Geology topics

Matthew C. Hansen

Publications and source records attributed to Matthew C. Hansen.

4 recordsLinked to original sources

USGS2018

Monitoring global tree mortality patterns and trends. Report from the VW symposium ‘Crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health’

From the 21stto the 23rdJune 2017, the Herrenhausen castle inHannover/Germany hosted a diverse and large crowd with morethan 70 tree physiologists, forest ecologists, forest inventoryexperts, remote-sensing scientists, and vegetation modele rs. Par-ticipants from six continent s and from more than 20 countriesgathered to discuss how to improve the scientiﬁc determination ofglobal-scale patterns, drivers, and trends of a threatening phe-nomenon: the apparent emergence of recent widespread treemortality events in diverse forests around the world.Continuin g the theme of a workshop held at the Max-PlanckInstitute for Biogeochemistry in Jena (Germany) in 2014 (Hart-mann et al., 2015), the Hanover meeting intended to developapproaches, tools and collaborative actions to accelerate progress inaddressing regional patterns and trends of tree mortality (Williamset al., 2013). Over the last decade climate change related treemortality events have been increasingly reported around the globe(van Mantgem et al., 2009; Carnicer et al., 2011; Peng et al., 2011;Brienen et al., 2015), but to what degree this is a global trend,amplifying under increasing climate change, remains uncertain.

New Phytologist↗

USGS2015

Characterizing Congo Basin rainfall and climate using Tropical Rainfall Measuring Mission (TRMM) satellite data and limited rain gauge ground observations

Quantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r 2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.

Journal of Applied Meteorology and Climatology↗

USGS2008

Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing “hotspots.” Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.

Proceedings of the National Academy of Sciences of↗

USGS2008

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.

Remote Sensing of Environment↗