An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data

Flores‐de‐Santiago, Francisco; Kovacs, John M.; Lafrance, Patrick

doi:10.1080/01431161.2012.715773

Cited by 36 publications

(17 citation statements)

References 36 publications

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“…However, studies monitoring mangroves in Africa are unevenly distributed in space and time. Several studies documented mangrove extent or changes across Africa [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], but primarily focusing on a few countries, such as Gambia, Guinea-Bissau, Guinea, Mauritania, Mozambique, Madagascar, Nigeria, Senegal, Sierra Leone, South Africa, and Tanzania. Moreover, most studies do not cover a temporal span suitable for longer-term consistent monitoring, such as those useful for tracking the sustainable development goals (SDG) adopted by the United Nations, especially Goal 15 ('Life on Land') indicator 15.1.1 for quantifying "forest area as a proportion of total land area".…”

Section: Introductionmentioning

confidence: 99%

“…Most of these studies use optical satellite data, especially Landsat, due to longer temporal coverage and ease of data accessibility. With the availability of active satellite data, many studies are increasingly utilizing radar data from sensors including the Advanced Land Observing Satellite (ALOS) based Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), RADARSAT-2, the Shuttle Radar Topography Mission (SRTM), for quantifying mangrove extent and other biophysical characteristics [15,[58][59][60][61][62][63][64][65]. While availability of Sentinel-1 from the European Space Agency (ESA) data has shown promise for continued use of radar data in mangrove mapping in the coming years, historical land cover mapping and monitoring often need to rely solely on optical remote sensing data due to the lack of radar data before the 1990s.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Mondal

Fatoyinbo

et al. 2019

Remote Sensing

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Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Mondal

Fatoyinbo

et al. 2019

Remote Sensing

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“…Dabrowska-Zielinska et al [43] and Conforth et al [52] also presented wetland classification using ALOS PALSAR data acquired in HV polarization. Flores De Santiago et al [53] found that a combination of HH and HV polarization modes from ALOS PALSAR data was better at separating forested wetlands using an object-based classification approach. Bwangoy et al [54] applied optical (Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) and JERS-1 (Japanese Earth Resources Satellite-1) data to classify the wetland and non-wetland classes of the Congo Basin.…”

Section: Classification Of Wetland Vegetation Habitat Typesmentioning

confidence: 99%

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

et al. 2016

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Abstract:The objectives of the study were to determine the spatial rate of CO 2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive-cross polarization) and VV (Vertical Transmit/Vertical Receive-like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ • ) calculated from Sentinel-1 data showed that LAI dominates the influence on σ • when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R 2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ • VH and σ • VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ • Sentinel-1 VH, σ • Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R 2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO 2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.

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“…In fact, Duke et al [13] have suggested that the services offered by mangrove ecosystems could become ecologically insignificant within the next 100 years if the current deforestation rates are maintained. Consequently, many techniques, including remote sensing, have been investigated in order to properly classify and monitor these forested wetlands (e.g., [14][15][16][17][18]). …”

Section: Introductionmentioning

confidence: 99%

Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data

et al. 2016

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Abstract:The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400-1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships.

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An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data

Cited by 36 publications

References 36 publications

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data

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