Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest ...loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
Over the past 3 decades the Arctic has seen substantial warming. Previous local to regional scale studies have shown a considerable reduction in the size of lakes in this region. The subsequent ...exposure of carbon‐ and methane‐rich sediments and the direct changes in surface albedo feed back into the drivers of regional and global climate change. Understanding and quantifying changes in the Arctic is a critical component of climate modeling due to the cooling effect of the Arctic on the global climate. The current work utilizes global satellite data from the Moderate Resolution Imaging Spectro‐radiometer (MODIS) instrument to investigate changes in lakes across Canada between 2000 and 2009. The results show a net reduction of more than 6,700 km2 in the surface area of water in lakes across Canada. Modest gains in the southern regions are offset by larger losses in surface area farther north. Additionally, spatial analysis shows that the lakes showing change are clustered in groups. This suggests that local variability may play a role in the observed changes. Further work is needed to extend the analysis to the circumpolar Arctic.
Key Points
Arctic lakes are numerous and are changing
Changes are greater and more widespread than previous studies indicate
Changes can be seen using moderate resolution remote sensing
An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) ...procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.
The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying ...high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.
We investigated normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers and found regions in North America that experienced marked increases in ...annual photosynthetic capacity at various times from 1982 to 2005. Inspection of these anomalous areas with multi-resolution data from Landsat, Ikonos, aerial photography, and ancillary data revealed a range of causes for the NDVI increases: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Vegetation in areas in the high Northern Latitudes appear to be solely impacted by climatic influences. In other areas examined, the impact of anthropogenic effects is more direct. The pattern of NDVI anomalies over longer time periods appear to be driven by long-term climate change but most appear to be associated with climate variability on decadal and shorter time scales along with direct anthropogenic land cover conversions. The local variability of drivers of change demonstrates the difficulty in interpreting changes in NDVI and indicates the complex nature of changes in the carbon cycle within North America. Coarse scale analysis of changes could well fail to identify the important local scale drivers controlling the carbon cycle and to identify the relative roles of disturbance and climate change. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.
Using Landsat images acquired since early 1970s, we have mapped the forest cover and change between 1989 and 2000, and estimated forest area in 1973 in Paraguay's Atlantic Forest Ecoregion (PAFE). ...The results revealed that as of 1973, 73.4% of the PAFE region was covered by forest. Since then, the proportion of forested area was quickly reduced to 40.7% by 1989 and further down to 24.9% by 2000. Two competing deforestation processes contributed to this rapid forest loss, with the first being driven by settlers and the second by large private land owners. During the 1989–2000 period, 80% of deforested areas were cleared by private land owners and 20% by the settlers. Protected areas slowed down forest loss within their boundaries, but not in their surrounding areas. The average percent forest loss in the area within 5 km from the boundary of Paraguay's major forested protected areas was 39% during the 1989–2000 period, which was essentially the same as that for the entire PAFE region during the same period. The high rates of forest loss in the areas surrounding the protected areas not only left the protected areas highly isolated as ecological “islands”, they may also be precursors to rapid forest loss within the protected areas. These protected areas are critical to the conservation of many species endemic or limited to the PAFE region and surrounding areas, and should be continuously monitored using recent and future satellite observations.
This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be ...included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.
Data from the Moderate Resolution Imaging Spectro-radiometer (MODIS) on-board the Earth Observing System Terra and Aqua satellites are processed using a land water mask to determine when an algorithm ...no longer needs to be run or when an algorithm needs to follow a different pathway. Entering the fourth reprocessing (Collection 6 (C6)) the MODIS team replaced the 1 km water mask with a 500 m water mask for improved representation of the continental surfaces. The new water mask represents more small water bodies for an overall increase in water surface from 1% to 2% of the continental surface. While this is still a small fraction of the overall global surface area the increase is more dramatic in certain areas such as the Arctic and Boreal regions where there are dramatic increases in water surface area in the new mask. MODIS products generated by the on-going C6 reprocessing using the new land water mask show significant impact in areas with high concentrations of change in the land water mask. Here differences between the Collection 5 (C5) and C6 water masks and the impact of these differences on the MOD04 aerosol product and the MOD11 land surface temperature product are shown.
Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area ...represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size.
The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite ...remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.
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