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Mapping the Philippines’ Mangrove Forests Using Landsat Imagery  [PDF]
Jordan B. Long,Chandra Giri
Sensors , 2011, DOI: 10.3390/s110302972
Abstract: Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines’ mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines’ degraded mangrove forests.
Multi-Decadal Mangrove Forest Change Detection and？Prediction in Honduras, Central America, with Landsat？Imagery and a Markov Chain Model  [PDF]
Chi-Farn Chen,Nguyen-Thanh Son,Ni-Bin Chang,Cheng-Ru Chen,Li-Yu Chang,Miguel Valdez,Gustavo Centeno,Carlos Alberto Thompson,Jorge Luis Aceituno
Remote Sensing , 2013, DOI: 10.3390/rs5126408
Abstract: Mangrove forests play an important role in providing ecological and socioeconomic services for human society. Coastal development, which converts mangrove forests to other land uses, has often ignored the services that mangrove may provide, leading to irreversible environmental degradation. Monitoring the spatiotemporal distribution of mangrove forests is thus critical for natural resources management of mangrove ecosystems. This study investigates spatiotemporal changes in Honduran mangrove forests using Landsat imagery during the periods 1985–1996, 1996–2002, and 2002–2013. The future trend of mangrove forest changes was projected by a Markov chain model to support decision-making for coastal management. The remote sensing data were processed through three main steps: (1) data pre-processing to correct geometric errors between the Landsat imageries and to perform reflectance normalization; (2) image classification with the unsupervised Otsu’s method and change detection; and (3)？mangrove change projection using a Markov chain model. Validation of the unsupervised Otsu’s method was made by comparing the classification results with the ground reference data in 2002, which yielded satisfactory agreement with an overall accuracy of 91.1% and Kappa coefficient of 0.82. When examining mangrove changes from 1985 to 2013, approximately 11.9% of the mangrove forests were transformed to other land uses, especially shrimp farming, while little effort (3.9%) was applied for mangrove rehabilitation during this 28-year period. Changes in the extent of mangrove forests were further projected until 2020, indicating that the area of mangrove forests could be continuously reduced by 1,200 ha from 2013 (approximately 36,700 ha) to 2020 (approximately 35,500 ha). Institutional interventions should be taken for sustainable management of mangrove ecosystems in this coastal region.
Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand  [PDF]
Uday Pimple, Dario Simonetti, Asamaporn Sitthi, Sukan Pungkul, Kumron Leadprathom, Henry Skupek, Jaturong Som-ard, Valery Gond, Sirintornthep Towprayoon
Journal of Computer and Communications (JCC) , 2018, DOI: 10.4236/jcc.2018.61025
Abstract:
Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Cosistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery; and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.
ATMOSPHERIC CORRECTION OF THE LANDSAT SATELLITE IMAGERY FOR TURBID WATERS
Hwan Ahn,Yu; Shanmugam,P; Hyung Ryu,Joo;
Gayana (Concepción) , 2004, DOI: 10.4067/S0717-65382004000200002
Abstract: this paper describes methods for the correction of the atmospheric effects in the landsat vis/nir imagery in relation to the retrieval of meaningful information about the ocean color, especially from case-2 waters around korean peninsula. three atmospheric correction (ac) methods implemented and examined, using the toa radiance or reflectance data, are 6s radiative transfer model, spectral shape matching (ssmm) and path-extraction methods. the results show that overall shape and magnitude of radiance or reflectance spectra of the atmospherically corrected landsat vis/nir imagery by ssmm appears to have very good agreement with the in-situ spectra collected for clear and turbid waters, while path-extraction over turbid waters though often reproduces in-situ spectra, but yields significant errors for clear waters due to the invalid assumption of zero values for the black ocean pixels of the landsat vis/nir bands. because of the standard atmosphere with constant aerosols and models adopted in 6s model, a large error is possible between the retrieved and in-situ spectra. validation suggests that accurate the retrieval of water-leaving radiance is not feasible with the invalid assumption of classical ac algorithms, but is feasible with ssmm.
ATMOSPHERIC CORRECTION OF THE LANDSAT SATELLITE IMAGERY FOR TURBID WATERS  [cached]
Yu Hwan Ahn,P Shanmugam,Joo Hyung Ryu
Gayana (Concepción) , 2004,
Abstract: This paper describes methods for the correction of the atmospheric effects in the Landsat VIS/NIR imagery in relation to the retrieval of meaningful information about the ocean color, especially from Case-2 waters around Korean peninsula. Three atmospheric correction (AC) methods implemented and examined, using the TOA radiance or reflectance data, are 6S radiative transfer model, spectral shape matching (SSMM) and path-extraction methods. The results show that overall shape and magnitude of radiance or reflectance spectra of the atmospherically corrected Landsat VIS/NIR imagery by SSMM appears to have very good agreement with the in-situ spectra collected for clear and turbid waters, while path-extraction over turbid waters though often reproduces in-situ spectra, but yields significant errors for clear waters due to the invalid assumption of zero values for the black ocean pixels of the Landsat VIS/NIR bands. Because of the standard atmosphere with constant aerosols and models adopted in 6S model, a large error is possible between the retrieved and in-situ spectra. Validation suggests that accurate the retrieval of water-leaving radiance is not feasible with the invalid assumption of classical AC algorithms, but is feasible with SSMM.
Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM  [PDF]
Bayan Alsaaideh, Ahmad Al-Hanbali, Ryutaro Tateishi, Toshiyuki Kobayashi, Nguyen Thanh Hoan
Journal of Geographic Information System (JGIS) , 2013, DOI: 10.4236/jgis.2013.54035
Abstract:

A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery  [PDF]
Bülent Saglam,Ertugrul Bilgili,Bahar Dincdurmaz,Ali Ihsan Kadiogulari,？mer Kücük
Sensors , 2008, DOI: 10.3390/s8063970
Abstract: Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.
Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery
B？？？？lent Saglam,Ertugrul Bilgili,Bahar Dincdurmaz,Ali ？？？°hsan Kadiogulari
Sensors , 2008,
Abstract: Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.
SHORELINE DETECTION AND CHANGES FOR TERENGGANU RIVER MOUTH FROM SATELLITE IMAGERY (LANDSAT 5 AND LANDSAT 7)  [PDF]
R. Zakaria, Y. Rosnan, S. A. Saidin, M. H. Yahaya, I. Kasawani, H. Lokman
Journal of Sustainability Science and Management , 2006,
Abstract: The Terengganu river mouth and related coastal area has been chosen to improve delineation process from satellite imagery data. Landsat 5 (1996) and Landsat 7 (2002) were used together with GIS capability to determine shoreline, sandy area and and the changes occur specially on sediment movement from 1996 to 2002. For methodology, RGB to IHS imagery conversion analysis has been used and classified using ISODATA (Iterative Self- Organizing Data Analysis) technique to detect the particular area. This technique had been introduced by Chang et al. (1999) and modification and development was made for Landsat images before it can be applied in this study. Satellite imagery on standardizing and processing technique for change detection analysis was used to improve results accuracy. Base on the result, shoreline and sandy area at the Terengganu river mouth were easily been detected using Landsat imagery. The delineation technique process was compatible and easy to use for fast shoreline and sandy area detection at coastal environment. Referring from the result also, Terengganu river mouth has having a minimal change on sedimentation from 1996 to 2000. The changes involved concerning accretion and erosion processes which possibly impact directly with human alteration.
Overcoming Limitations with Landsat Imagery for Mapping of Peat Swamp Forests in Sundaland  [PDF]
Lahiru S. Wijedasa,Sean Sloan,Dimitrios G. Michelakis,Gopalasamy R. Clements
Remote Sensing , 2012, DOI: 10.3390/rs4092595
Abstract: Landsat can be used to map tropical forest cover at 15–60 m resolution, which is helpful for detecting small but important perturbations in increasingly fragmented forests. However, among the remaining Landsat satellites, Landsat-5 no longer has global coverage and, since 2003, a mechanical fault in the Scan-Line Corrector (SLC-Off) of the Landsat-7 satellite resulted in a 22–25% data loss in each image. Such issues challenge the use of Landsat for wall-to-wall mapping of tropical forests, and encourage the use of alternative, spatially coarser imagery such as MODIS. Here, we describe and test an alternative method of post-classification compositing of Landsat images for mapping over 20.5 million hectares of peat swamp forest in the biodiversity hotspot of Sundaland. In order to reduce missing data to levels comparable to those prior to the SLC-Off error, we found that, for a combination of Landsat-5 images and SLC-off Landsat-7 images used to create a 2005 composite, 86% of the 58 scenes required one or two images, while 14% required three or more images. For a 2010 composite made using only SLC-Off Landsat-7 images, 64% of the scenes required one or two images and 36% required four or more images. Missing-data levels due to cloud cover and shadows in the pre SLC-Off composites (7.8% and 10.3% for 1990 and 2000 enhanced GeoCover mosaics) are comparable to the post SLC-Off composites (8.2% and 8.3% in the 2005 and 2010 composites). The area-weighted producer’s accuracy for our 2000, 2005 and 2010 composites were 77%, 85% and 86% respectively. Overall, these results show that missing-data levels, classification accuracy, and geographic coverage of Landsat composites are comparable across a 20-year period despite the SLC-Off error since 2003. Correspondingly, Landsat still provides an appreciable utility？for monitoring tropical forests, particularly in Sundaland’s rapidly disappearing peat swamp？forests.
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