.png)
Time Series Forecasting of Environmental Dynamics in Urban Ecotourism Forest Using Deep Learning
Abstract
Time Series Forecasting of Environmental Dynamics in urban forests is quite challenging, unless new approaches such as deep learning and remote sensing are employed. Deep learning-based time series algorithms offer robust scientific capabilities for forecasting and assessing sustainability trends using sequential data. Among these, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) have gained widespread adoption across various predictive modeling domains. In the present research, these algorithms are employed to analyze urban forest raster data derived from the Srengseng Ecotourism Forest, located in West Jakarta, Indonesia. The present study focuses on predicting the temporal patterns of key spatial indicators: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Forest Cover Density (FCD) in the Srengseng urban ecotourism forest area, spanning the years 2014 to 2024, through the application of LSTM, GRU, and BiLSTM deep learning architectures. The methodology used in this study is a combined approach involving remote sensing and deep learning. Spatial data were acquired through the delineation of a high-precision polygon of Srengseng Urban Forest using Google Earth Pro and Google Earth Engine (GEE). GeoTIFF datasets of NDVI, LST, and FCD for the years 2014–2024 were processed using Python-based modeling scripts. Model performance was evaluated through a comparative analysis of LSTM, GRU, and BiLSTM in predicting temporal trends in these ecological indicators. The results of this study show that the Bidirectional LSTM (BiLSTM) consistently demonstrated superior performance to predict all the data spatially, with scores of 0.94 for NDVI, 0.90 for FCD, and 0.85 for LST. Followed by LSTM that predicts NDVI (0.87), FCD (0.89), LST (0.83), as well as GRU, which can estimate spatial data NDVI (0.86), FCD (0.89), and LST (0.85). These results outperformed the predictive accuracy of both the standard LSTM and GRU models.
Keywords
Full Text:
PDFReferences
A. Rohmah, H. W. Wiranegara, and Y. Supriatna, “Jasa Lingkungan Hutan Kota Srengseng,” J. Penelit. Dan Karya Ilm. Lemb. Penelit. Univ. Trisakti, vol. 7, no. 2, pp. 182–192, 2022, doi: 10.25105/pdk.v7i2.12459.
R. K. Markho, S. Herlambang, P. Rahardjo, and S. Santoso, “Evaluasi Kelayakan Hutan Kota Studi Kasus Hutan Kota Srengseng, Jakarta Barat,” J. Sains, Teknol. Urban, Perancangan, Arsit., vol. 2, no. 2, p. 2731, 2020, doi: 10.24912/stupa.v2i2.8918.
L. A. D. Sari, D. Susanto, and Mukhlison, “The tree diversity of Srengseng Urban Forest in DKI Jakarta,” J. Penelit. Kehutan. Wallacea, vol. 11, no. 1, pp. 13–20, 2022, doi: 10.18330/jwallacea.2022.vol11iss1pp13-20.
A. Agustiyara, D. Mutiarin, A. Nurmandi, A. N. Kasiwi, and M. F. Ikhwali, “Mapping Urban Green Spaces in Indonesian Cities Using Remote Sensing Analysis,” Urban Sci., vol. 9, no. 2, pp. 1–23, 2025, doi: 10.3390/urbansci9020023.
E. Suryanto, A. I. Suroso, and R. Hasbullah, “Rancang Bangun Model Bisnis Ekowisata Grasberg Pasca Tambang,” J. Apl. Bisnis dan Manaj., vol. 7, no. 3, pp. 743–751, 2021, doi: 10.17358/jabm.7.3.743.
J. Kim and Y. Son, “Assessing and mapping cultural ecosystem services of an urban forest based on narratives from blog posts,” Ecol. Indic., vol. 129, p. 107983, 2021, doi: 10.1016/j.ecolind.2021.107983.
H. Tavárez and L. Elbakidze, “Urban forests valuation and environmental disposition: The case of Puerto Rico,” For. Policy Econ., vol. 131, 2021, doi: 10.1016/j.forpol.2021.102572.
R. Wu, J. Wang, D. Zhang, and S. Wang, “Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China,” Cities, vol. 114, no. August 2020, p. 103202, 2021, doi: 10.1016/j.cities.2021.103202.
N. Besic et al., “Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?,” Geosci. Model Dev., vol. 18, no. 2, pp. 337–359, 2025, doi: 10.5194/gmd-18-337-2025.
Z. A. Pierrat et al., “Proximal remote sensing: an essential tool for bridging the gap between high-resolution ecosystem monitoring and global ecology,” New Phytol., vol. 246, no. 2, pp. 419–436, 2025, doi: 10.1111/nph.20405.
M. Rhif, A. Ben Abbes, B. Martinez, and I. R. Farah, “Deep Learning Models Performance for NDVI Time Series Prediction: A Case Study on North West Tunisia,” 2020 Mediterr. Middle-East Geosci. Remote Sens. Symp. M2GARSS 2020 - Proc., no. June, pp. 9–12, 2020, doi: 10.1109/M2GARSS47143.2020.9105149.
M. Fathi, R. Shah-Hosseini, and A. Moghimi, “3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data,” Remote Sens., vol. 15, no. 23, pp. 1–20, 2023, doi: 10.3390/rs15235551.
Y. Sun, D. Lao, Y. Ruan, C. Huang, and Q. Xin, “A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data,” Sustain., vol. 15, no. 8, 2023, doi: 10.3390/su15086632.
Y. Zhou, C. Huang, T. Wu, and M. Zhang, “A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation,” Ecol. Modell., vol. 482, 2023, doi: 10.1016/j.ecolmodel.2023.110394.
M. Marjani, M. Mahdianpari, and F. Mohammadimanesh, “CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction,” Remote Sens., vol. 16, no. 8, 2024, doi: 10.3390/rs16081467.
W. Zhao and N. Efremova, “Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring,” pp. 2–6, 2024, [Online]. Available: http://arxiv.org/abs/2407.00834
Y. Fan, Q. Tang, Y. Guo, and Y. Wei, “BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms,” Sensors, vol. 24, no. 12, 2024, doi: 10.3390/s24123962.
C. Chen and J. Dong, “Deep learning approaches for time series prediction in climate resilience applications,” Front. Environ. Sci., vol. 13, no. April, pp. 1–18, 2025, doi: 10.3389/fenvs.2025.1574981.
I. Firdaus, Y. Arkeman, A. Buono and Hermadi, “Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm,” J. Phys. Conf. Ser., vol. 755, no. 1, 2016, doi: 10.1088/1742-6596/755/1/011001.
A. Javed, T. Kim, C. Lee, J. Oh, and Y. Han, “Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images,” Remote Sens., vol. 15, no. 17, 2023, doi: 10.3390/rs15174285.
D. A. L. I. IPBUniversity, “Reseach using i-Tree at Srengseng Urban Forest,” 2024. https://www.ipb.ac.id/news/index/2024/02/department-of-landscape-architecture-at-ipb-university-organizes-i-tree-eco-training-in-srengseng-city-forest-jakarta/?utm_source=chatgpt.com
H. Hashim, Z. Abd Latif, and N. A. Adnan, “URBAN VEGETATION CLASSIFICATION with NDVI THRESHOLD VALUE METHOD with VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 4/W16, pp. 237–240, 2019, doi: 10.5194/isprs-archives-XLII-4-W16-237-2019.
M. Wang, Z. Zhang, T. Hu, and X. Liu, “A Practical Single-Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat Series Data,” J. Geophys. Res. Atmos., vol. 124, no. 1, pp. 299–316, 2019, doi: 10.1029/2018JD029330.
D. T. Loi, T.-Y. Chou, and Y.-M. Fang, “Integration of GIS and Remote Sensing for Evaluating Forest Canopy Density Index in Thai Nguyen Province, Vietnam,” Int. J. Environ. Sci. Dev., vol. 8, no. 8, pp. 539–542, 2017, doi: 10.18178/ijesd.2017.8.8.1012.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014, doi: 10.5194/gmd-7-1247-2014.
G. G. Montgomery, Douglas C.; Peck Elizabeth A.;Vining, “Introduction to Linear Regression Analysis -- Douglas C. Montgomery; Elizabeth A. Peck; G. Geoffrey Vining -- Wiley Series in Probability and -- 9781119578727 -- 4321e1522f7e4417d69fbb6e681599ef -- Anna’s Archive.” 2021.
DOI: https://doi.org/10.47738/jads.v7i1.1029
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0