Publikasi

@article{Rosikin2023,

title = {Assessment of the success of canopy cover revegetation of former coal mine lands with Forest Canopy Density (FCD) Model in Kutai Kartanegara, East Kalimantan},

author = {Rosikin and Lilik Budi Prasetyo and Rachmad Hermawan},

doi = {10.29244/jpsl.13.4.574-585},

issn = {24605824},

year  = {2023},

date = {2023-01-01},

journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan},

volume = {13},

number = {4},

pages = {574–585},

abstract = {Coal mining plays a vital role in Indonesia's economic growth. However, these activities negatively impact the environment. To minimize this, the Indonesian government requires ex-mining land to be reclaimed, with one of the success criteria being canopy cover. Until now, there has been no measurable method that can determine the success rate of canopy cover on reclaimed land. This research was conducted to develop a measurement method based on remote sensing data using the Forest Canopy Density (FCD) Model, which is applied in Company X, Kutai Kertanegara. The FCD Model consisted of four biophysical indices, including AVI, BSI, SI, and TI, obtained from Landsat 8 OLI TIRS imagery from 2013–2021. The Kolmogorov-Smirnov normality test was performed before testing the relationship between FCD values and canopy cover using linear regression to obtain the canopy cover success value based on the FCD value. The FCD showed an increasing trend yearly, especially in the first two years after planting. Regression analysis showed a strong relationship between FCD values and canopy cover values, with R2 =0.775, and revealed that 75.35 is the FCD value threshold for a successful canopy cover in the reclamation area. This study shows that the FCD approach can be applied to determine the success rate of reclamation in post-mining areas.},

keywords = {Coal, forest canopy density, mining, reclamation, remote sensing},

pubstate = {published},

tppubtype = {article}

}

@article{Prayudha2021,

title = {The application of Landsat imageries and mangrove vegetation index for monitoring mangrove community in Segara Anakan Lagoon, Cilacap, Central Java},

author = {B. Prayudha and V. Siregar and Y. I. Ulumuddin and Suyadi and L. B. Prasetyo and S. B. Agus and Suyarso and K. Anggraini},

doi = {10.1088/1755-1315/944/1/012039},

issn = {17551315},

year  = {2021},

date = {2021-01-01},

journal = {IOP Conference Series: Earth and Environmental Science},

volume = {944},

number = {1},

abstract = {The only place for estuarine-mangroves in Java Island, Segara Anakan Lagoon, experiences the vast decline of mangrove cover. Satellite remote sensing has a critical role in monitoring that change as it allows to record vast areas over time. However, most studies tend to utilize satellite data to investigate the change of mangrove areas into other land-use types rather than identify the mangrove community's shifting. This study utilized the mangrove vegetation index (MVI) for monitoring the changes of mangrove communities at the life-form level using satellite data. The study used multi-temporal Landsat images as it has historical systematic archive data. The threshold value of the index for each class is defined by referring to the field data. The class referred to the life-form classification consisting of mangrove trees, Nypa, and understorey. The image analysis was conducted using Google Earth Engine (GEE), while R software was used for determining threshold values through statistical analysis. The result shows that the MVI can differentiate between some life forms of mangroves, with the overall accuracy reaching 78.79% and a kappa coefficient of 0.729. Further, the multi-temporal maps showed the decline of mangrove tree areas, which the understorey and Nypa community have replaced.},

keywords = {life-form community, mangrove changes, mangrove vegetation index, remote sensing},

pubstate = {published},

tppubtype = {article}

}