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EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu) Tatik Kartika; Dede Dirgahayu; Inggit Lolita Sari; I Made Parsa; Ita Carolita
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 Desember 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Pemanfaatan penginderaan jauh dalam memantau vegetasi sudah banyak dilakukan, tetapi pemanfaatannya untuk mengevaluasi rehabilitasi di lahan kritis masih sangat jarang. Kegiatan rehabiliatsi hutan dan lahan dilakukan karena makin meningkatnya lahan kritis. Kegiatan rehabilitasi tersebut perlu dievaluasi, mengingat banyak sekali dana, waktu, dan tenaga yang diperlukan. Selama ini evaluasi dilakukan dengan cara langsung mendatangi lokasi rehabilitasi dengan memantau pertumbuhan tanaman pada setiap akhir tahun sampai akhir tahun ketiga. Menurut ketentuan peraturan yang berlaku, rehabilitasi dapat dikatakan berhasil apabila 90% vegetasi yang ditanam bisa tumbuh di akhir tahun ketiga. Kegiatan evaluasi dengan cara memantau kondisi vegetasi atau kerapatannya dapat dilaksanakan dengan memanfaatkan data penginderaan jauh, karena data tersebut mempunyai sifat multi temporal dan cakupan yang luas dan ketersediannya yang berlimpah dan mudah didapat. Data penginderaan jauh yang digunakan adalah Landsat-8 tahun 2013 sampai dengan 2018 dan metode evaluasi adalah analisis NDVI dari waktu ke waktu menggunakan SIG. Hasilnya adalah bahwa dari hasil survey yang diperoleh di kawasan APL terdapat lokasi rehabilitasi di lahan tidak kritis, agak kritis, kritis, dan sangat kritis dan berturut-turut keberhasilan rehabilitasi untuk APL_TK; APL_K; APL_AK; APL_SK jika NDVI melampaui nilai 0,337; 0,465; 0,493; 0,490 setelah bulan ke 21,8; 24,5; 26, dan 25,8.

PENGEMBANGAN METODE KLASIFIKASI LAHAN SAWAH BERBASIS INDEK CITRA LANDSAT MULTIWAKTU Made Parsa; Dede Dirgahayu; Sri Harini
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 Juni 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Penelitian pengembangan model klasifikasi lahan sawah berbasis citra penginderaan jauh Landsat bertujuan untuk memperoleh model klasifikasi lahan sawah secara cepat. Penelitian ini menggunakan input citra Landsat (p/r 122064) multiwaktu tahun 2017, informasi spasial lahan baku sawah 2017 skala detil (BIG) dan data hasil suvei lapangan. Penelitian dilaksanakan di salahsatu sentra produksi beras Jawa Barat yaitu di Kabupaten Subang. Metode yang digunakan dalam penelitian ini adalah metode threshold (ambang batas) terhadap indek citra Landsat multiwaktu. Sebagai referensi digunakan informasi spasial lahan sawah skala detil yang dilengkapi dengan data hasil survei lapangan menggunakan drone. Pertama dilakukan koreksi atmosfer terhadap citra Landsat, kemudian dilakukan ekstrak/konversi ke beberapa indek (EVI, NDWI, NDBI). Untuk citra-citra yang berawan maka indeksnya diisi dengan teknik interpolasi dari nilai indek sebelum dan setelahnya. Tahap berikutnya adalah smoothing indek dan analisis statistik untuk memperoleh nilai minimum, maksimum, mean, median, range, EVI_tanam, EVI_panen, mean_tanam-panen, mean_veg, mean_generatif, NDWI_tanam, NDWI_panen, NDBI_tanam dan NDBI_panen. Akurasi klasifikasi dihitung dengan teknik confusion matrix (matrik kesalahan) menggunakan referensi informasi spasial skala detil. Berdasarkan analisis dan uji akurasi yang telah dilakukan terhadap beberapa model, akurasi yang paling tinggi dihasilkan oleh model ambang batas tiga indek (EVI_min, EVI, Max, EVI_range) dengan akurasi 90,4% dan nilai kappa 0,80.

COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE) Faisal Ashaari; Muhammad Kamal; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.

MULTITEMPORAL LANDSAT DATA TO QUICK MAPPING OF PADDY FIELD BASED ON STATISTICAL PARAMETERS OF VEGETATION INDEX (CASE STUDY: TANGGAMUS, LAMPUNG) I Made Parsa; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Paddy field has unique characteristics that distinguish it from other plants. Before it planting, paddy field is always flooded so that the appearance is dominated by water (aqueous phase). Within the growth of rice, field conditions will be increasingly dominated by greenish rice plants.While at the end, the rice plants will turn yellow indicating for harvesting. During flooding stage, the normalized difference vegetation index (NDVI) of pady field is negative. The negative value of NDVI of paddy field will ultimately increase to the maximum value at the maximum vegetative growth. TheNDVI of paddy field will decrease from generative phase until harvest and after harvest. The objective of this study was to perform the vegetation index analyses for multitemporal Landsat imagery of paddy field. The results showed that the difference of vegetation index values (maximum - minimum)of paddy field were greater than the difference of vegetation index values of other land uses. Such differences values can be used as indicator to map land for rice. The evaluation results with reference data showed that the mapping accuracy (overall accuracy) was of 87.4 percent.

MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2 Samsul Arifin; Tatik Kartika; Dede Dirgahayu; Gatot Nugroho
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 2 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3385

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (Î”NDVI) and open land index (Î”NBR).The study found that the best thresholds to detect revegetation were Î”NDVI > 0.0309 and Î”NBR < 0.0176 and to detect devegetation Î”NDVI < -0.0206 and Î”NBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES Made Parsa; Dede Dirgahayu; Sri Harini; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3333

The development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping.

CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Dwi Wahyu Triscowati; Bagus Sartono; Anang Kurnia; Dede Dirgahayu; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.

COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE) Faisal Ashaari; Muhammad Kamal; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2845

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.

MULTITEMPORAL LANDSAT DATA TO QUICK MAPPING OF PADDY FIELD BASED ON STATISTICAL PARAMETERS OF VEGETATION INDEX (CASE STUDY: TANGGAMUS, LAMPUNG) I Made Parsa; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1838

Paddy field has unique characteristics that distinguish it from other plants. Before it planting, paddy field is always flooded so that the appearance is dominated by water (aqueous phase). Within the growth of rice, field conditions will be increasingly dominated by greenish rice plants.While at the end, the rice plants will turn yellow indicating for harvesting. During flooding stage, the normalized difference vegetation index (NDVI) of pady field is negative. The negative value of NDVI of paddy field will ultimately increase to the maximum value at the maximum vegetative growth. TheNDVI of paddy field will decrease from generative phase until harvest and after harvest. The objective of this study was to perform the vegetation index analyses for multitemporal Landsat imagery of paddy field. The results showed that the difference of vegetation index values (maximum - minimum)of paddy field were greater than the difference of vegetation index values of other land uses. Such differences values can be used as indicator to map land for rice. The evaluation results with reference data showed that the mapping accuracy (overall accuracy) was of 87.4 percent.

VERIFICATION OF LAND MOISTURE ESTIMATION MODEL BASED ON MODIS REFLECTANCES IN AGRICULTURAL LAND Dede Dirgahayu; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1216

From this research, it is found that reflectances in the first, second, and sixth channels (R1, R2, R6) of MODIS have high correlations with surface soil moisture (percent weight) at 0-20 cm depth. An index called Land Moisture INdex (LMI) was created from the linier combination of R1 (percent), R2(percent), and R6 (percent). The MODIS reflectances and field soil moisture in paddy field taken from the Central and East Java during Juli-September 2005 are applied into the previous model which have been generated from data during July-September 2004. The result showed that there was a high correlation between Land/Soil Moisture (SM) which was measured from field survey, and LMI which was generated from the MODIS refectances. The best model equation between SM and LMI is the power regression model, which has the coeficient of determination of 88 percent. It is implied that soil moisture condition can be obtained from the MODIS data using LAnd Moisture Index. Therefore, the spatial information of drouht condition analysed throught the soil moisture in the agricultural land can be provided from the MODIS data.

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