Raghunandan Hills Reserve is an important protected area in Bangladesh that supports some remnant patches of natural forest and is the habitat of several globally threatened primates including Western Hoolock Gibbon, Northern Pig-tailed Macaque, and Capped Langur. However, deforestation and forest degradation due to anthropogenic factors, such as illegal logging and fuelwood collection are age-old problems at Raghunandan. The areas of the reserve vulnerable to future conversions due to the possible proximate or underlaying causes were unknown. This study analyzed the historical trend of forest and land-use/landcover transitions at Raghunandan Hills Reserve from 1995 until 2015 at a 10-year interval using Monte Carlo spectral unmixing and knowledge-based classification approaches to Landsat satellite images in Claslite and ArcGIS software. Based on the past trend, it then predicted the future trend of forest land-use/landcover transitions for 2025 and 2035 using an artificial multi-layer perceptron neural network with Markov Chain machine learning algorithm integrated into the land change modeler module of IDRISI/TerrSet software. Results indicated that ∼30 % to 35% of the total area of the reserve was covered by forest, which included patches of natural forest and plantations, whereas the remaining area was occupied by non-forest categories like scattered degraded forests, grasses, and shrubs. Forest cover declined during 1995–2005, and then increased slightly during 2005–2015 due to afforestation activities. This trend is likely to continue in the future with forest cover occupying nearly 40% of the reserve by 2025 and 2035. Along with identifying the areas where the forest is likely to be expanded, the areas of the reserve vulnerable to deforestation (hotspots) were also highlighted and quantified in the form of maps and statistics. The findings have useful implications for any forest conservation initiatives including the global climate change mitigation program reducing emissions from deforestation and forest degradation+, which requires identifying at-risk areas of planned and unplanned deforestation.
Deforestation and forest degradation are two important contributors to atmospheric emissions of carbon. To claim carbon credit for conservation plus enhancement of existing forest carbon stock under reducing emissions from deforestation and forest degradation (REDD+) program, a post-Kyoto climate change mitigation initiative, developing countries need to assess the level of respective forest biomass-carbon and corresponding emissions of carbon in a complete and verifiable manner. We aimed to develop a comprehensive approach following the recommended inter-governmental panel on climate change good practice guidelines to estimate emission factors and emissions of biomass-carbon due to deforestation and forest degradation at Raghunandan Hills Reserved Forest, Bangladesh, for REDD+ implications. The estimation was done using a combination of field and satellite data employing geospatial techniques. The critical assessment provided a comprehensive estimate of the emission factors and emissions of biomass-carbon in the form of maps and statistics with acceptable accuracy. The approaches and findings of this study may have important scientific and management implications including providing baseline information for biomass-carbon stock-related concerns such as REDD+.
Reducing emissions from deforestation and forest degradation (REDD+) has emerged as a global climate change mitigation initiative under negotiation by the United Nations Framework Convention on Climate Change aimed at providing financial support to the developing countries for conserving respective forests. To implement the REDD+ initiative, developing countries need to estimate, among other necessitates, the activity data (i.e., pattern and process) of respective deforestation and forest degradation. Bangladesh is steadily progressing through its REDD+ roadmap. However, an important research issue to address includes using remote sensing technology to detect activity data for deforestation in a spatially explicit manner following the recommended good practice guidelines by the Intergovernmental Panel on Climate Change. This study mapped the activity data for deforestation of a mixed forest in Bangladesh during 1995 to 2015, applying Monte-Carlo spectral unmixing classification algorithm to Landsat images in CLASlite software. The classification was verified using independently drawn reference points from high-resolution Google Earth images. A postclassification comparison method was applied to generate landcover transition matrices. The outputs were highly accurate maps (overall accuracy >90 % ) and statics of activity data for deforestation of the study area. The approaches and findings may have significant implications in adopting any REDD+ project in Bangladesh.
After fossil fuel burning, deforestation and forest degradation are the second largest contributors to greenhouse gas emissions to the atmosphere. In order to claim the carbon credit under the reducing from deforestation and forest degradation (REDD+) scheme, a United Nation’s Framework Convention on Climate Change initiative for climate change mitigation, developing countries are required to prepare national reference emission levels for forests on the basis of historic data and national circumstances. Part of developing reference emission levels includes quantifying location, pattern, and rate of historic forest degradation, which are also called in a word the activity data for forest degradation. Applying Monte-Carlo spectral unmixing technique to Landsat images in the CLASlite® algorithm followed by a knowledge-based classification approach, this research quantified the activity data for forest degradation at Raghunandan Hills Reserve (6143 ha) in Bangladesh. Moderate spatial resolution Landsat images were able to detect the activity data for degradation in a spatially explicit manner with high accuracy (>90 % ). The research approach and findings can serve as valuable information for any future national level initiative for developing activity data for REDD+ projects.
Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R 2 =0.60 , n=41 ), ground vegetation cover (R 2 =0.43 , n=41 ), bank width/height ratio (R 2 =0.41 , n=41 ), and valley confinement (producer’s accuracy=100% , n=9 ). Cross-validation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.
Remotely sensed spectral indices are used in a range of environments for estimating properties of vegetation, soil, atmospheric, and water features. Here, the development of an index sensitive to the amount of live coral, using in situ spectral reflectance data from Australia and Hawaii is outlined. From an initial spectral reflectance library of common reef benthic features, linear spectral mixing was used to create mixed reflectance signatures that represented image pixels in a reef environment. The correlation between the proportion of total live coral and the mixed reflectance signal at each wavelength was calculated to determine the wavelengths sensitive to variations in the amount of live coral. First and second derivatives of the reflectance spectra, in addition to simple band ratios, were also tested. The same processing and analysis procedures were then followed after simulating the spectral mixtures under different depths and levels of suspended organic content using a radiative transfer model (Hydrolight 4.1). Results show that the second derivative of reflectance at 564 nm was one of the wavelength regions most sensitive to variations in live coral cover and least sensitive to variations in water depth and quality. Subsequent research will present the applicability of this technique to hyperspectral image data.
Aerial photography interpretation is the most common mapping technique in the world. However, unlike an algorithm-based classification of satellite imagery, accuracy of aerial photography interpretation generated maps is rarely assessed. Vegetation communities covering an area of 530 km2 on Bullo River Station, Northern Territory, Australia, were mapped using an interpretation of 1:50,000 color aerial photography. Manual stereoscopic line-work was delineated at 1:10,000 and thematic maps generated at 1:25,000 and 1:100,000. Multivariate and intuitive analysis techniques were employed to identify 22 vegetation communities within the study area. The accuracy assessment was based on 50% of a field dataset collected over a 4 year period (2006 to 2009) and the remaining 50% of sites were used for map attribution. The overall accuracy and Kappa coefficient for both thematic maps was 66.67% and 0.63, respectively, calculated from standard error matrices. Our findings highlight the need for appropriate scales of mapping and accuracy assessment of aerial photography interpretation generated vegetation community maps.
Spatio-temporally variable information on total vegetation cover is highly relevant to water quality and land management in river catchments adjacent to the Great Barrier Reef, Australia. A time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR; 2000-2006) and its underlying biome classification (MOD12Q1) were compared to national land cover and regional, remotely sensed products in the dry-tropical Burdekin River. The MOD12Q1 showed reasonable agreement with a classification of major vegetation groups for 94% of the study area. We then compared dry-seasonal, quality controlled MODIS FPAR observations to (i) Landsat-based woody foliage projective cover (wFPC) (2004) and (ii) MODIS bare ground index (BGI) observations (2001-2003). Statistical analysis of the MODIS FPAR revealed a significant sensitivity to Landsat wFPC-based Vegetation Structural Categories (VSC) and VSC-specific temporal variability over the 2004 dry season. The MODIS FPAR relation to 20 coinciding MODIS BGI dry-seasonal observations was significant ( < 0.001) for homogeneous areas of low wFPC. Our results show that the global MODIS FPAR can be used to identify VSC, represent VSC-specific variability of PAR absorption, and indicate that the amount, structure, and optical properties of green and non-green vegetation components contribute to the MODIS FPAR signal.
Our ability to map coral reef environments using remote sensing has increased through improved access to: satellite images and field survey data at suitable spatial scales, and software enabling the integration of data sources. These data sets can be used to provide validated maps to support science and management decisions. The objective of this paper was to compare two methods for calibrating and validating maps of coral reef benthic communities derived from satellite images captured over a variety of Coral Reefs The two methods for collecting georeferenced benthic field data were: 1), georeferenced photo transects and 2), spot checks. Quickbird imagery was acquired for three Fijian coral reef environments in: Suva, Navakavu and Solo. These environments had variable water clarity and spatial complexity of benthic cover composition. The two field data sets at each reef were each split, and half were used for training data sets for supervised classifications, and the other half for accuracy assessment. This resulted in two maps of benthic communities with associated mapping accuracies, production times and costs for each study-site. Analyses of the spatial patterns in benthic community maps and their Overall and Tau accuracies revealed that for spatially complex habitats, the maps produced from photo transect data were twice as accurate as spot check based maps. In the context of the reefs examined, our results showed that the photo- transect method was a robust procedure which could be used in a range of coral reef environments to map the benthic communities accurately. In contrast, the spot check method is a fast and low cost approach, suitable for mapping benthic communities which have lower spatial complexity. Our findings will enable scientists, technicians and managers to select appropriate methods for collecting field data to integrate with high spatial resolution multi-spectral imagery to create validated coral reef benthic community maps.
Optical remote sensing has been used to map and monitor water quality parameters such as the concentrations of
hydrosols (chlorophyll and other pigments, total suspended material, and coloured dissolved organic matter). In the
inversion / optimisation approach a forward model is used to simulate the water reflectance spectra from a set of
parameters and the set that gives the closest match is selected as the solution. The accuracy of the hydrosol retrieval is
dependent on an efficient search of the solution space and the reliability of the similarity measure. In this paper the
Particle Swarm Optimisation (PSO) was used to search the solution space and seven similarity measures were trialled.
The accuracy and precision of this method depends on the inherent noise in the spectral bands of the sensor being
employed, as well as the radiometric corrections applied to images to calculate the subsurface reflectance. Using the
Hydrolight® radiative transfer model and typical hydrosol concentrations from Lake Wivenhoe, Australia, MERIS
reflectance spectra were simulated. The accuracy and precision of hydrosol concentrations derived from each similarity
measure were evaluated after errors associated with the air-water interface correction, atmospheric correction and the
IOP measurement were modelled and applied to the simulated reflectance spectra. The use of band specific empirically
estimated values for the anisotropy value in the forward model improved the accuracy of hydrosol retrieval. The results
of this study will be used to improve an algorithm for the remote sensing of water quality for freshwater impoundments.
Monitoring of coral reef environments require accurate, timely and relevant information on their composition and
condition. These environments are challenging to map due to their variation in reef type, remoteness, extent, benthic
cover composition and variable water clarities. This work evaluates the accuracy, cost and relevance of eight commonly
used benthic cover mapping approaches applied in three different coral reef environments in Fiji. The eight mapping
techniques varied in field data source (local knowledge, point and transect surveys), image data (Quickbird 2 and
Landsat 5 TM), level of image correction (none or atmospheric) and processing approaches (delineation and supervised
classification). The eight mapping approaches were assessed in terms of their: map accuracy; production time and cost.
Qualitative assessment was carried out by map users representing the local marine monitoring agencies. These map
assessments showed that users and producers preferred mapping approaches based on: supervised classification of
Quickbird imagery integrated with a basic field data. This approach produced an accurate map within a short time; with
low cost that suited the user's purpose. The findings from this work demonstrate how variations in coral reef
environments, and map purpose and resources management requirements affected the user's selection of a suitable
mapping approach.
The high level of success of estimating photosynthetic vegetation from multispectral satellite sensors at regional scales has not been repeated for non-photosynthetic vegetation and bare ground. Therefore regional scale estimates of total vegetation from multispectral sensors are largely underestimated with implications for a wide range of agricultural and environmental applications. Recent research using simulated data showed that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) had the potential to provide reliable estimates of bare ground and total vegetation. This study built on that research and found that estimates of bare ground retrieved from ASTER short-wave infrared imagery using linear spectral unmixing correlated well with field measurements (RMSE < 0.1, r2 > 0.7). Image endmember libraries required for spectral unmixing were extracted from the image data using a combination of field knowledge and the lignin and cellulose absorption index. The most reliable results were found by applying a sum-constraint to the unmixing models and tying the signatures at wavebands that corresponded to cellulose or clay-hydroxyl absorption features. The results of this research show that ASTER can improve the estimates of total vegetation extracted from satellite imagery for environmental studies at regional scales.
The spatial extent of Australia's forest estate lends itself to the application of an integrated multi-scale approach for the identification of koala habitat extent and condition. A multiple-scale methodology initially identifies forests using AVHRR, TM or predictive modelling to give a regional-state coverage. Then, the application of calibrated geometrical-optical models is used within forests to identify communities and structural properties such as projected crown cover, stem density and biomass. Finally, habitat quality at scale of individual trees is evaluated. This work reports on approaches used to map the location of trees that indicate high habitat quality for koalas and possibly other foliovores such as possums and greater gliders. An approach has been developed for using canopy level field spectrometer measurements to transform hyperspectral data into four components representing within-pixel proportions of target and non-target forest species, grasses and other non-tree components, and shade. A canopy detection routine is then used to produce canopy scale maps of individual species. This approach was tested using an Analytical Spectral Devices spectrometer in conjunction with CASI image data in a mixed coastal eucalypt forest at Koala beach, Pottsville, New South Wales. The maps of target species location were found to be 90.1% accurate when compared to field located species.
Remote Sensing of Inland, Coastal, and Oceanic Waters
18 November 2008 | Noumea, New Caledonia
Course Instructor
SC925: Applied Remote Sensing for Coral Reefs and Seagrass Mapping and Conservation
This course provides attendees with:
1) a complete review on the current status of coral reef and seagrass remote sensing,
2) a step-by-step procedure to collect field data to be used for image-based mapping in both benthic environments, and
3) an introduction to the mathematics needed for optimization of MPA networks, and an example of how to use maps to design such networks.
Many practical and useful examples from case studies are included throughout.
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