SDG 14: Life Below Water

Arsip:

Mangrove area degradation and management strategies in Indonesia: A review

Publication Friday, 19 April 2024

Utami, Westi | Sugiyanto, Catur | Rahardjo, Noorhadi

Journal of Degraded and Mining Lands Management, Open Access, Volume 11, Issue 3, Pages 6037 – 6047, April 2024

Abstract

Coastal communities are particularly vulnerable to the effects of climate change. This study aimed to review the impact of land use change and mangrove degradation on coastal areas and analyze the problems and strategies of mangrove management in Indonesia. Journal data sources were obtained from the Scopus and Google Scholar databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used in the study. The results indicate that economic activities such as mining, industry, and ponds are the main cause of mangrove damage, which in turn leads to increased disasters, CO2 emissions, and decreased community income. To address this issue, mangrove management strategies that take into account social, economic, and environmental factors are necessary. Successful mangrove restoration requires increased inclusion, community capacity, budgeting, and stakeholder involvement. Government policies regulating sustainable spatial planning, law enforcement, and institutional frameworks are crucial components of mangrove management. The study’s results are expected to serve as the foundation for developing sustainable coastal management strategies that enhance the safety of coastal communities from disasters and improve their welfare. © 2024 Brawijaya University. All rights reserved. read more

Dynamics of the aeolian landform at the coastal geosite of Parangtritis sand dune area, Yogyakarta

Publication Friday, 19 April 2024

Journal of Degraded and Mining Lands Management, Open Access, Volume 11, Issue 3, Pages 5839 – 5847, April 2024

Abstract

In 2014, Kelud, an active volcano in East Java, ejected ashes (hereafter termed KA; Kelud ash) that reportedly reached as far as Central Java and the Special Region of Yogyakarta (DIY). The KA deposits are observable in, among others, the coastal sand dune area in Parangtritis. Due to the active aeolian process, the KA layers can be found at different depths five years after the eruption. Thus, the KA can be used as a marker of the post-2014 aeolian activities at the Parangtritis dune, which was once classified as a degraded land and later recognized as a coastal geosite, ultimately allowing us to calculate the volume and sand transport rate. The research surveys performed at 25 points discovered volcanic ash layers at 1 cm to 26 cm below the surface. At some observed points, the ash deposits were found on the surface and continuously transported. This research observed an area of 60,000 m² with an average of 8.5 cm thick sand layers lying above the KA layer. Based on the calculation, the total volume of the transported sand at the Parangtritis sand dune from 2014 to 2019 was approximately 5,100 m³. This volume is considerably small owing to various obstacles in the transport zones. Based on the five-year dynamic of the sand dune, this study suggested that restoration needs to be conducted immediately particularly in eliminating vegetation barriers to maintain the sediment balance (erosion-transport-sedimentation) of the coastal sand dune area. © 2024 Brawijaya University. All rights reserved. read more

Monitoring vegetation patterns and their drivers to infer resilience: Automated detection of vegetation and megaherbivores from drone imagery using deep learning

Publication Friday, 19 April 2024

Ecological Informatics, Open Access, Volume 81, July 2024, Article number 102580

Abstract

Ecological pattern theory has highlighted spatial vegetation patterns that can be used as indicators of ecosystem resilience. Combining this spatial pattern theory with aerial imagery from drones and automated image processing with deep learning methods, we show how monitoring of natural ecosystems can be enhanced through quantifying vegetation spatial patterns. We demonstrate this approach in a tropical seagrass ecosystem with a high abundance of turtles that generate vegetation patches when grazing. Past field observations suggest that patch size and density reflect the seagrass meadow resilience, but understanding the natural variation in vegetation patchiness is crucial. Employing the deep learning methods of semantic segmentation and object detection, we quantify vegetation patchiness metrics and turtle distribution across 12 ha of seagrass meadow in the years 2012 and 2022. The resulting output facilitates spatial and temporal comparisons, revealing areas of low resilience. In 2012, turtle grazing across the entire site yielded vegetation patch sizes averaging 2 ± 0.2 m² (95% confidence interval). Reduced patch sizes of 0.24 ± 0.05 m² and 0.67 ± 0.6 m² at the reef edge and beach slope respectively, in conjunction with a reduced patch density, indicated lower resilience at the seagrass meadow edges. Analysis of the 2022 dataset indicates a general decrease in patch size over time, suggesting declining resilience. A retraining experiment of the semantic segmentation model was conducted where the initial model was retrained on the 2022 dataset and demonstrated the adaptability of the deep learning methods. Despite using different equipment, the model achieved high accuracy with only 5–10 additional training images. By providing the tools to conduct these analyses, we aim to stimulate the uptake of deep learning for enhancing the data obtained from aerial imagery to improve the monitoring and conservation of natural ecosystems. © 2024 The Authors read more