Remote Sensing and Mangrove

La producción acuícola ecuatoriana ha tenido crecimiento exponencial a los mercados internacionales, causando de manera directa un incremento en la extensión de hectáreas deforestadas especialmente en las zonas costeras de manglar del país. El objetivo de este estudio fue mapear y estimar el impacto que ha producido la deforestación producto de la actividad camaronera en los manglares del Golfo de Guayaquil, Ecuador. Para ello, se realizó un análisis espacial con imágenes satelitales Landsat de 1985 y 2017, se utilizó el índice espectral NDVI para mapear la cobertura vegetal y NDWI para mapear e identificar las piscinas camaroneras. Además, se realizó un análisis bibliográfico para conocer el posible impacto negativo de la deforestación. Los resultados obtenidos nos permitieron mapear las piscinas camaroneras, convirtiéndose en una posible herramienta para monitorear nuevas actividades de deforestación ilegal. La afectación directa se da en la red trófica produciendo afectaciones en la población que se dedica a la pesca artesanal de ictiofauna, malacofauna y carcinofauna, además degrada la barrera natural contra la
erosión y la asimilación de carbono en el planeta aumentando así el efecto invernadero. Ecuador ha perdido aproximadamente 27% del manglar original, especialmente por la producción acuícola, el urbanismo y extracción de recursos naturales en esta región.

This is a power point presenting the key characteristics of the mangrove such as its physical and biological specific traits, propagation, growth, varieties, and ecosystem. It also presents the global distribution, environmental impact, and importance.
At the end of the presentation, it shows few projects dedicated for adopting the specie and the projects' concepts and developments

Sweeping across Bangladesh and India, the Sundarbans forest is the world's largest contiguous mangrove forest.
Although the human population density is high at the edge, Sundarbans has not encountered significant areal
transformation in the last four decades. However, we argue that forest degradation can occur discontinuously
within the forest without alteration of the entire forest area. In this paper, we used MODIS land products to compare the spatiotemporal ecological dynamics of the Bangladesh and Indian part of this mangrove forest between
2000 and 2010. We used the following 5 ecological parameters for our analysis: the Percent Tree Cover (PTC), Enhanced Vegetation Index (EVI), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Evapotranspiration
(ET). Our pixel-based time-series trend analysis for each MODIS image stack, using an ordinary least square
(OLS) regression method, showed that forest degradation is happening in fragmented parcels within the forest.
The degradation rate is comparatively higher in the Bangladesh part than in the Indian part of Sundarbans. Compartments 8, 10, 12, and 15 in the Bangladesh part, in particular, show high degradation, while compartment 48
and the southern edge of 45 show slight increases in PTC or EVI. Forest degradation in the Indian part of the forest
is evident in the National Park and Reserve Forest blocks; however, no substantial degradation is evident in the
western section. We have identified certain anthropogenic stressors (i.e., oil pollution, shrimp farming) and natural stressors (i.e., increased salinity, cyclones, forest fire) which might be responsible for the observed degradation. We have provided sustainable planning options and policy transformation alternatives for those areas under
pressure from these stressors. We anticipate that our analysis of forest degradation will help management agencies, conservators, and policy makers achieve better management of this world's largest mangrove forest for a sustainable future

The mangrove is one of the most endangered ecosystems on the planet. Indeed, despite all the benefits that it recognizes, namely the reduction of the risk of coastal erosion, the breeding and nursery grounds for fish or the sequestration of carbon, nevertheless are alarming loss of mangrove area in the world. This study focuses on the establishment of a GIS and an analysis of the dynamics and typology of mangroves in Libreville and its surroundings. Landsat high spatial resolution satellite imagery (30 m) mapped the dynamics of mangroves and land cover. As a result, mangroves evolve in crescendo and decrescendo in time and space. The mangrove areas of the selected dates are: 920 km², 1051.2 km², 997.3 km² and 1048.64 km², respectively in 1990, 2000, 2014 and 2018. This change is partly due to the cloudy disturbance of the different images and, above all, the ever-increasing evolution of the built-up space, and therefore human and also industrial pressures. In order to analyze the typology of mangroves in Libreville and its surroundings, three maps were combined (mangrove species, mangrove height and mangrove cover). This analysis is a prerequisite in the study of the estimation of mangrove biomass. The results produced were introduced in a GIS with ArcGis, which itself should be managed by an environmental observatory.

Studies in mangrove ecosystem are often focused on biological or ecological criteria and interdependency between mangroves and people is normally neglected. The situation is similar in Tamil Nadu; India which has a coastline of about 950 km. One of the major mangrove forests in Tamil Nadu is situated in Pichavaram, Cuddalore district. The present study was carried out in the seventeen hamlets, which are directly or indirectly dependent on the Pichavaram mangrove wetlands for their livelihood and survival. These seventeen hamlets consist of over 2600 households many of whom derive their principal income from fishing and related activities. Individual surveys were carried out for 10% of the households in each of the selected hamlets. Semi-structured questionnaires were used for surveys to study the attitude and perception of the community on the conservation and importance of mangrove wetlands and resources. The study was conducted to assess the awareness, attitudes and views of people dependent on the mangrove ecosystem towards conservation issues and development options. It was observed that a large percentage of the sampled population showed a positive inclination towards conservation of the ecosystem and were well aware of their responsibility towards it.

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R2 = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha−1 (average = 87.67 Mg ha−1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.

It is so important to be aware of quantitative and qualitative characteristics of changes for the environment, land use planning and sustainable development. Detecting the changes in the condition of an issue is done by time difference observations. Change detection refers to the process of detecting time changes of an object through different time observations. The detection of changes by satellite imageries has come recently to the focus of attention due to their comprehensive and integrated characteristics and their ability to monitor changes during long periods as well as their application to monitoring and controlling changes in the forest ecosystems. The vegetation maps are now used to generate required information for macro and micro planning. This study was done to monitor the changes in the forest of Golestan province in the past and also to investigate the possibility of its future forecast using the Land Change Modeler (LMC). A forest type map was first prepared to monitor changes in forests of Golestan province from 2000 to 2015. The images taken in 12 months during 2000 and 2015 were collected form MODIS satellite imageries to monitor the forest. After pre-processing and preparing the time series in two sections, the forest changes were considered using the Normalized Difference Vegetation Index and Moisture Stress Index (NDVI and MSI). The changes were then classified by indices, including the lands with excellent, very good, medium and poor coverage and compared together. The accuracy of the classification results was assessed using the field maps. The best result was found in the average time series data and the use of the NDVI index was prioritized over other indices. The forecast of changes by 2030 as 0.8629 Kappa and the model results for 2030 indicated a decline of 70000 in the land with high and very high capacities and a further increase in land with a low capacity.

Mangroves provide a natural solution for sequestering atmospheric carbon. Quantifying the potential carbon storage in mangrove ecosystems is crucial in harnessing mangrove’s function for climate change mitigation. The objective of this study is to assess the carbon stock of mangrove vegetation in Infanta, Quezon through remote sensing. To determine the spatiotemporal changes in mangrove area by vegetation density (bare, sparse, and dense) of different barangays, Normalized Difference Vegetation Index (NDVI) analysis was done using corrected satellite images through ArcGIS. The annual rate of change (%) of mangrove forests was computed. Accuracies of the NDVI images were calculated using semi-automatic classification plugin (SCP) of QGIS, which showed a high accuracy of the classification process. Secondary data on aboveground mangrove carbon stocks in San Juan, Batangas, and the computed mangrove area of Infanta were utilized in the computation of aboveground carbon stock and its corresponding CO2 equivalent. Over the 20-year study period, dense mangrove cover changed by –11.7%. Although there was an increase in dense mangroves of 2.6% over the 2000–2010 period mainly in Barangay Binonoan and Dinahican, the following decade (2010–2020) recorded a decrease of –14.2% implying that gradual mangrove deforestation or degradation persisted outweighing the reforestation efforts of various agencies in the study area. For the municipality as a whole, the estimated aboveground carbon stock for the years 2000, 2010, and 2020 were 304.3 Gg C, 310.7 Gg C, and 286.6 Gg C, respectively with corresponding translated equivalent values of 1,115 Gg CO2, 1,139.4 Gg CO2, and 1,050.8 Gg CO2. Mangroves of Infanta are worth at least USD 4.5 million at USD 4.3 ton–1 CO2 (prevailing market price as of 2020). As a result, mangrove forests in the research area should be continuously conserved to ensure sustained carbon sequestration and other equally important ecosystem services.

Selasa, 12 Desember 1992 terjadi bencana alam nasional akibat gempa bumi di bawah laut dengan magnitudo 6,8 SR yang akhirnya menyebabkan tsunami. Tsunami yang muncul menyebabkan bencana yang cukup besar di sepanjang Pantai Utara Pulau Flores. Dampak besar dari tsunami tersebut adalah korban jiwa sekitar 2.000 orang dan kerugian harta benda. Tsunami juga menghabiskan luasan hutan mangrove di Pulau Flores tepatnya di Flores bagian Utara yaitu Flores Timur dan Maumere. Tujuan dari penelitian ini yaitu untuk mengetahui perubahan tutupan mangrove multi temporal, di Pantai Utara Flores Timur sebelum dan sesudah tsunami berbasis Sistem Informasi Geografis (SIG) dengan menggunakan metode OBIS (Object Based Image Segmentation) untuk mengetahui luasan tutupan mangrove dan NDVI (Normalized Difference Vegetation Index) untuk mengetahui kelas kerapatan mangrove melalui tajuk mangrove yang akan menunjukkan tingkat kehijauannya. Hasil analisis menunjukkan bahwa pada tahun 1989 sebelum terjadinya tsunami luasan mangrove mencapai 249,31 ha dengan kelas kerapatan tertinggi mencapai 224,1 ha (89,88%), tahun 1997 tepatnya 5 tahun setelah terjadinya tsunami luasan hutan mangrove mengalami penurunan yang besar menjadi 100,36 ha dengan kelas kerapatan tertinggi hanya 57,60 ha (57,40%). Tahun 2006 luasan mangrove mengalami kenaikan yang tidak begitu besar menjadi 121,07 ha dengan kelas kerapatan jarang yang tertinggi yaitu 55,90 ha (46,17%), kenaikan luasan mangrove ini terjadi karena adanya suksesi atau perubahan sebagai akibat modifikasi lingkungan fisik dalam suatu ekosistem sehingga akan mengarah kepada terbentuknya suatu komunitas yang permanen dan mantap. tahun 2017 tepatnya 25 tahun setelah terjadinya tsunami luasan hutan mangrove mengalami peningkatan menjadi 175,70 ha dengan kelas kerapatan tertinggi yaitu 111,70 ha (63,58%), kenaikan ini dapat terjadi karena setiap spesies mangrove mempunyai kemungkinan untuk mengalami perubahan seiring dengan berjalannya waktu, dalam hal ini dibutuhkan periode waktu 15-30 tahun untuk mangrove dapat memulihkan dirinya sendiri dan berakhir setelah mencapai ekosistem klimaks atau keadaan seimbangnya.
Kata Kunci : Tsunami, SIG, Perubahan Tutupan Lahan, Mangrove, Suksesi.

El presente reporte preliminar revisa algunos aspectos iniciales a las condiciones socioambientales a la zona estuarina de la desembocadura del río Neverí, Barcelona, Venezuela. Se realizan algunas aproximaciones taxonómicas de la vegetación asociada al ecosistema de manglares, así como una breves referencias dasométricas que dan cuenta de la importancia de la prevalenciay socioconservación de dicho ecosistema para la región.

Mangrove vegetation is one of the forest ecosystems that offers a potential of substantial greenhouse gases (GHG) emission mitigation, due to its ability to sink the amount of CO2 in the atmosphere through the photosynthesis process. Mangroves have been providing multiple benefits either as the source of food, the habitat of wildlife, the coastline protectors as well as the CO2 absorber, higher than other forest types. To explore the role of mangrove vegetation in sequestering the carbon stock, the study on the use of remotely sensed data in estimating carbon stock was applied. This paper describes an examination of the use of remote sensing data particularly Landsatdata with the main objective to estimate carbon stock of mangrove vegetation in Perancak Estuary, Jembrana, Bali. The carbon stock was estimated by analyzing the relationship between NDVI, Above Ground Biomass (AGB) and Below Ground Biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the carbon organic value of 0.47. The study results show that the total accumulated biomass obtained from remote sensing data in Perancak Estuary in 2015 is about 47.20±25.03 ton ha-1 with total carbon stock of about 22.18±11.76 tonC ha-1and CO2 sequestration 81.41±43.18 tonC ha-1.

Keywords: Perancak Estuary, carbon stock estimation, mangrove, CO2 sequestration, NDVI

Pada saat muson tenggara perairan selatan Jawa, Bali dan Nusa Tenggara mengalami upwelling yang berpengaruh ke perairan selat Bali disebut sebagai in-direct upwelling. Pengaruh upwelling ini bisa dilihat dari perubahan parameter fisika, kimia dan biologi perairan seperti suhu, klorofil-a dan nutrien. Penelitian ini bertujuan untuk mengetahui pengaruh terjadinya upwelling terhadap distribusi suhu, klorofil-a dan nutrien terutama nitrat,
fosfat, dan silika di Selat Bali dari hasil observasi bulan Juni-September 2012. Lokasi penelitian di koordinat 8,4 LS – 8,85 LS dan 114,40 BT – 115,5 BT. Metode yang digunakan adalah observasi lapangan saat terjadinya upwelling. Pengukuran parameter suhu dan klorofil-a menggunakan Conductivity Temperature Depth (CTD). Sampel nutrien
diambil tiap layer kedalaman dari permukaan sampai 100 m dibagi menjadi 3 zona mewakili karaketristik perairan utara (Laut Bali), tengah (Selat Bali) dan selatan (Samudera Hindia). Hasil penelitian menunjukkan terjadi peningkatan konsentrasi nutrien yang terlihat dari peningkatan konsentrasi nitrat, fosfat, silikat dan klorofil-a serta penurunan suhu. Konsentrasi klorofil-a maksimum di kedalaman 20 m zona selatan yang berhubungan langsung dengan Samudera Hindia dan terjadi kenaikan massa air dingin dikedalaman 70 m serta peningkatan konsentrasi fosfat di kedalaman 60 m, hal ini memperkuat bahwa pada bulan Juni-September perairan selat Bali mendapatkan pengaruh upwelling kuat dari Samudera Hindia.
Kata kunci : in-direct upwelling, suhu, klorofil-a, nutrien

Mangrove forests occur throughout the coastlines of Arabia, one of the most environmentally extreme regions of their global distribution. The gray mangrove [Avicennia marina (Forsk.) Vierh.] is the only widespread tree species in this region, providing a unique evergreen habitat that supports high mangrove-associated biodiversity and many ecosystem services of socioeconomic importance. This study provides the first literature-based analysis of the status and historical development of mangrove science in Arabia. The goal was to identify trends and gaps in regional mangrove research, providing information essential to guiding future science and conservation programs. The analyses incorporated 400 publications from over six decades since the first article in 1955, each classified into 11 major research topics and including studies from each of the regional seas (Red Sea, Arabian Sea, Sea of Oman, and the Persian/Arabian Gulf). The results demonstrate that there has been exponential growth in research on regional mangroves in recent decades (particularly around human impacts and ecology). The study further reveals knowledge gaps in important research topics and geographical areas that could be exploited to provide insights into broader biogeographic issues. These results provide a comprehensive assessment of the growing body of knowledge on regional mangroves, and provide direction for future research that can guide conservation, management and scientific knowledge of these critical ecosystem engineers.

Above ground biomass (AGB) is a tool for estimating carbon storage capacity of plants and hence the estimation methods are crucial. Karankadu mangrove swamp in the Palk Bay, Southeast coast of India is characterized by the presence of young mangrove species Avicennia marina (A. marina). AGB of A. marina in Karankadu mangrove swamp with 10 hectares (ha) plots was estimated using the non-destructive allometric method. Analyses of allometric regression comprised of leaves, branches and stems of A. Marina depend upon the independent variables of diameter at breast height (DBH), height (H) and basal area (BA). Average AGB of A. marina in the Karankadu mangrove swamp was found to be 10.71 t h-1. A. Marina species amidst in hyper saline conditions effectively captures the atmospheric CO 2 and storing them as biomass in the above ground parts than that of terrestrial ecosystem. Above ground carbon (AGC) content of A. marina trees calculated using AGB values was ranged between 1.15 and 7.92 t ha-1 .

Se realizó un análisis de las tasas de deforestación de los manglares en México mediante la comparación bibliográfica de estudios de caso que abarcan periodos entre 1973 y 2011. Se revisaron un total de 19 estudios realizados a partir de la clasificación de imágenes satelitales para dos temporadas que registraron tasa de deforestación y un nivel de exactitud aceptable para su clasificación. Ocho de ellos corresponden a la región Pacífico Norte, cinco a la región Península de Yucatán, tres al Pacífico Sur, dos en la región Golfo de México y uno es del Pacífico Centro. Para todos los casos revisados se observa una disminución de la superficie de manglar. Los promedios de deforestación varían de acuerdo a la región. La tasa de deforestación anual promedio calculada a partir de esta información es de 1.1%. Aunque esta tasa coincide con la registrada en otras estimaciones, es necesario considerar las limitaciones impuestas por la falta de información en algunas regiones. Se considera de gran importancia el monitoreo de los cambios de cobertura de los manglares a escala local-regional, ya que éste permitirá determinar tendencias en la condición del ecosistema, identificar agentes de deforestación y establecer propuestas de manejo costero.

Palabras clave: manglares, México, cambios de cobertura, clasificación de imágenes, agentes de deforestación.

Resumen El Humedal Nacional Térraba-Sierpe se localiza el sureste de Costa Rica, tiene una extensión aproximada de 14 800 ha de mangle y para este trabajo se realizó una clasificación espectral con una imagen satelital Rapid Eye del año 2012 para determinar la distribución de las especies de mangle más abundantes: Rhizophora racemosa, Pelliciera rhizophorae, la asociación entre la especies R. racemosa y P. rhizophorae, y Rhizophora mangle; además de otras especies del humedal presentes como Acrostichum aureum. También, se realizó un análisis de las características geomorfológicas y ecológicas que favorecen a las especies dominantes en el HNTS y cómo los cambios en el uso de la tierra en las zonas adyacentes a esta área silvestre protegida generan alteraciones en este delicado ecosistema de gran importancia internacional.

Abstract The Térraba-Sierpe National Wetland is located in southeastern Costa Rica, has an approximate extension of 14 800 ha of mangrove and for this work a spectral classification with a satellite image Rapid Eye of the year 2012 was carried out to determine the distribution of the more abundant mangrove species: Rhizophora racemosa, Pelliciera rhizophorae, association between the species R. racemosa and P. rhizophorae, and Rhizophora mangle; in addition to other present wetland species as Acrostichum aureum. Also, an analysis of the geomorphological and ecological characteristics favoring the dominant species in the HNTS was accomplished and how the changes in the land use in the adjacent zones to this protected wild area generate alterations in this delicate ecosystem of great international importance.

ABSTRAK Pengukuran karbon hutan selama ini lebih banyak dilakukan secara terestrial dan belum banyak memanfaatkan data penginderaan jauh. Kelebihan data penginderaan jauh dibandingkan metode terestrial adalah lebih efisien dari segi waktu, biaya dan tenaga, tetapi metode terestrial memiliki tingkat akurasi yang lebih tinggi. Penelitian ini bertujuan untuk mengetahui sejauh mana data penginderaan jauh dan transformasi indeks vegetasi dapat dimanfaatkan dalam mengestimasi kandungan karbon pada kawasan hutan mangove, sehingga akan diketahui kandungan karbon lapangan dan tingkat akurasi pemetaan. Metode yang digunakan dalam peneltian ini dengan perhitungan kandungan karbon pada beberapa titik sampel lapangan dengan alometri dan melihat korelasi antara kandungan karbon pada titik sampel lapangan dengan nilai indeks vegetasi TVI. Nilai karbon total yang didapat dengan menggunakan indeks vegetasi TVI pada kawasan Taman Nasional Alas Purwo sebesar 583.981 ton dengan nilai R 2 = 0,63, nilai karbon rata-rata untuk setiap piksel diketahui sebesar 4,2409 ton/piksel dan nilai karbon rata-rata untuk setiap meter diketahui sebesar 0,0424 ton/m 2. KATA KUNCI : Penginderaan Jauh, Karbon Hutan, Indeks Vegetasi 1. PENDAHULUAN Potensi sumberdaya hutan Indonesia sangat melimpah, dan salah satunya adalah hutan mangrove. Potensi hutan mangrove Indonesia cukup besar, Indonesia memiliki luas hutan mangrove terbesar di dunia (Kusmana, 1996). WALHI (2010), menjelaskan telah terjadi penurunan luas hutan mangrove. Luasan hutan mangrove Indonesia menurun dari luas awal sekitar 4.5 juta ha menjadi 1.9 juta ha. Penurunan luas hutan mangrove terjadi paling dominan karena kerusakan yang disebabkan oleh faktor manusia, seperti alih tataguna lahan mangrove menjadi lahan tambak, ekploitasi kayu mangrove untuk kayu bakar dan arang khususnya untuk jenis Rhizopora spp dan Bruguiera spp. Kerusakan mangrove yang terjadi akan mempengaruhi besarnya kemampuan hutan mangrove dalam penyerapan karbon. Permasalahan klimatik tersebut oleh United Nations for Climate Change Convention (UNFCCC) telah dicari solusi untuk mengurangi dampak emisi gas buang GRK. Pertemuan yang diselenggarakan di Bali pada tahun 2007 pada pertemuan UN Conference of Parties ke 13 (COP 13) mengeluarkan suatu program Reduced Emissions from Deforestation and Degradation (REDD) yang bertujuan untuk mendukung negara-negara berkembang di dalam mengurangi emisi dari deforestasi dan degradasi (www.unfccc.int). Program REDD ini diharapkan negara berkembang berpartisipasi aktif untuk menjaga kelestarian hutannya sebagai bentuk sumbangsih alami dalam menciptakan ―mesin penyerap karbon‖ untuk mengurangi emisi gas buang GRK. Stok karbon pada suatu vegetasi khususnya pohon sangat diperluka n bagi keselarasan lingkungan dari emisi yang berlebihan karena pohon dan organisme foto-ototrof lainnya mengalami proses fotosintesis pada siang hari, proses tersebut membutuhkan suatu komponen penting berupa CO 2 dari atmosfer. Penyerapan CO 2 dalam jumlah besar oleh tumbuhan dilakukan pada siang hari bertepatan saat berbagai aktifitas manusia memproduksi emisi gas buang GRK (salah satunya karbon) di alam bebas.

Although the protective role of mangroves for coasts has been increasingly recognized, that of estuarine mangroves is less well acknowledged. The complex root, stem, and canopy system of healthy estuarine mangroves efficiently reduces the impact of a strong, along-bank flow during high tides and high river discharge, protecting the riverbank from eroding. If a sediment source is available, a healthy mangrove forest also offers a higher potential for sedimentation to compensate for sea-level rise. Unfortunately, along the Mekong, Vietnam, estuaries, mangroves have been rapidly destroyed. In many regions, estuarine mangroves have degraded into narrow strips of ,50 m. Riverbanks at those locations are eroding at a rate of 2–4 m y À1. The main reason for this ''estuarine mangrove squeeze'' phenomenon is due to the increasing demand to create more space for local fish farming. Hence, squeeze is used in a broader sense than in the context of sea-level rise impact alone. The hypothesis is that there is a critical minimum width for an estuarine mangrove forest strip to maintain its ability to survive. The analysis of available data, both from literature and from satellite observations, supports the hypothesis: An average critical width for Mekong estuaries was found to be approximately 80 m. To obtain insight into the efficiency of a mangrove forest in reducing the impacts of alongshore flow, the state-of-the-art Delft3D model was applied to the data. The model showed that the penetration-length scale of the shear layer into a mangrove forest requires a certain minimum space to develop fully. It is hypothesized that the minimum width of a mangrove forest, which equals this maximum penetration-length scale, has a crucial role for the health of a mangrove system. ADDITIONAL INDEX WORDS: Riverbank erosion, fish farm, critical width, flow penetration.

Mangroves are most opportunistic plant to find out the favorable environment for adjustment with changing natural conditions of the coastal processes over time and space. The salt loving halophytic plants of intertidal environment are also known as a perfect biological indicator of coastal environmental changes. They act as natural buffer or bio-shield against the wind breaks, tidal waves and coastal erosion. Surface stability  of younger deltaic sediments is achieved in the Sundarban coast due to the location and luxuriant growth of mangroves. Presently, Sundarban mangroves are affected by multiple ways of degradations that will produce the significant risks or vulnerabilities to the deltaic coasts occupied by land hungry people of South-Asia.
The present study reveals with an attempt to prepare a checklist for the assessment  of  mangrove  degradations  with  special  reference  to  south  west  Sundarban coast.  The  significance  of  mangrove  conservation  will  be  strongly  supported  by such  degradation  check  lists  for  the  coastal  managers. The  present work  is  conducted by extensive field works over a prolonged period, use of professional experiences  of  the  authors,  and  application  of  Geospatial  Techniques  for  database generation  and  management  to  achieve  the  purposes.  However,  factor  analysis
(PCA) method is also utilized to justify the ideal sequential factors those are responsible  for  mangrove  degradations  for  each  study  area  of  the  islands  as  per  their regional location characters.
So far as, seven major factors and their total 56 sub-factors of mangrove degradations have been identified in southwestern parts of the Sundarban from the temporal field observations, remote sensing studies and explored historical documents in the study areas. From the present study it is revealed that hypersalinity, storm effects and sediment deposition parameters are mainly responsible for mangrove degradations in Patibania Island (Susnir Char); and for the Fredrick Island fishery development, land erosion and hypersalinity parameters are liable to mangrove degradations; and finally, fishery development, sediment deposition and land erosion parameters are sequentially responsible for mangrove degradations in Henry’s Island.

Coastal erosion, land use and land cover (LULC) changes analysis using remote sensing is a dynamic, relatively low cost based precise method using now a day. Coastal districts of Bangladesh occupied by naturally grown mangrove forest which are susceptible to rapid land cover (LC) changes and natural erosion. Barguna and Patuakhali districts of Bangladesh deserve special attention for conserving coastal mangrove forest named Tengragiri Wildlife Sanctuary and variety of human forces income. The core objective of this research is to analyze the LULC change along with coastal erosion analysis from 2000 to 2017. Combination of four years Landsat satellite image analysis, primary field data, geo-tag photography, secondary information, utilization of forest carbon inventory 2015 data, and semi-structured questionnaire are the key approaches adopted in the study. K-means cluster based unsupervised and maximum likelihood supervised classification by using ERDAS Imagine 2014 found the total study area is 33,361 ha. Random sampling (40 points/class) based accuracy assessment and verification by google earth pro 7.1 found overall accuracy 88.15% and Kappa coefficient is 0.867. Python coding program and overlay operation tested for conversion analysis any found weighted overlay provide best results. An intensive RS analysis of 33,564 ha mangrove forest and community landscapes generated six (6) distinct land cover class and sub-classes, e.g. Forest, agriculture & grassland, plantation, sandbar, settlement and waterbody. During 2000-2017, agriculture and grassland were decreasing 23 ha/year. Out of 11,831 ha (in 2000) Agri-grass land 9,326 ha remained intact while remaining 2,246 ha converted to settlement mixed with homestead plantation class. This study also presents the landscape erosion-accretion due to natural, quasi-natural and anthropogenic interventions which shows that, along the river flow and at the confluence at the Nishanbaria Union (local name Khouttar Char & Fakir hat) to lower side of the Tengragiri WS locations are susceptible to high trend of land erosion whereas accretions are prominent on the reverse sides named Baliatali Union, Barabagi Union and so on. These results of the study and developed maps will be helpful for the community people, line departments, national and international policy maker and the researchers' community for monitoring coastal geomorphology including erosion and accretion of this landmass.

Cat Ba is one of the most well-known islands located in North Vietnam, which has been recognized as a biosphere reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2004. Despite the large potential carbon stocks in mangrove forests of Cat Ba, the mangrove ecosystem of this island has suffered severe deforestation and forest degradation due to the conversion to shrimp aquaculture. Monitoring mangrove forest changes plays an important role for effective mangrove conservation and management. The objectives of this study were to map the spatial distribution of mangrove forest and to assess their changes between 2010 and 2015 in Cat Ba Biosphere Reserve, Hai Phong city of Vietnam using ALOS PALSAR data and a GIS-based support vector machine algorithm. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected. Then, spatial distributions of mangroves were derived using the support vector machine classifier. The results showed that the ALOS-2 PALSAR for 2015 achieves the overall accuracy of 85% and the kappa coefficient of 0.81, compared with those of 81% and 0.77, respectively from the ALOS PALSAR for 2010. The mangrove forest areas in the Cat Ba Biosphere Reserve, Vietnam decreased by 15% from 2010 to 2015. This research shows the potential use of ALOS PALSAR data combined with machine learning techniques in monitoring mangrove forest changes in tropical and semi-tropical climates.

Mangroves are plants that live in the tidal shoreline, with the increasing development and population growth, the coastal ecosystems, especially mangrove degradation. Similarly, the condition of the mangrove ecosystem in the Gulf Pangempang. The purpose of this study was to determine the distribution and extent of using 8/ETM+ Landsat satellite imagery. Research conducted since the month of July through the month of August 2015. This research used 8/ETM+ 6 Landsat imagery months of 2015 and ground check at 6 Station. The mapping method was used to obtain the results of mangrove forest area and further analyzed using ErMapper 6.4 and ArcGIS 10 software. From the data analysis it was known that mangrove forest area in the year of 2015 was ± 257.83 ha and the results of the analysis in the field found there were 13 species of mangrove species include: Aigeceras corniculatum, Aigeceras hydrophyliacea, Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Lumnitzera littorea, Nypa fruticans, Rizophora apiculata, Scyhiphora hydrophylicea, Rizophora mucronata, Rizophora rumphiana, Sonneratia alba.

The main objective of this study is to map the spatial distribution of mangrove species and assess their changes from 2010 to 2015 in Hai Phong City of Vietnam located on the tropical region using the ALOS PALSAR data and an optimized rule-based decision tree technique. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected and used, and then, spatial distributions of mangrove species were derived using logistic model tree (LMT) classifier. The LMT is current state-of-the-art machine learning method that has not been used for mapping of mangrove species. The results showed that incorporation of ALOS PALSAR imagery and GIS in the LMT algorithm provides satisfactory overall accuracy and kappa coefficient. The ALOS-2 PALSAR for 2015 achieved better overall accuracy, with an increment of 3.6% in mapping mangrove species than that of the ALOS PALSAR for 2010. The ALOS-2 PALSAR-derived model yielded the overall accuracy of 83.8% and the kappa coefficient of 0.81, compared with those of the ALOS PALSAR-derived model, 80.2% and 0.78, respectively. The results of classification for 2010 and 2015 were significantly different using the McNemar test. This research demonstrates the potential use of ALOS PALSAR together with machine learning techniques in monitoring mangrove species in tropical areas. Keywords ALOS PALSAR · Hai Phong City · Mangrove species · Decision tree GIS · Logistic model tree

Este estudo visa, através da aplicação de técnicas de sensoriamento remoto, contribuir
para o desenvolvimento de um modelo de fato sustentável das atividades ecoturísticas
nas áreas de manguezal da região e conferir suporte para a elaboração de políticas
públicas para o setor.

Mangrove forests in many parts of the world are declining at an alarming rate, which intensifies vulnerability in coastal ecosystems. An area like coastal West Bengal, including the Sundarban delta, where the world's largest mangrove populations have flourished, has a substantial negative impact of anthropogenic pressure since the early 15-16th century. Frequent coastal hazards, sea-level rise, and ever-increasing anthropogenic pressure have complicated the growth and regeneration of mangroves. In this study, an attempt has been made to map the patches of mangrove available in the coastal region of West Bengal along with their spatio-temporal, zonal pattern of change and probable reasons for those changes. The study approaches have been achieved by the analysis of Landsat data using several buffer zones from the coastline during 1989-2018 through geospatial techniques. The result indicates that despite a reduction of mangrove populations in the pre-and post-colonial era, overall mangrove areas have a marginally positive growth of about 0.47% (68.06 sq. km) of the total area during the assessment period. Although the mangrove area along the shoreline is decreasing faster, many new mangrove patches have appeared towards the mainland. Simultaneously, mangrove areas are encroached in the northern periphery of Sundarban Biosphere Reserve to convert them into settlements, agriculture, and aquaculture. This study could help to protect mangrove populations and implement appropriate conservation measures.

It is widely agreed that rapid development has led to mangroves being in urgent need of improved monitoring and assessment techniques for better conservation. In Malaysia, the Convention on Wetlands, otherwise known as the Ramsar Convention, came into being specifically to address this problem and protect this particular area. The rapidly rising sea-level at mangrove sites is currently impacting the depth of mangrove soil, so action must be extended using available technology to sustain mangrove lives. This study tested if the vegetation indices from recent high-resolution multispectral satellite images Satellite Pour l’Observation de la Terre, known as SPOT, can map mangrove density and predict health area for the sites. An Unmanned Aerial Vehicle (UAV) was also used to fly at the nearer sites for mangrove density classification mapping based on feature extraction tools classification. This study employed Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Greenness Vegetation Index (GVI), and Ratio Vegetation Index (RVI) to map mangrove density for the study site. The study showed, in terms of Maximum Likelihood Classifier (MLC) measurement, that 52% of the area was “high density”, 10%-19% was “low density”, and “low density mixed sand” and also “sand” area. The results also showed that NDVI responded higher for “high density” with 0.56, GNDVI with 0.27 for “high density”, GDVI with 17.0, and RVI only classified two density areas. In addition, the UAV images were classified into shadow, obvious tree crown, vegetation in the water, sea area, and others. As expected, the study revealed that UAV (b) was presented in a very high percentage in obvious tree crowns but in a low percentage with 2% of other classes, making it a distinct class from others. In addition, UAV (a) showed 42% shadow with a small portion distributed in other vegetation features (sea area and others) class, 4% to 10%. Mangrove density can also be used as an indicator of mangrove health status because low density mangroves are always found near to the risk areas.

This research tested the use of geographic information systems using Sentinel 1 and Sentinel 2 satellite data to estimate biomass mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong province. 15 sample plots (10 m × 10 m) in the field were established for making models and evaluation, the satellite images for processing in 2017 were provided freely by ESA Corporation. The study created land cover and biomass maps from field allometric equations and estimated results from the model by maximum likelihood classification and the regression model, respectively. For land cover accuracy assessment, Kappa index was employed with 93% accuracy. NDVI, SAVI were representative indices of optical Sentinel 2 images, similarly, VV and VH backscatter, VV/VH and VH/VV from Sentinel 1A images. The study showed that Sentinel 1 backscatters were unable to generate model due to quite low R 2. Compare to optical images, the NDVI index was used for biomass estimating, the total biomass was about 67,983.12 tons, average: 153.94 ± 27.01 ton/ha, maximum: 223.14 ton/ha. By comparing real numbers and estimated numbers, the results were acceptable, 23.8% average. We conclude that the optical Sentinel 2 has been more suitable to make estimating the model for mangrove biomass at a small-scale level, especially for commune level.

Mangrove forest plays an important role in preventing coastal ecosystems. This ecosystem has been undergone many conversions such as shrimp pond conversion, settlements, plantations, and rice fields. Information over mangrove forest cover timely and accurate is substantially needed to ensure this ecosystem is well conserved. However, such information is still limited both in the perspective of data quantity as well as its mapping methodology. This study aims to analyze the most optimum technique to detect mangrove forest using satellite remote sensing images. Study site was South Lampung District. Mangrove forest cover was resulted from satellite image classification using maximum likelihood classification and object oriented classification algorithms. The results showed that the object oriented classification algorithm gave more accurate detection for mangrove forests with overall accuracy of 95%. The total area of mangrove forest in South Lampung District by this optimum method in 2017 was 458 hectares.

In this newspaper article I describe Boquerón State Forest, its creation in 1918 and the apparent historical "memory loss" of both academics and government officials.  I hypothesize that this loose of consciousness was the culprit of the ill fated project to privatize ecologically rich public lands for 99 years in PR P of S 1621.

The invasion of alien species is a significant threat to global biodiversity and the top driver of climate
change. The present study was conducted in the Great Rann of Kachchh, part of Kachchh Biosphere
Reserve, Gujarat, India, which has been severely affected by invasion of Prosopis juliflora. The invasive
weed infestation has been identified using multi-temporal remote sensing datasets of 1977, 1990,
1999, 2005 and 2011. Spatial analyses of the transition matrix, extent of invasive colonies, patchiness,
coalescence and rate of spread were carried out. During the study period of three and half decades,
almost 295 km2 of the natural land cover was converted into Prosopis cover. This study has shown an
increment of 42.9% of area under Prosopis cover in the Great Rann of Kachchh, part of the Kachchh
Biosphere Reserve during 1977 to 2011. Spatial analysis indicates high occupancy of Prosopis cover
with most of the invasion (95.9%) occurring in the grasslands and only 4.1% in other land cover types.
The process of Prosopis invasion shows high patch initiation, followed by coalescence, indicating aggressive
colonization of species. The number of patches within an area of < 1 km2 increased from 1977 to
2011, indicating the formation of new Prosopis habitats by replacing the grasslands. The largest patch
of Prosopis cover increased from 144 km2 in 1977 to 430 km2 in 2011. The estimated mean patch size
was 7.8 km2 in 1977. The mean patch size was largest during 2011, i.e., 9 km2
. The annual spread rate
for Prosopis has been estimated as 2.1% during 2005–2011. The present work has investigated the long
term changes in Prosopis cover in the Great Rann of Kachchh, part of Kachchh Biosphere Reserve.
The spatial database generated will be useful in preparing strategies for the management of Prosopis
juliflora.

Sri Lanka is an island nation, exhibiting remarkable biological diversity in spite of its small size; Country has rich ecosystem diversity because of its topographic and climatic heterogeneity as well as its coastal influence. The wetland ecosystems in Sri Lanka have been internationally recognized for their outstanding biological prosperity and their conservation status. Sri Lanka’s wetlands are also diverse, comprised of 103 major rivers and associated marshes, and about 12,000 irrigation tanks harboring wetland species including inland and coastal wetland ecosystems which are even coming under Ramsar Wetland Sites. One such coastal wetland ecosystem in Sri Lanka is Crow Island coastal ecosystem. The Crow Island Beach Park is a public park located in Colombo—15 (6°58&#39;24.1&quot;N, 79°52&#39;09.9&quot;E). The land extent of this park is 7 ha. The beach park consists of rich avifaunal diversity since it is a coastal wetland. Sri Lanka has been recognized as a country with “important Bird areas”, a “key Asian region for threatened birds” and an “Endemic Bird Area”. The main objective of the present study was to document the avifaunal diversity of the coastal wetland of the beach park. Multiple surveys were conducted through random visits in the area for a period of two months from July to August 2020. Their status was evaluated from the National Red List. Diurnal and nocturnal observations were made through the unaided eye and using a binocular via multiple random walks and point counting method. A total of 28 bird species were recorded belonging to 14 orders and 22 families representing 5.69% of the birds recorded in Sri Lanka (492). Of the reported bird families, nine families represented Order Passeriformes while four species of birds found in the park represented family Ardeidae. This park is not only significant for biodiversity conservation but also for its value as a coastal wetland. The local community of the area is constantly interacting with the area for their living and for leisure. Consequently, these habitats are threatened by irresponsible human activities, such as garbage dumping, noise pollution, lighting fires for garbage burning, clearing of natural vegetation in and around wetlands. However, detailed survey has to be carried out to study the abundance, seasonal variation to recommend the conservation and management measures.

Knowledge of the nature of seasons and months in terms of the fire risk is very important in environmental planning, land management and forest resource management in order to achieve the sustainable development. One of the applications of remote sensing in this regard is the continuous monitoring of the zone to detect changes. Currently, the vegetation mapping is used to generate information for macro and micro planning. In order to monitor changes across the Golestan province forests through different seasons in 2000-2015, all images of MOD13Q1 MODIS were prepared during this period. Then, the images of the Normalized Difference Vegetation Index (NDVI) were prepared for the four seasons and twelve months of the year. The classification of the indices included lands covered with excellent, moderate, weak and very poor coverage was conducted in order to investigate the changes. Then, the comparison was then performed by LAND FIRE points and the validity of the classification results was evaluated. It was concluded that the seasons of the year from high risk to low risk were winter, summer, fall and spring, respectively. In the high-risk season, winter, January was the most dangerous month and in the low risk season, spring, may was the lowest month of the year.

The author describes the ecological and historical importance of the protected natural areas of Puerto Rico, their perils, and proposes various ways to help protect the areas, one being eco-tourism as a way to create jobs and obtain economic sustainability for the communities surrounding the areas (a novel Darwinian approach: if you protect you may gain fitness).

Mangroves in different part of the globe including world's largest halophytic population in Sundarban are under tremendous pressure due to global warming, sea level rise, natural disasters and everincreasing influence of human population. In such an alarming situation conservation of mangrove population has been recommended by the experts. Species-level mapping of mangroves is one of the important steps for sustainable conservation of mangrove ecosystem. This study demonstrates the efficiency of a combination of Hyperion hyperspectral and IRS Resourcesat-2 LISS-IV multispectral data for discriminating and mapping some mangrove species at Lothian Island and Saptamukhi Reserve Forest located in the western part of Sundarban Biosphere Reserve of India. The spectral signature drawn on Hyperion data helped to extrapolate field samples of various pure pixels of the mangrove plant species using a region growing approach. Those training samples were used to apply Object-based Image Analysis (OBIA) classification on 5 m spatial resolution LISS-IV data. Mostly dominant mangrove species like Aegialitis rotundifolia, Aegiceras corniculatum, Avicennia community, Ceriops community, Excoecaria agallocha, Lumnitzera racemosa and Phoenix paludosa have been successfully classified along with other associated landuse/landcover in these islands. The methodology used in the study has a huge potential in identification, mapping and management through restoration of mangroves in difficult terrain like the Sundarban.

It is so important to be aware of quantitative and qualitative characteristics of changes for the environment, land use planning and sustainable development. Detecting the changes in the condition of an issue is done by time difference observations. Change detection refers to the process of detecting time changes of an object through different time observations. The detection of changes by satellite imageries has come recently to the focus of attention due to their comprehensive and integrated characteristics and their ability to monitor changes during long periods as well as their application to monitoring and controlling changes in the forest ecosystems. The vegetation maps are now used to generate required information for macro and micro planning. This study was done to monitor the changes in the forest of Golestan province in the past and also to investigate the possibility of its future forecast using the Land Change Modeler (LMC). A forest type map was first prepared to monitor changes in forests of Golestan province from 2000 to 2015. The images taken in 12 months during 2000 and 2015 were collected form MODIS satellite imageries to monitor the forest. After pre-processing and preparing the time series in two sections, the forest changes were considered using the Normalized Difference Vegetation Index and Moisture Stress Index (NDVI and MSI). The changes were then classified by indices, including the lands with excellent, very good, medium and poor coverage and compared together. The accuracy of the classification results was assessed using the field maps. The best result was found in the average time series data and the use of the NDVI index was prioritized over other indices. The forecast of changes by 2030 as 0.8629 Kappa and the model results for 2030 indicated a decline of 70000 in the land with high and very high capacities and a further increase in land with a low capacity.