Spatial distribution of forest aboveground biomass estimated from remote sensing and forest inventory data in New England, USA.

Abstract We combined satellite (Landsat 7 and Moderate Resolution Imaging Spectrometer) and U.S. Department of Agriculture forest inventory and analysis (FIA) data to estimate forest aboveground biomass (AGB) across New England, USA. This is practical for large-scale carbon studies and may reduce uncertainty of AGB estimates. We estimate that total regional forest AGB was 1,867 teragram (1012, dry weight) in 2001, with a mean AGB density of 120 Mg/ha (Standard deviation = 54 Mg/ha) ranging from 15 to 240 Mg/ha within a 95% percentile. The majority of regional AGB density was in the range of 80 to 160 Mg/ha (58.2%). High AGB densities were observed along the Appalachian Mountains from northwestern Connecticut to the Green Mountains in Vermont and White Mountains in New Hampshire, while low AGB densities were concentrated in the Downeast area of Maine (ME) and the Cape Cod area of Massachusetts (MA). At the state level, the averaged difference in mean AGB densities between simulated and FIA (as reference) was -2.0% ranging from 0% to -4.2% with a standard error of 3.2%. Within the 95% confidence interval the differences between FIA and simulated AGB densities ranged from 0 to 6% (absolute value). Our study may provide useful information for regional fuel-loading estimates

Fire models and methods to map fuel types: The role of remote sensing.

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information andmethods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques

Identifying Areas Affected By Fires In Sumatra Based On TIME Series Of Remotely Sensed Fire Hotspots And Spatial Modeling

Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots

IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING

Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires.  Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments.  Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots

Age, extent and carbon storage of the central Congo Basin peatland complex

Peatlands are carbon-rich ecosystems that cover just three per cent of Earth's land surface, but store one-third of soil carbon. Peat soils are formed by the build-up of partially decomposed organic matter under waterlogged anoxic conditions. Most peat is found in cool climatic regions where unimpeded decomposition is slower, but deposits are also found under some tropical swamp forests. Here we present field measurements from one of the world's most extensive regions of swamp forest, the Cuvette Centrale depression in the central Congo Basin. We find extensive peat deposits beneath the swamp forest vegetation (peat defined as material with an organic matter content of at least 65 per cent to a depth of at least 0.3 metres). Radiocarbon dates indicate that peat began accumulating from about 10,600 years ago, coincident with the onset of more humid conditions in central Africa at the beginning of the Holocene. The peatlands occupy large interfluvial basins, and seem to be largely rain-fed and ombrotrophic-like (of low nutrient status) systems. Although the peat layer is relatively shallow (with a maximum depth of 5.9 metres and a median depth of 2.0 metres), by combining in situ and remotely sensed data, we estimate the area of peat to be approximately 145,500 square kilometres (95 per cent confidence interval of 131,900-156,400 square kilometres), making the Cuvette Centrale the most extensive peatland complex in the tropics. This area is more than five times the maximum possible area reported for the Congo Basin in a recent synthesis of pantropical peat extent. We estimate that the peatlands store approximately 30.6 petagrams (30.6 × 10(15) grams) of carbon belowground (95 per cent confidence interval of 6.3-46.8 petagrams of carbon)-a quantity that is similar to the above-ground carbon stocks of the tropical forests of the entire Congo Basin. Our result for the Cuvette Centrale increases the best estimate of global tropical peatland carbon stocks by 36 per cent, to 104.7 petagrams of carbon (minimum estimate of 69.6 petagrams of carbon; maximum estimate of 129.8 petagrams of carbon). This stored carbon is vulnerable to land-use change and any future reduction in precipitation

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently

Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas

Fire risk assessment is one of the most important components in the management of fire that offers the framework for monitoring fire risk conditions. Whilst monitoring fire risk conditions commonly revolved around field data, Remote Sensing (RS) plays key role in quantifying and monitoring fire risk indicators. This study presents a review of remote sensing data and techniques for fire risk monitoring and assessment with a particular emphasis on its implications for wildfire risk mapping in protected areas. Firstly, we concentrate on RS derived variables employed to monitor fire risk conditions for fire risk assessment. Thereafter, an evaluation of the prominent RS platforms such as Broadband, Hyperspectral and Active sensors that have been utilized for wildfire risk assessment. Furthermore, we demonstrate the effectiveness in obtaining information that has operational use or immediate potentials for operational application in protected areas (PAs). RS techniques that involve extraction of landscape information from imagery were summarised. The review concludes that in practice, fire risk assessment that consider all variables/indicators that influence fire risk is impossible to establish, however it is imperative to incorporate indicators or variables of very high heterogeneous and “multi-sensoral or multivariate fire risk index approach for fire risk assessment in PA.Keywords: Protected Areas, Fire Risk conditions; Remote Sensing, Wildfire risk assessmen

Integration of remote sensing and GIS in studying vegetation trends and conditions in the gum arabic belt in North Kordofan, Sudan

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently