Depth Invariant Index Research Articles

The Assessment of Coral Reefs Mapping Methodology: An Integrated Method Approach

Indonesian waters hold the world’s mega biodiversity of coral reefs. However, a range of anthropogenic pressures are threatening the coral reefs persistence. Since the early 20th century, remote sensing data has been assessed to map and monitor coral reefs. The reef habitats are monitored at various hierarchical spatial scales using integrated remote sensing and field data, but the level of detail and accuracy at a single point still questionable. Therefore, this study aims to assess the coral reefs methodology based on an integrated digital image processing approach. The method will employ a multi-pair and a single pair or an initial pair of Depth Invariant Index (DII) transformation bands, pixel-based Isodata and K-Means algorithm, and supervised classification method based on maximum likelihood and nearest neighbor algorithms. Object-based classification images, training areas, and data references were supported by previous research. The findings indicate that the maximum likelihood algorithm is better to apply for supervised classification for a single transformation band, while the K-Means algorithm is better for pixel-based classification since better accuracy can be obtained. However, various remote sensing data, band combinations, and clusters may affect the difference in results.

Water-depth-zoning inversion based on the relationship between two-band radiance data and the depth-invariant index

Water depth is important in hydrology and is often estimated by remote sensing. Studies have shown that different substrates have a certain influence on satellite derived bathymetry (SDB). In this work, the depth is estimated after the depth-invariant index used to perform substrate classification. This paper proposes a new depth-invariant index based on the relationship of dual-band radiance, and it is compared with the depth-invariance index proposed by Lyzenga (1978) (note that: the index is not used for SDB). Experiments is carried out in shallow water around North Island and East Island of Xisha Islands. Results show that for the two study sites, application of the indexes provide good classification results and reduce the estimation error. The classification accuracy of the proposed index is 2.90% and 0.38% higher than that of the Lyzenga’s model for the two sites, respectively. Compared with the mean absolute error of depth estimation without classification, that of SDB after classification with the proposed index is reduced by 0.10 m and 0.07 m for the two substrate types, while the estimation error of SDB after classification with Lyzenga’s model is 0.11 m and 0.10 m lower than that of the unclassified. Estimation accuracy of water depth over coral reefs showed great improvement, but over coral sands, it showed only slight improvement when compared with SDB based on the unclassified.

Detection of Coral Reef Bleaching Based on Sentinel-2 Multi-Temporal Imagery: Simulation and Case Study

Sentinel-2 mission has been shown to have promising applications in coral reef remote sensing because of its superior properties. It has a 5-day revisit time, spatial resolution of 10 m, free data, etc. In this study, Sentinel-2 imagery was investigated for bleaching detection through simulations and a case study over the Lizard Island, Australia. The spectral and image simulations based on the semianalytical (SA) model and the sensor spectral response function, respectively, confirmed that coral bleaching cannot be detected only using one image, and the change analysis was proposed for detection because there will be a featured change signal for bleached corals. Band 2 of Sentinel-2 is superior to its other bands for the overall consideration of signal attenuation and spatial resolution. However, the detection capability of Sentinel-2 is still limited by the water depth. With rapid signal attenuation due to the water absorption effect, the applicable water depth for bleaching detection was recommended to be less than 10 m. The change analysis was conducted using two methods: one radiometric normalization with pseudo invariant features (PIFs) and the other with multi-temporal depth invariant indices (DII). The former performed better than the latter in terms of classification. The bleached corals maps obtained using the PIFs and DII approaches had an overall accuracy of 88.9 and 57.1%, respectively. Compared with the change analysis based on two dated images, the use of a third image that recorded the spectral signals of recovered corals or corals overgrown by algae after bleaching significantly improved the detection accuracy. All the preliminary results of this article will aid in the future studies on coral bleaching detection based on remote sensing.

Application of PlanetScope-based Depth Invariant Index method in Seagrass Mapping: The study in Thi Nai Lagoon, Binh Dinh Province

The seagrass ecosystem is one of the most critical coastal and marine ecologies. The seagrass meadows can protect the seabed coastal erosion, absorb nutrients from coastal runoff and stabilize sediments, act as bio-filters, and provide a habitat for marine animals. Nevertheless, under the impact of socio-economic growth, seagrass in the Thi Nai lagoon has disappeared and been severely deteriorated. Therefore, mapping seagrass in this area is important in informing ecologists and economists to improve sustainable marine ecosystem management strategies. This study aims to detect seagrass in Thi Nai lagoon, Vietnam, by remote sensing method. PlanetScope (PS) satellite images have high spatial (3m) resolution that is applied to extract seagrass using Lyzenga's Depth Invariance Index technique for water column correction and Maximum Likelihood Classifier (MLC). After that, evaluate the accuracy of the results using field data collected from July 29, 2020, to August 1, 2021. The study results achieved high accuracy in isolating seagrass subjects with an overall accuracy of 85.09% and Cohen's Kappa value of 0.8. There are approximately 180 hectares of seagrass in the study area. Moreover, the results also show that seagrasses are mainly distributed on the sand, muddy sand, and sandy mud along the west shore of Thi Nai lagoon and dunes such as Con Chim, Con Trang, and Con Tau, with depths ranging from 1 to 2.5 meters (90.97 %).

Mapping and estimating harvest potential of seaweed culture using Worldview-2 Satellite images: a case study in Nusa Lembongan, Bali − Indonesia

Unreliable information on harvest potential is a persistent challenge for the Indonesian government and industry alike to manage an efficient supply chain of seaweed raw material. The use of remote sensing technology to assess seaweed harvest potential has been scarcely available in the literature. This current research aimed at estimating the harvest potential of seaweed Kappaphycus alvarezii through remote sensing using supervised classification with maximum likelihood (MLC) and contextual editing (CE) methods. This research evaluated the capabilities of different band combinations along with depth invariant index (DII) to enhance the remote sensing accuracy in estimating seaweed harvest potential. The seaweed classification using Worldview-2 imagery was compared with the in-situ references (ground-truthing). The potential data bias resulted from different imagery acquisition timestamps with the in-situ measurement was kept minimal as both data time stamps were ten days apart and within the same seaweed culture cycle. The average dry weight of all seaweed samples collected during the research was 924 ± 278.91 g/m2 with culture ages between 1 and 40 days. The classification results based on MLC+CE with a 5-band combination method without DII showed a better correlation and closer fit with the in-situ references compared to the other methods, with an overall accuracy of 79.05% and Tau coefficient value of 0.75. The estimated total harvest potential based on the combined seaweed classes was 531.26 ± 250.29 tons dry weight.

Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

Abstract Rabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R 2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R 2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R 2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

Monitoring benthic habitats using Lyzenga model features from Landsat multi-temporal images in Google Earth Engine

Benthic habitats have conventionally been monitored through physical site visits by diving. This requires the use of expensive resources and time to achieve the desired results. This study adopts remotely sensed Landsat multi-temporal images to monitor benthic habitats within marine protected areas in Kenya. We adopt the Lyzenga approach to compute depth invariant index features on Google Earth Engine (GEE) API to aid benthic classification. Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms were used for the supervised classification of benthic habitats based on training classes selected under the guidance of an initial K-means unsupervised classification. RF had a higher overall accuracy of 83.48% compared to SVM at 52.69% as per 2015 reference data. We, therefore, used RF to map benthic habitats between 2003 and 2018. Findings indicate that there are high conversions between corals, seagrass and sand benthic classes. Since environmental and anthropogenic pressures act synergistically in causing changes, an in-depth research would be necessary to assess the vulnerability of benthic habitats to these factors. However, the use of multi-spectral imagery and powerful GEE cloud computing platform can aid monitoring and acquisition of marine data.

Comparison of seagrass cover from multi-scale imagery

Mapping of seagrass bed can be done by using different characteristics of satellite images data such as Landsat-8 and Sentinel-2. However, there has been no research compared to the results from those images in Indonesia. This study aims to analyze and compare spatially the condition of seagrass bed from Landsat-8 and Sentinel-2 imagery covered Kodingareng Lompo island. Spatially sharpened (Pan-sharpening) Landsat-8 imagery from 30 to 15 meters resolution in four spectral bands (visible and NIR) were used in this case. The results showed the four classes seagrass cover percentage consisted of sparse class (0 – 25%), medium-class (26 – 50%), dense class (51 – 75%), and very dense class (76 – 100%). Classification of seagrass cover class and water column correction (Depth Invariant Index) value differed between the two imageries. Accuracy test of seagrass cover classification from Landsat-8 (88.1%) and Sentinel-2 (89.2%) were both suitable to use since they were higher than the standard value (>85%). Differences in the characteristics of both images cause differences in the results of the seagrass cover classification.

Tracking Decadal Changes in Palk Bay Coral Community Structure: A Multivariate Approach

The study assessed a decade long changes of the coral community structure in the Palk Bay region using multivariate analysis based on observed coral life-forms and its diversity between the years, 2002 and 2018. A total of 11 study sites were selected from Rameswaram Island to Mandapam coast. Live coral cover was found to be 27% (21% non-Acropora and 6% Acropora corals) during the study period 2002–2005. Principal component analysis (PCA) showed that there was a significant variability (PC1: 39.5% variance) in coral cover between the study site, Bison House and the rest of the study sites. In the study of 2014–2018, live coral cover observed was 17% and this included 11% non-Acropora and 6% Acropora corals. PCA showed that the contribution of live coral cover gradually reduced towards the northern part of Rameswaram Island. Metric multi-dimensional Scaling (MDS) showed that there were four clusters formed between the sites with 80% similarity as observed based on the life-form categories. There were three major clusters with 70% similarity observed based on coral diversity. The highest diversity index (Shannon-wiener) was observed at Olaikudah and Villunditheertham as 1.906 and 1.864 respectively. As of 2014–2018, the live coral status showed 37% less than that found during 2002–2005 study period. Variation of reef flat composition was studied using Depth-Invariant Index for the study period 2002–2005. This study has helped to understand the longitudinal gradient of incidence, temporal variations, and distribution pattern of coral reef community categories in the Palk Bay region.

ANALISIS PERUBAHAN HABITAT DASAR PERAIRAN DANGKAL MENGGUNAKAN CITRA SATELIT RESOLUSI TINGGI DI KARANG LEBAR, KEPULAUAN SERIBU

Keperluan data dan informasi tentang habitat bentik sangat diperlukan untuk menjaga dan melestarikan ekosistem yang ada di perairan. Rusaknya habitat bentik dapat terjadi karena adanya aktifitas antropogenik dan bencana alam yang akan berimbas pada biota dan ekosistem yang ada disekitarnya, oleh karena itu untuk mengetahui dan memantau kondisi perairan dan habitat perairan dangkal perlu dilakukannya pemetaan. Penelitian ini bertujuan untuk menganalisis perubahan habitat dasar perairan laut dangkal di Karang Lebar, Kepulauan Seribu. Penelitian ini memanfaatkan citra multispektral resolusi tinggi QuickBird 2008 dan WordView-2 2018 untuk mendeteksi perubahan geospasial habitat bentik. Klasifikasi citra multispektral dilakukan dengan penerapan algoritma SVM (Support Vector Machine) dan transformasi DII (Depth Invariant Index) pada kedua citra yang digunakan. Jumlah kelas habitat bentik yang dihasilkan adalah sebanyak lima kelas yaitu terumbu karang, karang mati, padang lamun, pasir, dan rubble. Hasil analisis menunjukkan akurasi keseluruhan 58,18% dan 70,9% pada penerapan klasifikasi dengan input band multispektral masing-masing untuk citra 2008 dan 2018, serta 60% dan 80% pada hasil transformasi DII masing masing untuk citra 2008 dan 2018. Perubahan kelas rubble di tahun 2008 menjadi kelas pasir tahun 2018 merupakan yang paling besar luasannya dibandingkan dengan perubahan pada kelas lainnya, yaitu 81,46 ha.

Benthic habitat classification using high resolution satellite imagery in Sebaru Besar Island, Kepulauan Seribu

Remote sensing technology can provide spatial information for mapping shallow water benthic habitat, a case study conducted on Sebaru Besar Island. The purpose of this study was to analyze mapping accuracy of shallow water benthic habitats usings WorldView 2 and SPOT 6 (201 imageries). The classification of multispectral images is carried out using the Depth Invariant Index (DII) transformation and by applying the Maximum Likelihood (MLH) algorithm to both satellite images. The number of benthic habitat classes produced are eight habitat classes from each image used. The results of the analysis show that the overall accuracy in Worldview 2 and SPOT 6 images is 61.29% and 51.61%. Results of Z-statistic comparison between Worldview-2 and SPOT-6 imagery was 1,04, means that the results did not differ significantly.

Study on spatial distribution of coral reefs in Nam Yet island by using GIS and remote sensing techniques

Research and application of GIS and remote sensing techniques combined with field survey in coastal areas of Nam Yet island had been carried out to establish the distribution map of submarine habitats. Depth-invariant index was used to correct water column’s effects on spectral reflectance of each habitat. The results of satellite image classification showed that area with well-developed coral at great depths accounted for 12%, area with well-developed coral at small depths accounted for 9%, area with poorly-developed coral accounted for 13%, dead coral area accounted for 15% and area of sand, grit, pebbles and weathered coral accounted for 51%. The assessment after classification showed that the overall accuracy of the satellite image interpretation process was 94% and the kappa coefficient was 0.93.

SATELLITE-BASED MAPPING OF ABOVE-GROUND BLUE CARBON STORAGE IN SEAGRASS HABITAT WITHIN THE SHALLOW COASTAL WATER

Abstract. These Mapping and estimation of seagrass total above-ground carbon (STAGC) using satellite-based techniques are required to fast-track the achievement of the 2020 agenda on Sustainable Development Goals (SDG) 14th. This attainment is possible as seagrass habitats provide a critical coastal ecosystem for storing blue carbon stock, sediment accumulation, fisheries production and stabilisation of coastal environment. However, seagrasses are generally declining across the globe due to anthropogenic disturbance, resulting in a prolonged growth rate of seagrasses that varies according to the species compositions. Therefore, this study aims at mapping and estimation of seagrass total above-ground carbon (STAGC) using Landsat ETM+ in the coastline of Penang. These satellite images were calibrated with Bottom Reflected Index (BRI) and Depth Invariant Index (DII) to compare the estimate of the STAGC for more accuracy. The leaving radiances of the seagrass were correlated with the corresponding in-situ measurements to predict seagrass carbon. This established relationship with BRI image shown a healthy correlation with STAGB (R2 = 0.992, p ≤ 0.001). Whereas the STAGB versus DII relationship has less accuracy (R2 = 0.955, p ≤ 0.01), adjusted R2 = 0.980 and 0.978 were recorded for both BRI and DII STAGC estimate using the logistic model. Therefore, careful management of blue carbon stock is essential, as this study shall contribute to achieving targets 14.2 and 14.5 of SDG 14th by the United Nations.

ACCURACY OF UNSUPERVISED CLASSIFICATION TO DETERMINE CORAL HEALTH USING SPOT-6 AND SENTINEL-2A

Abstract. Characteristics of corals spectral from different species are expected to have optically different characters. The aims of this research are to compare unsupervised classification between IsoData and K-Means methods with Lyzenga application, and to analyze the precision of SPOT-6 and Sentinel-2A satellite imagery in classsifying shallow water habitat. The image processing are atmosferic correction, cropping, masking, Depth Invariant Index, Unsupervised classification, ground truthing, reclassify, accuracy assessment, and shallow water habitat spectral reflectance analysis. Rubble and dead coral with algae were indicating as coral death due to either damaging human activity or natural death such as bleaching. The accuracy of unsupervised classification IsoData and K-Means method have the same accuracy 62.50%. The IsoData method is better detected live coral and algae. Rubble were dominant detected in K-Means method.

LANDSAT CAPABILITY TO MAPPING OF TOTAL SOLID SUSPENDED (TSS) AND COASTAL ECOSYSTEM

Abstract. The purpose of this study was to analyze the distribution dynamics and TSS concentrations based on four different seasons and different years for five years. Image processing of TSS included Correction with Dark Object Substraction and Atmospheric correction method, and Syarif Budhiman algorithm used to extract TSS. The image processing of coastal ecosystem was composed of eight main steps including: atmospheric correction, cropping, masking, depth invariant index, Unsupervised Classification, ground truthing, reclassify, and accuracy assessment. The distribution of suspended sediments is the highest and dominant content in the coastal area adjacent to the river estuary. In addition, based on quality standards for marine biota and TSS values for fisheries purposes, it can be revealed that the fertility of Spermonde coastal waters crosses the quality standard and is not good for fisheries activities.

Analysis of Coral Reef Area Based on Landsat 8 Satellite Image Data Processing using the Lyzenga Algorithm

Indonesia has the potential of invaluable biological resources from an economic and ecological perspective, one of which is coral reef resources. To determine the condition of the coral reef ecosystem in Indonesian waters as an effort to monitor and monitor using remote sensing data. The purpose of this research is the creation of an information system through a website that can help users access information effectively and efficiently.Utilization of Landsat 8 satellite image data to observe the bottom of the waters directly on coral reef mapping using the Lyzenga Algorithm method, also known as the depthinvariant index method or the water column correction method. This method produces a baseline index that is not affected by depth and works well in clear shallow waters such as in coral reef habitat areas (Maritorena, 1996). The results showed that the image processing data had a map accuracy level for Menjangan Besar and Menjangan Kecil islands of 77.78%, while the map accuracy level for Karimunjawa and Kemujang Islands was 72.23%. And from the processed data, it is known that coral reef cover in general has increased from 2014 to 2018, for Karimunjawa and Kemujan Islands an increase of 17.4% for Menjangan Besar and Menjangan Kecil Islands only a slight increase of 0.27% and for Bunaken Island an increase of 9.1%. And there is one island that is processed has decreased, namely Bunaken Island by 4.5%.

Sentinel-2 for mapping the spatio-temporal development of submerged aquatic vegetation at Lake Starnberg (Germany)

Submerged aquatic vegetation (SAV) plays an important role in freshwater lake ecosystems. Due to its sensitivity to environmental changes, several SAV species serve as bioindicators for the trophic state of freshwater lakes. Variations in water temperature, light availability and nutrient concentration affect SAV growth and species composition. To monitor the trophic state as required by the European Water Framework Directive (WFD), SAV needs to be monitored regularly. This study analyses the development of macrophyte patches at Lake Starnberg, Germany, by exploring four Sentinel-2A acquired within the main growing season in August and September 2015. Two different methods of littoral bottom coverage assessment are compared, i.e. a semi-empirical method using depth-invariant indices and a physically based, bio-optical method using WASI-2D (Water Colour Simulator). For a precise Sentinel-2 imaging by date and hour, satellite measurements were supported by lake bottom spectra delivered by in situ data based reflectance models. Both methods identified vegetated and non-vegetated patches in shallow water areas. Furthermore, tall- and meadow-growing SAV growth classes could be differentiated. Both methods revealed similar results when focusing on the identification of sediment and SAV patches (R² from 0.56 to 0.81), but not for a differentiation on SAV class growth level (R² <0.42).

Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%.

The Ability of Lyzenga’s Algorithm for Seagrass Mapping using Sentinel-2A Imagery on Small Island, Spermonde Archipelago, Indonesia

The existence of the value of depth variation in the study area is also a requirement in the application of this algorithm. The purpose of this study is to determine the ability of the use of algorithms in the mapping of seagrass by comparing the results of classification between imagery using the Lyzenga’s algorithm with imagery without using the Lyzenga’s algorithm. The result of imagery classification applying the Lyzenga’s algorithm shows the visibility of baseline water objects that are more easily recognizable using Depth Invariant Index (DII) value format as evidenced by the increase of 2 classes of seagrass cover. In this study there are five seagrass cover classes of shallow water in Pajenekang Island, Spermonde-Indonesia Islands using Lyzenga’s algorithm, namely class are 1) Very good: > 75%; 2) Good: 50,575,4%; 3) Rather Good: 25,5-50,4%; 4) Bad: 5,5-25,4%; and 5) Very Bad: <5.5%.

Mapping shallow waters habitats using OBIA by applying several approaches of depth invariant index in North Kepulauan Seribu

The availability of satellite imagery with a variety of spatial resolution, both free access and commercial become as an option in utilizing the remote sensing technology. Variability of the water column is one of the factors affecting the interpretation results when mapping marine shallow waters. This study aimed to evaluate the influence of water column correction (depth-invariant index) on the accuracy of shallow water habitat classification results using OBIA. This study was conducted in North of Kepulauan Seribu, precisely in Harapan Island and its surrounding areas. Habitat class schemes were based on field observations, which were then used to build habitat classes on satellite imagery. The water column correction was applied to the three pairs of SPOT-7 multispectral bands, which were subsequently used in object-based classification. Satellite image classification was performed with four different approaches, namely (i) using DII transformed bands with single pair band input (B1B2), (ii) multi pairs bands (B1B2, B1B3, and B2B3), (iii) combination of multi pairs band and initial bands, and (iv) only using initial bands. The accuracy test results of the four inputs show the values of Overall Accuracy and Kappa Statistics, respectively 55.84 and 0.48; 68.53 and 0.64; 78.68 and 0.76; 77.66 and 0.74. It shows that the best results when using DII and initial band combination for shallow water benthic classification in this study site.