Changes Detection of Mangrove Ecosystembased on Obia Method in Liong River, Bengkalis Riau Province

Status of mangrove ecosystem on Liong River, Bengkalis Island, Riau Province, is currently in a condition that tends to get a stressed doe to 60% of indigenous people living around mangroves are loggers. Series Landsat is used as recording data to map the mangrove and to see the changes in the region. This study aims to map changes in mangrove ecosystems from 1990 - 2017 using the OBIA method. The field observation was done using Unmanned Aerial Vehicle (UAV). The results showed that mangrove area has decreased every year. It was caused by anthropogenic and natural factors. Approximately 4.2% of mangrove decrease from 1990 to 2017 and mangrove highest exploitation occurred in 2007 with a decline of 31.5%

Mapping of Mangrove Distribution in Percut Sei Tuan Sub-District Deli Serdang Regency

Mangroves are coastal ecosystems that are rich in diversity of flora and fauna. Mangroves have an important role in coastal areas in terms of ecology, biology, and economy. However, mangrove management has not balanced these three aspects and tends to be exploited for economic purposes. The purpose of this study was to map the distribution of mangroves, identify changes and zoning of mangroves in the Percut Sei Tuan subdistrict. The method used in this research is a survey method. Collecting data from Landsat Multitemporal images in 2017 and 2021 as well as field data through observation. Data analysis was performed using Envi and Arc GIS software. The results showed that the distribution of mangroves was found along the coast of Percut Sei Tuan District. The decline in mangrove ecosystems occurred in the period 2017 and 2021. Mangrove zoning in this area is in accordance with other mangrove zoning in Indonesia, which consists of open mangroves, transitional mangroves, brackish mangroves, and mangroves close to the mainland. The diversity of mangrove species in the form of true mangroves and associated mangroves was also found on the coast of Percut Sei Tuan District

Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan Citra Sentinel-2b di Sungai Liong, Bengkalis, Provinsi Riau

Penelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutru-turut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek

INDIVIDUAL COMMUNITY CHARACTERIZATION RELATIONSHIP TO PARTICIPATION IN MANGROVE FOREST MANAGEMENT

Mangrove forests as a potential natural resource have many benefits that make them managed and always utilized by the community. However, the current condition of mangrove forests is very concerning and has suffered damage, one of the causes of which is human activities and activities. This study aims to find out the relationship of individual characteristics of the community to participation in mangrove forest management. Respondents to this study were 35 people who were members of community groups in Margasari Village. The stages and methods carried out in this study are: determination of the weight and score of individual characteristics of community and community participation using the Likert scale method with questionnaire instruments; and analysis of the relationship of individual characteristics of community to participation using the spearman rank method. The results showed that the community has actively carried out mangrove forest utilization and management activities in Margasari Village. This can be seen in the conditions of individual characteristics of community. People who are of productive age can carry out and carry out a job and activities optimally. In addition, the level of community activity can be seen in 90% of respondents who were educated last at the high school level, which shows that the mindset, absorption, and knowledge related to mangrove forests and their management are quite good. This study concludes that the individual characteristics of community do not indicate a close and significant relationship to community participation. Thus, the changes that occur in the individual characteristics of the community will not change the pattern of community activities and activities in the management of mangrove forests in Margasari Village

KLASIFIKASI MANGROVE BERBASIS OBJEK DAN PIKSEL MENGGUNAKAN CITRA SENTINEL-2B DI SUNGAI LIONG, BENGKALIS, PROVINSI RIAU

ABSTRAK Penelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan  (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutru-turut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek. ABSTRACTResearch on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community

Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions  increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level

Determination of Mangrove Adequacy as Natural Coastal Defence Against Wave Dynamics Using Remote Sensing Analysis for Southern Kedah Coastline

Malaysia had been hit by the Indian Ocean Tsunami (IOT) 2004 which has took 68 lives of Malaysia and thousands lives from the other affected countries. Kedah was one of the most affected states in northern Peninsular Malaysia. It is proved that less severe damaged found at area with presence of mangroves fringe on its coastline. It also has been proven by many researches on mangroves’ abilities in protecting the coastline by reducing the impact of the wave actions. However, if the Peninsular Malaysia is about to hit by tsunami again, it is unidentified if the current mangrove forest along the coastlines are able to reduce the impact of the tsunami’s waves just like how they did before. Therefore, this study aims to fully acknowledged and assess current condition of the mangrove forest along the Kedah’s coastline and evaluate their performances as the reliable natural coastal protector towards wave actions

Effect of the net radiation substitutes on maize and soybean evapotranspiration estimation using machine learning methods

La estimación precisa de la evapotranspiración (ET) es esencial para gestionar agua en cultivos, pero no es una tarea fácil. Las metodologías empíricas de ET requieren mediciones precisas de la radiación neta (Rn) para obtener resultados confiables. Sin embargo, estas mediciones no son rutinarias en las estaciones meteorológicas. Este trabajo exploró el uso de aprendizaje automático para estimar la ET diaria con dos sustitutos de Rn: la radiación solar extraterrestre (Ra) y la Rn modelada (RnM). Se utilizó Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB) y Multilayer Perceptron (MLP) para modelar observaciones de FLUXNET. Adaptive Boosting brindó los mejores resultados con observaciones de Rn (RnO), con un valor para la raíz del error cuadrático medio de aproximadamente el 16 % de Rn medio observado. La Rn resultante (AB RnM) se utilizó para modelar la ET, usando RnO, AB RnM y Ra, junto a variables meteorológicas y el índice NDVI. Los métodos evaluados estimaron adecuadamente la ET, arrojando errores similares a los obtenidos con RnO, cuando se contrastan con las observaciones de ET. Estos resultados demuestran que AB y KR son aplicables con datos rutinarios meteorológicos y de satélite para estimar la ET.Accurate evapotranspiration (ET) estimation is essential for water management in crops, but it is not an easy task. Empirical ET methodologies require precise net radiation (Rn) measurements to obtain accurate results. Nevertheless, Rn measurements are not easy to obtain from meteorological stations. Thus, this study explored the use of machine learning algorithms with two Rn substitutes, to estimate daily ET: the extraterrestrial solar radiation (Ra) and a modelled Rn (RnM). Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB), and Multilayer Perceptron (MLP) were applied to model FLUXNET Rn and ET observations. Adaptive Boosting produced the best field Rn measurements (RnO), yielding a Root Mean Square Error of about 16 % of the mean observed Rn. The resulting Rn (AB RnM) was used to model daily crops ET employing the above-mentioned machine learning methods with RnO, AB RnM, and Ra, in conjunction with meteorological variables and the NDVI index. The evaluated methods were suitable to estimate ET, yielding similar errors to those obtained with RnO, when contrasted with ET observations. These results demonstrate that AB and KR are applicable with rutinary meteorological and satellite data to estimate ET.Fil: Venturini, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Walker, Elisabet. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Fonnegra Mora, Diana Carolina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Fagioli, Gianfranco. Kilimo S.a; Argentin

Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yang, X., Zhu, Z., Qiu, S., Kroeger, K. D., Zhu, Z., & Covington, S. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sensing of Environment, 276, (2022): 113047, https://doi.org/10.1016/j.rse.2022.113047.Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.This study was supported by USGS North Atlantic Coast Cooperative Ecosystem Studies Unit (CESU) Program for Detection and Characterization of Coastal Tidal Wetland Change (G19AC00354)