Publications

1.8 to 2.7 million hectares per year since 2000 (5).That for Indonesia averaged 0.7 million hectares per year since 2000 (6).

Images using reflected visible and near-infrared data and images using passive microwave data were compared in terms of their usefulness for characterizing land-cover types in South America and Africa. The former images are of the normalized difference vegetation index (NDVI) subsampled to approximately 15-20 km resolution in the NOAA global vegetation index product. The latter images of the microwave polarization difference temperature (MPDT) are derived from the difference between horizontally and vertically polarized radiation in the 37 GHz band. Results of maximum-likelihood classifications applied to multi-temporal data sets indicate that, overall, the NDVI data sets are substantially better than the MPDT data sets for land-cover characterization. However, the greater sensitivity of the MPDT data in semi-arid areas results in their superior performance for some classes in these areas. The combined use of MPDT and NDVI data sets show clear synergistic benefits in using the two data sets. However, the evidence suggests that for most cover types, increasing the temporal frequency of the NDVI images is more advantageous than incorporating MPDT data sets.

Over the past 50 years, the forests of western Washington and Oregon have been extensively harvested for timber. This has resulted in a heterogeneous mosaic of remaining mature forests, clear-cuts, new plantations, and second-growth stands that now occur in areas that formerly were dominated by extensive old-growth forests and younger forests resulting from fire disturbance. Traditionally, determination of seral stage and stand condition have been made using aerial photography and spot field observations, a methodology that is not only time- and resource-intensive, but falls short of providing current information on a regional scale. These limitations may be solved, in part, through the use of multispectral images which can cover large areas at spatial resolutions in the order of tens of meters. The use of multiple images comprising a time series potentially can be used to monitor land use (e.g. cutting and replanting), and to observe natural processes such as regeneration, maturation and phenologic change. These processes are more likely to be spectrally observed in a time series composed of images taken during different seasons over a long period of time. Therefore, for many areas, it may be necessary to use a variety of images taken with different imaging systems. A common framework for interpretation is needed that reduces topographic, atmospheric, instrumental, effects as well as differences in lighting geometry between images. The present state of remote-sensing technology in general use does not realize the full potential of the multispectral data in areas of high topographic relief. For example, the primary method for analyzing images of forested landscapes in the Northwest has been with statistical classifiers (e.g. parallelepiped, nearest-neighbor, maximum likelihood, etc.), often applied to uncalibrated multispectral data. Although this approach has produced useful information from individual images in some areas, landcover classes defined by these techniques typically are not consistent for the same scene imaged under different illumination conditions, especially in the mountainous regions. In addition, it is difficult to correct for atmospheric and instrumental differences between multiple scenes in a time series. In this paper, we present an approach for monitoring forest cutting/regrowth in a semi-mountainous portion of the southern Gifford Pinchot National Forest using a multisensor-time series composed of MSS, TM, and AVIRIS images.

No abstract is provided for this article.

No abstract is provided for this article.

No abstract is provided for this article.

We used historical Landsat imagery to monitor forest degradation from charcoal production in the main supplying region of the Mozambican capital, Maputo, during a ten-year period (2008–2018). We applied a change detection method that exploits temporal NDVI dynamics associated with charcoal production. This forest degradation temporal sequence exposes the magnitude and the spatial and temporal dynamics of charcoal production, which is the main forest degradation driver in sub-Saharan Africa. The annual area under charcoal production has been steadily increasing since 2008 and reached 11 673 ha in 2018. The total forest degraded extent in the study area during the 10-year study period covered 79 630 ha, which represents 68% of the available mopane woodlands in 2008. Only 5% of the available mopane woodlands area remain undisturbed in the study area. Total gross carbon emissions associated charcoal production during this 10-year period were estimated in 1.13 Mt. These results mark forest degradation from charcoal production as the main driver of forest cover change in southern Mozambique. They also denote that, while charcoal production may be relatively localized in space, its implications for forest cover change and carbon emissions in a sub-Saharan African context are relevant at larger geographical scales. This study represents a proof of concept of the feasibility of medium resolution Earth observation data to monitor forest degradation from charcoal production in the context of the growing urban energy demand. It also highlights the potential opportunities to improve REDD+ monitoring, reporting and verification efforts in sub-Saharan Africa as a first step toward designing effective management and policy interventions.

Determining the physical dimensions of hypervelocity impact structures is challenging due to erosion of their primary relief features on Earth. Critical measurements, such as outer rim diameter, are important for estimating the kinetic energy released and subsequent environmental effects. We developed a Radial Profile Analysis System algorithm, which uses a digital elevation model (DEM) to identify topographic rings surrounding a complex impact structure through an iterative process that assigns the most consistently identified positive relief ring as the apparent outer rim. We investigated five terrestrial impact structures across a range of apparent diameters whose physical dimensions are well established from geological or geophysical studies. Multiple DEM data sets from 2 to 30 m horizontal resolution were evaluated to determine the role of spatial resolution in estimating apparent outer rim diameters. The most reliable predictions were achieved using the 12 m TanDEM-X data set with an estimated error ≤16%. We then achieved an estimated error ≤8% when applying the algorithm to three Martian peak ring impact structures (Kepler, Lowell, and Lyot) using 200 m resolution DEMs, and 1%–13% when applied to a 600 m resolution DEM of the Mead multi-ringed impact structure on Venus. Our results indicated that apparent outer rim diameters of complex impact structures can be estimated using these methods with reasonable reliability, but prediction efficacy decreased with decreasing DEM vertical fidelity. Application of these methods to additional impact structures is required to quantify prediction uncertainty before applying this methodology to more recent impact structures with questionable diameters.