scholarly journals Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China

2018 ◽  
Vol 10 (12) ◽  
pp. 2020 ◽  
Author(s):  
Chunyan Lu ◽  
Jinfu Liu ◽  
Mingming Jia ◽  
Mingyue Liu ◽  
Weidong Man ◽  
...  
Keyword(s):  
Forest Dynamics ◽  
Mangrove Forest ◽  
Temporal Distribution ◽  
Forest Monitoring ◽  
Scale Model ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Landsat Images ◽  
Quanzhou Bay ◽  
Optimal Segmentation

Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km2, which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.

scholarly journals Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 637
Author(s):  
Huong Thi Thuy Nguyen ◽  
Giles E. S. Hardy ◽  
Tuat Van Le ◽  
Huy Quoc Nguyen ◽  
Hoang Huy Nguyen ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Monsoon Season ◽  
Coastal Development ◽  
Northern Coast ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Mangrove Restoration ◽  
Coast Of Vietnam ◽  
Landsat Satellite

Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

scholarly journals Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing

2019 ◽  
Vol 11 (19) ◽  
pp. 5356 ◽  
Author(s):  
Liao ◽  
Zhen ◽  
Zhang ◽  
Metternicht
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Mangrove Forest ◽  
Public Awareness ◽  
Hainan Island ◽  
Current Data ◽  
Support Vector ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Wastewater Disposal

Implementation of the UN Sustainable Development Goals requires countries to determine targets for the protection, conservation, or restoration of coastal ecosystems such as mangrove forests by 2030. Satellite remote sensing provides historical and current data on the distribution and dynamics of mangrove forests, essential baseline data that are needed to design suitable policy interventions. In this study, Landsat time series were used to map trends and dynamics of mangrove change over a time span of 30 years (1987–2017) in protected areas of Hainan Island (China). A support vector machine algorithm was combined with visual interpretation of imagery and result showed alternating periods of expansion and loss of mangrove forest at seven selected sites on Hainan Island. Over this period, there was a net decrease in mangrove area of 9.3%, with anthropic activities such as land conversion for aquaculture, wastewater disposal and discharge, and tourism development appearing to be the likely drivers of this decline in cover. Long-term studies examining trends in land use cover change coupled with assessments of drivers of loss or gain enable the development of evidence based on policy and legislation. This forms the basis of financing of natural reserves of management and institutional capacity building, and facilitates public awareness and participation, including co-management.

scholarly journals Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1018
Author(s):  
Charissa J. Wong ◽  
Daniel James ◽  
Normah A. Besar ◽  
Kamlisa U. Kamlun ◽  
Joseph Tangah ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Field Measurements ◽  
Tree Level ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Estimation Model ◽  
Landsat Images ◽  
Ground Biomass ◽  
Elevation Data ◽  
Srtm Data

Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.

scholarly journals Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

2020 ◽  
Vol 12 (22) ◽  
pp. 3729
Author(s):  
Leon T. Hauser ◽  
Nguyen An Binh ◽  
Pham Viet Hoa ◽  
Nguyen Hong Quan ◽  
Joris Timmermans
Keyword(s):  
Land Use ◽  
Mangrove Forest ◽  
Temporal Dynamics ◽  
Mekong Delta ◽  
Google Earth ◽  
Data Availability ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Landsat 7

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Economic value analysis of mangrove forest ecosystems in Sorong, West Papua Province

2014 ◽  
pp. 39
Author(s):  
Roger R Tabalessy
Keyword(s):  
Mangrove Forest ◽  
Economic Value ◽  
Forest Degradation ◽  
Primary Data ◽  
Mangrove Forests ◽  
Value Analysis ◽  
Human Needs ◽  
The People ◽  
West Papua ◽  
Mangrove Wood

Coastal areas can either meet the human needs or give great contribution to the development. However, rapid infrastrural development in Sorong, west Papua, has been followed by high demand for mangrove timber and caused mangrove forest degradation due to exploitation. This exploitation could also result from high economic value of the mangrove timber. This study was done to analyze the economic value of mangrove wood utilized by the people to support the development process in Sorong. This study used primary data obtained through interviews and the economic value calculation of mangrove forests. It found that Sorong had mangrove economic value of IDR 165,197,833, 491. Wilayah pesisir selain dapat memenuhi kebutuhan hidup manusia juga memberikan kontribusi yang besar bagi pembangunan. Cepatnya pembangunan infrastruktur di Kota Sorong diikuti pula dengan tingginya permintaan akan kayu mangrove dan menyebabkan terjadinya degradasi hutan mangrove akibat eksploitasi. Eksploitasi ini disebabkan juga akibat kayu mangrove memiliki nilai ekonomi. Penelitian yang dilakukan ini bertujuan untuk menganalisis nilai ekonomi kayu mangrove yang dimanfaatkan oleh masyarakat Kota Sorong dalam proses menunjang pembangunan. Penelitian ini menggunakkan data primer yang diperoleh melalui hasil wawancara dan perhitungan nilai ekonomi hutan mangrove. Hasil penelitian ini menunjukkan nilai ekonomi ekosistem hutan mangrove yang berada di Kota Sorong adalah Rp165.197.833.491.

scholarly journals Stable Coexistence in a Field-Calibrated Individual-Based Model of Mangrove Forest Dynamics Caused by Inter-Specific Crown Plasticity

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 955
Author(s):  
Uwe Grueters ◽  
Mohd Rodila Ibrahim ◽  
Hartmut Schmidt ◽  
Katharina Tiebel ◽  
Hendrik Horn ◽  
...  
Keyword(s):  
Forest Dynamics ◽  
Mangrove Forest ◽  
Resource Competition ◽  
Standing Stock ◽  
Mean Field ◽  
Individual Based Model ◽  
Local Competition ◽  
Competition Theory ◽  
Crown Plasticity ◽  
Classical Competition

(1,2) In this theoretical study, we apply MesoFON, a field-calibrated individual-based model of mangrove forest dynamics, and its Lotka–Volterra interpretations to address two questions: (a) Do the dynamics of two identical red mangrove species that compete for light resources and avoid inter-specific competition by lateral crown displacement follow the predictions of classical competition theory or resource competition theory? (b) Which mechanisms drive the dynamics in the presence of inter-specific crown plasticity when local competition is combined with global or with localized seed dispersal? (3) In qualitative support of classical competition theory, the two species can stably coexist within MesoFON. However, the total standing stock at equilibrium matched the carrying capacity of the single species. Therefore, a “non-overyielding” Lotka–Volterra model rather than the classic one approximated best the observed behavior. Mechanistically, inter-specific crown plasticity moved heterospecific trees apart and pushed conspecifics together. Despite local competition, the community exhibited mean-field dynamics with global dispersal. In comparison, localized dispersal slowed down the dynamics by diminishing the strength of intra-/inter-specific competition and their difference due to a restriction in the competitive race to the mean-field that prevails between conspecific clusters. (4) As the outcome in field-calibrated IBMs is mediated by the competition for resources, we conclude that classical competition mechanisms can override those of resource competition, and more species are likely to successfully coexist within communities.

scholarly journals Assessing the Natural Recovery of Mangroves after Human Disturbance Using Neural Network Classification and Sentinel-2 Imagery in Wunbaik Mangrove Forest, Myanmar

2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Win Sithu Maung ◽  
Jun Sasaki
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Transfer Learning ◽  
Mangrove Forest ◽  
Kappa Coefficient ◽  
Mangrove Forests ◽  
Ann Model ◽  
Shrimp Ponds ◽  
Natural Recovery ◽  
Sentinel 2

In this study, we examined the natural recovery of mangroves in abandoned shrimp ponds located in the Wunbaik Mangrove Forest (WMF) in Myanmar using artificial neural network (ANN) classification and a change detection approach with Sentinel-2 satellite images. In 2020, we conducted various experiments related to mangrove classification by tuning input features and hyper-parameters. The selected ANN model was used with a transfer learning approach to predict the mangrove distribution in 2015. Changes were detected using classification results from 2015 and 2020. Naturally recovering mangroves were identified by extracting the change detection results of three abandoned shrimp ponds selected during field investigation. The proposed method yielded an overall accuracy of 95.98%, a kappa coefficient of 0.92, mangrove and non-mangrove precisions of 0.95 and 0.98, respectively, recalls of 0.96, and F1 scores of 0.96 for the 2020 classification. For the 2015 prediction, transfer learning improved model performance, resulting in an overall accuracy of 97.20%, a kappa coefficient of 0.94, mangrove and non-mangrove precisions of 0.98 and 0.96, respectively, recalls of 0.98 and 0.97, and F1 scores of 0.96. The change detection results showed that mangrove forests in the WMF slightly decreased between 2015 and 2020. Naturally recovering mangroves were detected at approximately 50% of each abandoned site within a short abandonment period. This study demonstrates that the ANN method using Sentinel-2 imagery and topographic and canopy height data can produce reliable results for mangrove classification. The natural recovery of mangroves presents a valuable opportunity for mangrove rehabilitation at human-disturbed sites in the WMF.

Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000

2007 ◽  
Vol 73 (1-2) ◽  
pp. 91-100 ◽  
Author(s):  
Chandra Giri ◽  
Bruce Pengra ◽  
Zhiliang Zhu ◽  
Ashbindu Singh ◽  
Larry L. Tieszen
Keyword(s):  
Satellite Data ◽  
Forest Dynamics ◽  
Mangrove Forest ◽  
Multi Temporal ◽  
The Sundarbans

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

scholarly journals Ecological relations between mangrove leaf litter and the spatial distribution of the gastropod Melampus coffeus in a fringe mangrove forest

2015 ◽  
Vol 105 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Diva S. Tavares ◽  
Rafaela C. Maia ◽  
Cristina Rocha-Barreira ◽  
Helena Matthews-Cascon
Keyword(s):  
Spatial Distribution ◽  
Leaf Litter ◽  
Mangrove Forest ◽  
Atlantic Coast ◽  
Ground Surface ◽  
Mangrove Forests ◽  
Physical Presence ◽  
Ecological Relations ◽  
Melampus Coffeus ◽  
Pulmonate Gastropod

Leaf litter represents a food source to many organisms that may directly contribute to organic matter decomposition. In addition, the physical presence of these vegetal detritus contributes for the modification of some environmental areas and produce microhabitats that may act as a refuge against predators and desiccation for many animals. The pulmonate gastropod Melampus coffeus (Linnaeus, 1758) (Ellobiidae) is a very common specie in Atlantic Coast mangrove forests and feeds on fallen mangrove leaves. It was hypothesized that the spatial distribution of Melampus coffeus is directly affected by mangrove leaf litter biomass deposition. Thus, this research aimed at evaluating the spatial distribution of these gastropods in relation to the biomass of mangrove leaf litter through a twelve-month period. The study area was established in the middle estuary of Pacoti River, state of Ceará, Brazil where two adjacent zones with different topographic profiles were determined. Samples of Melampus coffeus and leaf litter were collected monthly, throughout a year, from the mangrove ground surface. The results indicated that the presence of twigs in mangrove litter favor the occupation by smaller individuals of M. coffeus, probably because smaller individuals are more susceptible to predator attacks and desiccation than larger ones, and twigs and branches may provide a safe microhabitat.

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