Pemetaan Distribusi Mangrove Menggunakan Citra Sentinel-2A: Studi Kasus Kota Langsa

Mangroves are one of the most productive ecosystems for human life, marine ecosystems, and coastal areas. Mangrove distribution is a distribution based on specific geographical or administrative boundaries. Kota Langsa is one of the areas that has a good representation of the distribution of mangroves. Therefore, researchers studied the Kota Langsa area because Kota Langsa is one of the areas with the largest and most diverse mangrove ecosystem in Aceh Province. This study examines the mapping of mangrove distribution using Sentinel-2A multispectral imagery with composite images of Red, Green, and Blue. This research uses SNAP software. The research stages consist of radiometric correction, atmospheric correction, and multispectral image classification. The method used in image classification is the maximum likelihood algorithm. The use of the maximum likelihood algorithm is because the maximum likelihood algorithm gives the best results among other algorithms. The development of the research is the distribution of mangroves in Langsa City, covering an area of 4727.35 ha, which is divided into three sub-districts and eleven gampong (kelurahan). The sub-districts that have mangrove distribution are East Langsa District covering an area of 3240.25 Ha (68.55%), Langsa Barat District covering an area of 1486.47 Ha (31.45%), and Langsa Lama District covering an area of 0.63 Ha (0.013)

Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further researchEkosistem mangrove merupakan kawasan yang sangat potensial, umumnya berada di kawasan ekoton (kombinasi kawasan intertidal dan supratidal), dimana terdapat interaksi antara perairan (laut, air payau, dan sungai) dengan kawasan daratan. Indonesia khususnya wilayah Banten dan Jawa Barat memiliki kawasan mangrove yang sangat luas dan saat ini terancam alih fungsi lahan. Apalagi pemetaan sebaran hutan bakau menggunakan platform Google Earth Engine berbasis Cloud Computing kurang dipublikasikan. Oleh karena itu, penelitian ini dilakukan dengan memperkenalkan sebaran hutan mangrove yang melibatkan metode algoritma klasifikasi Random Forest (RF), dan mencari modifikasi indeks yang terbaik. Uji kombinasi dilakukan dengan melibatkan indeks NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, dan LSWI. Sebaran mangrove terdapat di tiga provinsi (Jawa Barat, Banten, dan DKI Jakarta) yaitu seluas 933,54 ha (8,372%), 1.537,89 ha (18,231%), dan 8.184,82 ha (73,397%). Dari 70 pengujian kombinasi, indeks LSWI (K13, Type-A) merupakan kombinasi dengan tingkat akurasi terendah sebesar 58,45% (Overal Accuracy) dan 39,59 (Kappa statistik), dan kombinasi K23 (SAVI-MNDWI-IBI) merupakan kombinasi yang terbaik yaitu 96,48% dan 92,79. Hasil dan rekomendasi dalam penelitian ini diharapkan dapat digunakan sebagai acuan dalam menentukan kebijakan perlindungan kawasan mangrove dan referensi untuk penelitian selanjutnya

Pemetaan kondisi hutan mangrove di kawasan pesisir Selat Madura dengan pendekatan Mangrove Health Index memanfaatkan citra satelit Sentinel-2

Abstrak. Pemetaan dan pemantauan kondisi hutan mangrove diperlukan untuk rehabilitasi dan konservasi lingkungan. Mangrove Health Index (MHI) menggunakan analisis citra satelit merupakan pendekatan baru yang bisa digunakan untuk mengetahui kualitas lingkungan ekosistem hutan mangrove. Penelitian ini bertujuan untuk untuk mengetahui struktur komunitas hutan mangrove dan melakukan analisis spasial-temporal MHI di kawasan pesisir Surabaya dan Sidoarjo menggunakan citra satelit. Data yang digunakan untuk analisis struktur komunitas mangrove pada penelitian ini adalah hasil pengamatan lapang di 10 transek. Untuk analisis MHI menggunakan citra Sentinel 2 perekaman tahun 2015, 2018, 2021. Hasil analisis menunjukkan bahwa spesies mangrove yang paling dominan di lokasi penelitian adalah Avicennia marina. Analisis citra satelit mendeteksi pertambahan luas mangrove yang signifikan dari tahun 2015 hingga 2021 yaitu lebih dari 500 Ha. Berdasarkan analisis MHI, terjadi perubahan positif dari kondisi hutan mangrove dominansi buruk (MHI 66,68%). Pertambahan luas hutan mangrove diiringi dengan perbaikan kondisi ekosistem dengan indikator meningkatnya MHI.Abstract. Mapping and monitoring the condition of mangrove forests is needed for environmental rehabilitation and conservation. Mangrove Health Index (MHI) using satellite image analysis is a new approach that can be used to determine the environmental quality of mangrove forest ecosystems. This study aims to determine the structure of the mangrove forest community and conduct a spatial and temporal MHI analysis in the coastal areas of Surabaya and Sidoarjo. The data used in this study were the results of field observations on 10 transects. MHI analysis using Sentinel 2 imagery recorded in 2015, 2018, 2021. The results of the analysis show that the most dominant mangrove species in the research location is Avicennia marina. Analysis of satellite imagery detects a significant increase in mangrove area from 2015 to 2021, which is more than 500 Ha. Based on the MHI analysis, there was a positive change from poor dominant mangrove forest conditions (MHI 66.68%). The increase in the area of mangrove forests is accompanied by improvements in ecosystem conditions with indicators of increasing MHI.

Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass meadows

Accurate habitat mapping methods are urgently required for the monitoring, conservation, and management of blue carbon ecosystems and their associated services. This study focuses on exposed intertidal seagrass meadows, which play a major role in the functioning of nearshore ecosystems. Using Sentinel-2 (S2) data, we demonstrate that satellite remote sensing can be used to map seagrass percent cover (SPC) and leaf biomass (SB), and to characterize its seasonal dynamics. In situ radiometric and biological data were acquired from three intertidal meadows of Zostera noltei along the European Atlantic coast in the summers of 2018 and 2019. This information allowed algorithms to estimate SPC and SB from a vegetation index to be developed and assessed. Importantly, a single SPC algorithm could consistently be used to study Z. noltei-dominated meadows at several sites along the European Atlantic coast. To analyze the seagrass seasonal cycle and to select images corresponding to its maximal development, a two-year S2 dataset was acquired for a French study site in Bourgneuf Bay. The po-tential of S2 to characterize the Z. noltei seasonal cycle was demonstrated for exposed intertidal meadows. The SPC map that best represented seagrass growth annual maximum was validated using in situ measurements, resulting in a root mean square difference of 14%. The SPC and SB maps displayed a patchy distribution, influenced by emersion time, mudflat topology, and seagrass growth pattern. The ability of S2 to measure the surface area of different classes of seagrass cover was investigated, and surface metrics based on seagrass areas with SPC >= 50% and SPC >= 80% were computed to estimate the interannual variation in the areal extent of the meadow. Due to the high spatial resolution (pixel size of 10 m), frequent revisit time (<= 5 days), and long-term objective of the S2 mission, S2-derived seagrass time-series are expected to contribute to current coastal ecosystem management, such as the European Water Framework Directive, but to also guide future adaptation plans to face global change in coastal areas. Finally, recommendations for future intertidal seagrass studies are proposed

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Applying multi-temporal landsat satellite data and markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.</jats:p

The global tree carrying capacity (keynote)

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Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia

Mapping and inventory of the distribution and composition of mangrove vegetation structures are crucial in managing mangrove ecosystems. The availability of airborne LiDAR remote sensing technology provides capability of mapping vegetation structures in three dimensions. It provides an alternative data source for mapping and inventory of the distribution of mangrove ecosystems. This study aims to test the ability of airborne LiDAR data to classify mangrove vegetation structures conducted in Ratai Bay, Pesawaran District, Lampung Province. The classification system applied integrates structure attributes of lifeforms, canopy height, and canopy cover percentage. Airborne LiDAR data are derived into canopy height models (CHM) and canopy cover percentage models, then grouped by examining statistics and the zonation distribution of mangroves in the study area. The results of this study show that airborne LiDAR data are able to map vegetation structures accurately. The canopy height model derived using a pit-free algorithm can represent the maximum tree height with an error range of 3.17 meters and 82.3-88.6% accuracy. On the other hand, the canopy cover percentage model using LiDAR Fraction Cover (LFC) tends to be overestimate, with an error range of 16.6% and an accuracy of 79.6-94.7%. Meanwhile, the classification results of vegetation structures show an overall accuracy of 77%

Semi-automatic classification of tree species using a combination of RGB drone imagery and mask RCNN: case study of the Highveld region in Eswatini

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTree species identification forms an integral part of biodiversity monitoring. Locating at-risk species and predicting their distribution is equally as important as tracing invasive alien plant species distributions. The high prevalence of the latter and their destructive impact on the environment is the focus for this thesis. In areas of the world where technology limitations are restrictive, an approach using low-cost, available RGB drone imagery is proposed to train advanced deep learning models to distinguish individual tree species; three dominant species (Pinus elliotti, Eucalyptus grandis and Syzygium cordatum) providing the bulk of sampling data, of which the first two are highly invasive in the region. This study explored the efficacy of utilizing Mask RCNN, an instance segmentation deep neural network, in identifying multiple classes of trees within the same image. In line with the low-cost approach, Google Colaboratory was utilized which drastically lowers the training time necessary and alleviates the need for high GPU systems. The model was trained on imagery from three study areas which were representative of three distinct landscapes: very dense forest, moderately dense forest with overlapping canopies, and open forest. The results indicate decent performance in open forest landscapes where overlapping tree crowns is infrequent with mean Average Precision of 0.71. On the contrary, in a dense forest landscape with many interlocking tree crowns, a mean Average Precision of 0.43 is highly indicative of the model’s poor performance in such environments. The trained network was also observed to have higher confidence scores of detected objects within the open forest study areas as opposed to dense forest

Mangrove forest mapping through remote sensing imagery: study case for Buenaventura, Colombia

[EN] Mangroves are plant communities of high ecological and economic importance for coastal regions. This investigation provides a methodology for mapping Mangrove forests through remote sensing images in a semidetail scale (1:25,000) in a sector of the municipality of Buenaventura, Colombia. A Sentinel 2 image and 2017 highresolution ortophotomosaic of the municipality were used for the mangrove cartography, using QGIS software, spectral analysis was performed and supervised classification was established using Maximum Likelihood algorithm. Results shown that mangrove is the most representative cover in the study area whit 7,264.21 ha in total extension (59.21% of total area), the development classification got a thematic accuracy of 80% and 0.70 in Kappa index. The used methodology can be used as an academic and research reference for mangrove semi-detail mapping in the world.[ES] Los manglares son comunidades vegetales de alta importancia ecología y económica para las regiones costeras. La presente investigación proporciona un método para determinar la cartografía de bosques manglar mediante imágenes de sensores remotos a escala 1:25.000 en un sector del municipio de Buenaventura, Colombia; para la cartografía de bosques de manglar se empleó una imagen satelital Sentinel 2 y una ortofotografía de alta resolución del año 2017; usando el software libre QGIS, se realizó los análisis espectrales, se estableció una clasificación supervisada mediante el algoritmo de máxima verosimilitud. Los resultados obtenidos muestran que la cobertura de manglar es la de mayor representatividad en el área de estudio con una extensión total de 7.264,21 ha (59,21% del área total), la clasificación desarrollada presentó una exactitud temática global de 80% e índice de Kappa de 0,70. El método empleado sirve como un referente sobre la cartografía de bosques de manglar en el mundo.Perea-Ardila, MA.; Oviedo-Barrero, F.; Leal-Villamil, J. (2019). Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso Buenaventura, Colombia. Revista de Teledetección. (53):73-86. https://doi.org/10.4995/raet.2019.11684SWORD73865