Spatio-temporal monitoring of mangrove biodiversity using Landsat-8 imagery in Kendari Bay, Indonesia

Abstract. Fekri L, Analuddin K, Yusnaini, Adimu HE, Chadijah A. 2024. Species composition and size distribution of fishes in mangrove ecosystems in Kendari and Staring Bays, Southeast Sulawesi, Indonesia. Biodiversitas 25: 3683-3692. Mangrove ecosystem plays very important role in supporting marine organisms including fishes, shells, etc. This study aimed to determine the species composition and size distribution of fishes living around the mangrove ecosystems in Kendari and Staring Bays, Southeast Sulawesi, Indonesia. Fish samplings were conducted using 6 gill nets fishing gear with mesh sizes of 1, 1.5 and 2 inches. Mangrove species composition was sampled using circular plots with diameter of 14 m. The fish conservation status and abundance were determined, while size distribution was analyzed in terms of frequency distribution of body weight, and correlation of body weight and body length. A total of 33 fish species were recorded across the two sampling sites with 30 species were found in Kendari Bay and 19 species were found in Staring Bay. The fish species are mostly classified as least concern based on the IUCN Red List. We found that species richness, presence and abundance of fishes varied according to the moon lightness periods during the fish catchments. The highest species richness was found in the dark moon both in Kendari and Staring Bays, while the lower fish richness was found in the transition from the dark to the full moon in Kendari Bay and the transition from the full moon to the dark moon in Staring Bay. Five fish species associated with mangroves of Kendari and Staring Bays were found more abundant and showed different size distribution pattern, i.e., normal shape of size distribution for Zenarchopterus dispar (Valenciennes, 1847), Ambassis dussumieri Cuvier, 1828 and Upeneus moluccensis Bleeker, 1855, while Mugil cephalus Linnaeus, 1758 and Acentrogobius viridipunctatus Valenciennes, 1837 showed L-shaped size distribution. These five dominant fishes showed well allometric relationships of body length and weight. Our findings suggested that mangrove ecosystems in Kendari and Staring Bays play an important role in maintaining fish diversity.

Can remote sensing of land cover improve species distribution modelling?

Marine debris is a solid material that is either accidentally or accidentally disposed of in a river that empties into the sea or is left directly in the sea. The research on marine debris is carried out in the estuary and mangrove forest areas of Kendari Bay which is assumed to be accommodated garbage sites that enter the coastal area. The purpose of this study is to identify the types of composition and potential sources of marine waste contributors found in several river estuaries and mangrove forest areas, to know the density of marine debris and to mapping the distribution of marine debris from several river estuaries and mangrove forest in Kendari Bay. This research was conducted by survey method and using 5x5 quadratic plots in a random sampling. The data obtained were analyzed statistically and map overlays. The results showed that the composition of marine debris types in mangrove ecosystems and estuaries in Kendari bay generally consisted of plastic (plastic bottles, plastic bags, ropes, pipettes, plastic cups), metals (beverage cans), rubber, glass (glass bottles) and others (cloth, paper and others) with the dominant amount of plastic waste. The highest total density of waste types in the four locations in the mangrove ecosystem and river estuary in Kendari Bay is at station 1 (Lahundape Mangrove Tracking Area). The density of plastic waste dominates the four locations in the mangrove ecosystem with the highest value of 3,024 items/m2 in the mangrove ecosystem around the estuary of Mandonga and Lahundape. The high distribution of marine debris in the mangrove ecosystem is found at station 1 and station 2 because it is directly related to the river flow which contributes greatly to marine debris input in Kendari Bay.

The Loess Plateau, serving as a crucial ecological barrier in China, plays a pivotal role in maintaining and enhancing the habitat quality. This study focuses on the Loess Plateau as a research area. It innovatively integrates the InVEST with the XGBoost-SHAP model to systematically investigate the spatio-temporal characteristics and driving mechanisms of habitat quality changes. A decline in habitat quality has been observed in the study area over the past three decades. The degree of degradation shows a distinct northwest-southeast spatial gradient. High-quality habitat areas are predominantly located in mountainous ecological zones such as the Lüliang Mountains and the Taihang Mountains, as well as on the southern Shanbei Plateau and the western Longzhong Plateau Terrain ruggedness index (TRI), population density (Pop), and normalized difference vegetation index (NDVI) are the primary factors driving habitat quality spatiotemporal differentiation. The study provides a scientific foundation for ecosystem services management on the Loess Plateau.

Mangroves play a vital role in the environment and contribute signific aptly to the well-being of coastal communities by providing goods and services. Unfortunately, the degradation and deforestation of mangroves has frequently occurred. Therefore, it is important to understand the vulnerability of mangroves and its impact on blue carbon storage for effective mangrove management and coastal planning. This study aims to assess the vulnerability of mangroves and its effect on blue carbon storage in the coral triangle region of Southeast Sulawesi, Indonesia. The vulnerability assessment included factors such as exposure, sensitivity, and adaptive capacity. Blue carbon storage was evaluated based on carbon stock in sediment, biomass, and total carbon stock in mangrove ecosystems in both protected and unprotected areas. The mangrove ecosystem in the protected area of Rawa Aopa Watumohai National (RAWN) Park showed lower vulnerability due to lower exposure, lower sensitivity, and higher adaptive capacity. On the other hand, mangrove ecosystems in unprotected areas such as Tinanggea (TNG), Kendari Bay (KDI), and Staring Bay (STR) exhibited moderate to high vulnerability due to higher exposure, sensitivity, and lower adaptive capacity. Mangroves in RAWN Park, which were less vulnerable, had higher blue carbon storage in sediment (381.64 tons C/ha), biomass (312.65 tons C/ha), and the entire ecosystem (706.76 tons C/ha). These values were significantly higher (p<0.05) compared to those in TNG (306.66 tons, 144.15 tons, and 448.37 tons C/ha, respectively), KDI (262.08 tons, 227.73 tons, and 470.76 tons C/ha, respectively), and STR (169.44 tons, 66.66 tons, and 253.27 tons C/ha, respectively). The high vulnerability of mangrove ecosystems resulted in reduced carbon storage in the coastal regions of Southeast Sulawesi. Therefore, efforts such as rehabilitation and restoration, legislation, and conservation should be prioritized to enhance blue carbon storage, and other ecosystem services provided by mangroves in the coral triangle region of Southeast Sulawesi.

Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts.

Abstract. Hasidu LOAF, Jamili, Kharisma GN, Prasetya A, Maharani, Riska, Rudia LOAP, Ibrahim AF, Mubarak AA, Muhasafaat LO, Anzani L. 2020. Diversity of mollusks (bivalves and gastropods) in degraded mangrove ecosystems of Kolaka District, Southeast Sulawesi, Indonesia. Biodiversitas 21: 5884-5892. Mollusks are one of the mangrove organisms whose classes are bivalves and gastropods. It plays an important role in mangrove and marine ecosystems as filter feeders, predators, and herbivores. This study aims to knows the diversity and abundance of mollusks (bivalves and gastropods) in several mangrove ecosystems in the Kolaka coastline as well as the similarity of these locations. This study was conducted in mangrove ecosystems of Induha Village, Mangolo Village, Tahoa Village, and Towua Village of Kolaka District, Southeast Sulawesi, Indonesia, from July to August 2019. This is a transect method stretched along a 100 m line perpendicularly from the seaward. The size of the mollusks subplot was 1 m2 and placed along the line transect. Each line transect comprises 10 subplots. To analyze the diversity index, evenness index, and its abundance, Kaleida Graph 4.0 version was used. This research indicates that the mollusks consist of 4 families of bivalves with 6 species and 10 families of gastropods with 182 species. It also found out 23 species of mollusks scattered to each location. The molluscan species which spread in all four mangrove ecosystems were Terebralia sulcata, Nerita planospira, and Batillaria multiformis. In Induha, the mollusks species were Anadara notabilis and Drupella margariticola. Meanwhile, Saccostrea cucullata, Pirenella incisa, Clithon oualaniensis, and Clithon pulchellum were only found in Towua. The diversity index of bivalves in each location was categorized as low diversity index category, as well as gastropods were categorized as medium diversity index. The highest diversity index of gastropods was in Induha (H' = 1.96). It was supported by the good mangrove ecosystem for mollusks' habitat. The lowest diversity index of gastropods was in Towua (1.41). This research depicts that three kinds of species with high abundance rate whose rates were >1 ind/m2are located in two different locations namely; P. incisa (3.9 ind/m2) and S. cucullata (3.2 ind/m2) in Towua and followed by B. multiformis (2 ind/m2) and Isognomon ephippium (1.2 ind/m2) in Mangolo.

Rapid global urbanization has caused habitat degradation and fragmentation, resulting in biodiversity loss and the homogenization of urban species. Birds play a crucial role as biodiversity indicators in urban environments, providing multiple ecosystem services and demonstrating sensitivity to changes in habitat. However, construction activities often disrupt urban bird habitats, leading to a decline in habitat quality. This paper proposes a framework for prioritizing habitat restoration by pinpointing bird hotspots that demand attention and considering the matching relationship between bird richness and habitat quality. Shanghai represents a typical example of the high-density megacities in China, posing a significant challenge for biodiversity conservation efforts. Utilizing the random forest (RF) model, bird richness patterns in central Shanghai were mapped, and bird hotspots were identified by calculating local spatial autocorrelation indices. From this, the habitat quality of hotspot areas was evaluated, and the restoration priority of bird habitats was determined by matching bird richness with habitat quality through z-score standardization. The results were as follows: (1) Outer-ring green spaces, large urban parks, and green areas along coasts or rivers were found to be the most important habitats for bird richness. Notably, forests emerged as a crucial habitat, with approximately 50.68% of the forested areas identified as hotspots. (2) Four habitat restoration types were identified. The high-bird-richness–low-habitat-quality area (HBR-LHQ), mainly consisting of grassland and urban construction land, was identified as a key priority for restoration due to its vulnerability to human activities. (3) The Landscape Shannon’s Diversity Index (SHDI) and Normalized Difference Vegetation Index (NDVI) are considered the most significant factors influencing the bird distribution. Our findings provide a scientifically effective framework for identifying habitat restoration priorities in high-density urban areas.

Geloina expansa is a front-runner commodity of the mangrove ecosystem. This species is notably experiencing ecological pressures in Kendari Bay. Accordingly, this study aims to determine their production, biomass, and turnover in the mangrove ecosystem. This research is hoped to provide empirical information that will aid in the formulation of the management strategy of mangrove clam resources in Southeast Sulawesi. Clam samples were collected at random in three selected sampling areas using a 1x1 m2 quadrat-transect sampling approach. The clams were measured for their shell length, total weight and weight of fresh meat. The clam meat was dried to obtain a shell-free dry mass. The production, biomass, and turnover of the clams were calculated using standard formulas. The population density of the clams ranged from 23.78 ind/m2 (October) to 77.44 ind/m2 (February), where the remaining months of observations showed similar values throughout. The clams biomass population in each size class ranged from 0.04 to 4.95 g/m2. The somatic production, as per the dry weight showed the highest value at 6.9 cm shell length (2.01g/m2/year). The lowest individual somatic production was found in the shell width of 9.7 cm (0.55 g/m2/year). The turnover rate (P/B) of the mangrove clam was 1.73/year. The density of the mangrove clams in the mangrove forest in Kendari Bay was found to be high. This was accompanied by high productions in the young or small-sized groups, peaking at a size smaller than the size where peak biomass was found.

The integrity of habitat quality is a pivotal cornerstone for the sustainable advancement of local ecological systems. Rapid urbanization has led to habitat degradation and loss of biodiversity, posing severe threats to regional sustainability, particularly in extremely vulnerable arid zones. However, systematic research on the assessment indicators, limiting factors, and driving mechanisms of habitat quality in arid regions is notably lacking. This study takes Urumqi, an oasis city in China’s arid region, as a case study and employs the InVEST and PLUS models to conduct a dynamic evaluation of habitat quality in Urumqi from 2000 to 2022 against the backdrop of land use changes. It also simulates habitat quality under different scenarios for the year 2035, exploring the temporal and spatial dynamics of habitat quality and its driving mechanisms. The results indicate a decline in habitat quality. The habitat quality in the southern mountainous areas is significantly superior to that surrounding the northern Gurbantunggut Desert, and it exhibits greater stability. The simulation and prediction results suggest that from 2020 to 2035, habitat degradation will be mitigated under Ecological Protection scenarios, while the decline in habitat quality will be most pronounced under Business-As-Usual scenarios. The spatial distribution of habitat quality changes in Urumqi exhibits significant autocorrelation and clustering, with these patterns intensifying over time. The observed decline in habitat quality in Urumqi is primarily driven by anthropogenic activities, urban expansion, and climate change. These factors have collectively contributed to significant alterations in the landscape, leading to the degradation of ecological conditions. To mitigate further habitat quality loss and support sustainable development, it is essential to implement rigorous ecological protection policies, adopt effective ecological risk management strategies, and promote the expansion of ecological land use. These actions are crucial for stabilizing and improving regional habitat quality in the long term.

Abstract. The ever-evolving technology has significantly affected the sensors used in UAV cameras and has played an important role in the expansion of the application areas of hobbyist and commercial UAVs. In particular, UAVs with multispectral (MS) cameras, which have the potential to detect a wide range of spectral information, are widely used in many popular research areas such as precision agriculture and forestry. However, despite their advanced capabilities, the high cost of these technologies limits their accessibility for basic users. In this study, the agricultural potential of RGB UAVs, which have a much wider user base due to their lower cost, was investigated by predicting the Normalized Difference Vegetation Index (NDVI), which is widely preferred for plant classification, growth and health monitoring. In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R2) or the model accuracy of the algorithms used. The approaches applied have generally been tested on single photographs or solely on vegetation areas. In this study, using the MS UAV NDVI map as reference, a comprehensive evaluation approach was applied where each pixel of the NDVI prediction maps produced by categorical boosting (CatBoost), light gradient boosting machine (LightGBM) and a stacking ensemble learning model obtained from the combination of both algorithms, whose performance in NDVI estimation has not been tested extensively before. The models were tested in an urban area with numerous buildings and a large study area with dense vegetation. The performance of the NDVI maps was analyzed using R2, Root Mean Square Error (RMSE), Normalized Median Absolute Deviation (NMAD) and Standard Deviation (STD) metrics. As a result of the comprehensive analysis, it was found that the models performed similarly in general, but the LightGBM model was slightly behind the others. The considerable results around 0.81–0.83 as R2 and ~0.09 as RMS and STD clearly showed that RGB cameras can be a lower-cost alternative solution for generating NDVI maps in agricultural studies when supported by machine learning models.

UAV-based partially sampling system for rapid NDVI mapping in the evaluation of rice nitrogen use efficiency

This study analyzes temporal trends in vegetation health across South-South Nigeria’s ecological zones from 2000 to 2020, using the Normalized Difference Vegetation Index (NDVI) as a key metric. The research investigates changes in vegetation density and vigor within rainforest, derived savanna, freshwater swamp, mangrove, and Guinea savanna zones, linking observed patterns to climatic variability and human activities. NDVI data from MODIS satellites, alongside climate data on rainfall, temperature, and soil moisture, were processed using ArcGIS 10.5 to evaluate spatiotemporal vegetation dynamics. The results reveal a general decline in NDVI across all zones over the 20-year period, with marked reductions in vegetation health after 2012. The rainforest zone exhibited the highest initial NDVI values, reflecting dense vegetation, but experienced significant declines due to deforestation and land-use changes. The derived savanna and mangrove zones displayed the sharpest NDVI reductions, highlighting their heightened vulnerability to agricultural expansion, urbanization, and pollution. Conversely, the freshwater swamp and Guinea savanna zones showed fluctuating NDVI patterns, with temporary recoveries attributed to favorable climatic conditions or reduced land-use intensity. Climatic variability, particularly erratic rainfall and rising temperatures, emerged as significant drivers of vegetation stress, affecting growth cycles and ecosystem resilience. Human-induced pressures, including deforestation, oil exploration, agricultural intensification, and urban development, compounded vegetation loss, disrupting ecosystem services such as carbon sequestration, water regulation, and biodiversity support. The study underscores the critical role of NDVI in monitoring vegetation health and guiding sustainable land-use practices. Recommendations include targeted conservation efforts for vulnerable zones, implementation of sustainable agricultural practices, and integration of NDVI monitoring into environmental policy frameworks. Community engagement and enforcement of land-use regulations are emphasized to balance development needs with ecological preservation.By providing a comprehensive assessment of vegetation trends and their underlying drivers, this research contributes valuable insights for environmental management and land-use planning in the South-South region of Nigeria, addressing the dual challenges of climate change and anthropogenic pressures.

In river-connected lake regions, both land use and hydrological regime changes may affect the ecosystem services; however, few studies have attempted to elucidate their complex influences. In this study, the spatiotemporal dynamics of eight ecosystem services (crop production, aquatic production, water yield, soil retention, flood regulation, water purification, net primary productivity, and habitat quality) were investigated through remote-sensing images and the InVEST model in the Dongting Lake Region during 2000–2020. Results revealed that crop and aquatic production increased significantly from 2000 to 2020, particularly in the northwestern and central regions, while soil retention and net primary productivity also improved. However, flood regulation, water purification, and habitat quality decreased, with the fastest decline in habitat quality occurring at the periphery of the Dongting Lake. Land-use types accounted for 63.3%, 53.8%, and 40.3% of spatial heterogeneity in habitat quality, flood regulation, and water purification, respectively. Land-use changes, particularly the expansion of construction land and the conversion of water bodies to cropland, led to a sharp decline in soil retention, flood regulation, water purification, net primary productivity, and habitat quality. In addition, crop production and aquatic production were higher in cultivated land and residential land, while the accompanying degradation of flood regulation, water purification, and habitat quality formed a “production-pollution-degradation” spatial coupling pattern. Furthermore, hydrological fluctuations further complicated these dynamics; wet years amplified agricultural outputs but intensified ecological degradation through spatial spillover effects. These findings underscore the need for integrated land-use and hydrological management strategies that balance human livelihoods with ecosystem resilience.

Surface Bidirectional Reflectance Distribution Function (BRDF) correction of spectral data (Li et al., 2010) has important applications to time series based analysis and classification. However, it has been reasonably proposed that the BRDF information itself can be used directly in the time series applications for land cover mapping and climate change etc. To use such data it is important to understand the characteristics of BRDF and its variation over different cover and climate conditions and how they relate to well-understood variations in spectral data in terms of the land cover characteristics and changes. Many studies have suggested that BRDF is related to the characteristics of land cover types (Brown de Colstoun and Walthall, 2006 and Jiao et al., 2011), especially to vegetation structure (height and cover) (Lovell and Graetz, 2002; Li et al., 2013) and also climate patterns. In this study, 10 years of MODIS BRDF data sets (MOD43A1) from 2002 to 2011 have been used to conduct an analysis using time series data for land cover data products available in Australia. The data have been averaged over individual years to remove the seasonal patterns and variation for reasons which were outlined in Li et al. (2013) and are briefly discussed later in this paper. Using the root mean square (RMS, the distance of the shape function from Lambertian which is a measure of its asymmetry) as a BRDF shape indicator, with the inter-annual data series the study has found that: • The average RMS for three bands (red, near-infrared and shortwave infrared) for each year is well correlated with Normalized Difference Vegetation Index (NDVI) if it is separated by land cover classes. Correlation coefficients R 2 range between 0.5-0.7. The RMS also varies significantly between land cover classes. • Inter-annual variation of RMS is small for typical vegetation classes, especially for classes with high vegetation cover. • If Normalized Difference RMS is used (called NDRMS, calculated using red and near-infrared bands and the same formula as NDVI), its relationship with NDVI is much stronger than that of RMS. Correlation coefficients R 2 are close to 0.9 for most of the years. Each land cover class has well defined NDRMS patterns. The separation is clearer than for the RMS patterns. • NDRMS seems quite sensitive to climate change as indicated by NDVI but the relationship over the 10 years in some classes is different from the overall relationship between classes averaged over all years. In vegetated classes, NDRMS has tended to increase in this way much more sensitively after the change from a long dry period to wet years, and most particularly after 2009. The sensitivity has apparently increased with class average NDVI. From the above, it has been concluded that: • Both RMS and NDRMS are able to differentiate land cover classes defined in the Australian Dynamic Land Cover Dataset (DLCD) series well. They both correlate well with spectral NDVI if the patterns are separated by land cover classes and averaged at least over individual years (removing intra-annual effects). • Both RMS and NDRMS can potentially be used as additional features to map land cover. However, NDRMS seems to be the more sensitive of the two. • However, confident and successful use of these features will need additional understanding of the sources of the variation and the information they bring compared with traditional spectral data. In particular, further studies are needed to understand the rising sensitivity in NDRMS compared with NDVI as cover and greenness increase and the previously reported (Li at al., 2013) questions concerning relative phases of intra-annual variation in NDRMS and RMS relative to NDVI.