4).

SURFACE URBAN COOL ISLAND ASSESSMENT IN VILLAGE-LEVEL TOURISM DESTINATIONS USING GIS AND PRINCIPAL COMPONENT ANALYSIS Hernina, Revi; Wicaksono, Arif
Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) Vol. 8, No. 1
Publisher : UI Scholars Hub

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This study describes the process to generate classification of village-level tourism destinations known as kampung tematik in Bogor Municipality based on values from Surface Urban Cool Island (SUCI), Normalized Difference Built-up Index (NDBI) ≤ -0.17, Normalized Difference Vegetation Index (NDVI) > 0.3, and Urban Index (UI) < -0.2. SUCI values represent the cool temperature, while NDBI ≤ -0.17, NDVI > 0.3, and UI < -0.2 are associated with dense and healthy vegetation. Herein, Band 10 from Landsat 8 was processed to generate land surface temperature (LST) and further, SUCI image was derived from LST image by employing SUCI algorithm. All three variables, namely NDVI, NDBI, and UI were also processed from Landsat 8 with date 24 May 2020. These four variables of SUCI, NDVI, NDBI, and UI image pixels were superimposed with 39 kampung tematik locations using Geographic Information System (GIS) which resulted in 14 locations containing the area percentage from four variables. The values from four variables in 14 locations were inputted into Principal component Analysis (PCA) using R software version 4.1.3. Based on PCA, it reveals that three tourism destinations are classified with area percentage of SUCI less than 50% while the other 11 destinations are classified with area percentage of SUCI more than 50%. In addition, characteristics of tourism destinations with SUCI area percentage less than 50% are identified to have low area percentage of NDBI ≤ -0.17, NDVI > 0.3, and UI < -0.2.

Modelling Land Use Land Cover Change in Banyumas Regency Using Remote Sensing Data for Tourism Policy Evaluation Hernina, Revi; Wicaksono, Arif; Wibowo, Adi; Damayanti, Astrid
Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) Vol. 9, No. 2
Publisher : UI Scholars Hub

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Tourism is essentially a geographical phenomenon, encompassing the movement and flow of people and spatial distribution patterns relating to land use consumption. The impacts of tourism on LUCC must track and monitor regrading effected to environment and human side. For decades, Banyumas Regency has been known for its famous tourist destinations such Baturaden District and numerous waterfalls. However, the development of tourism infrastructures within its vicinity has sparked complaints from communities, particularly damaged roads and decreasing tourist visits. From environmental perspective, excessive tourism development might cause decreasing natural carrying capacity. Therefore, to provide deeper analysis regarding the current tourism development, this article describes the Land Use/Land Cover Change (LULC) in Banyumas Regency in 2000, 2010, and 2020 based on satellite data analysis using Google Earth Engine (GEE). Sentinel satellite data in 2020 were processed to generate LULC, while Landsat TM 5 data were used to provide LULC in 2000 and 2010. LULC classification was performed using Random Forest with accuracy assessment (Kappa) reach 0.81 for 2000, 0.91 for 2010, and 0.96 for 2010. Furthermore, based on CA-Markov method, the predicted LULCC in 2030 will be 84.28% unchanged and 15.72% change. In addition, several tourism development policies need to be evaluated based on the predicted LULCC in Banyumas Regency in order to avoid disturbance to natural resources.

Relationship Between Vegetation Greenness (NDVI) and Land Surface Temperature Across Land Cover Types in Ciwidey Sub-Watershed (1990–2020) Syabila, Syal; Marko, Kuswantoro; Hernina, Revi
Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) Vol. 9, No. 1
Publisher : UI Scholars Hub

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Land cover change significantly influences vegetation greenness and land surface temperature (LST), particularly in upstream watershed regions experiencing rapid development. This study aims to analyze changes in vegetation greenness (NDVI), land surface temperature, and their relationship across different land cover types in the Ciwidey Sub-Watershed, Bandung Regency, during 1990–2020. Landsat 5 TM and Landsat 8 OLI images (Path/Row 122/65) acquired in July 1990, 2005, and 2020 were processed using radiometric correction, supervised classification (Maximum Likelihood), NDVI extraction, and mono-window LST algorithm. Land cover classification accuracy was assessed using confusion matrix analysis. Linear regression was applied to evaluate the relationship between NDVI and LST. Results indicate a decline in high vegetation greenness areas and an increase in very low greenness and non-vegetated areas, particularly in built-up land and mixed gardens. Surface temperature shows a consistent upward trend, with dominant temperature classes shifting from 19–21°C (1990–2005) to 22–24°C (2020). Regression analysis reveals a strong negative relationship between NDVI and LST in all observed years (1990: y = -15.113x + 20.683; 2005: y = -14.313x + 23.012; 2020: y = -10.927x + 25.175), indicating that increasing vegetation greenness reduces surface temperature. These findings confirm that vegetation degradation contributes to thermal amplification in the sub-watershed. The study highlights the importance of vegetation conservation for sustainable watershed management.

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