Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

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Human interfaces and urban change

Remote Sensing for Ecology and Conservation ◽

10.1093/oso/9780199219940.003.0019 ◽

2010 ◽

Author(s):

Ned Horning ◽

Julie A. Robinson ◽

Eleanor J. Sterling ◽

Woody Turner ◽

Sacha Spector

Keyword(s):

Species Richness ◽

North America ◽

Agricultural Land ◽

Population Change ◽

Human Impacts ◽

Remotely Sensed ◽

Conservation Priorities ◽

Data Sets ◽

Remotely Sensed Data ◽

Political Boundaries

For the first time in human history, more people live in urban areas than in rural areas, and the patterns of suburbanization and urban sprawl once characteristic of North America are now present globally (Obaid 2007). As conservation biologists seek to prioritize conservation efforts worldwide, urbanization and agricultural development emerge as two of the most extensive processes that threaten biodiversity. Suburban and rural sprawl are significant drivers of forest fragmentation and biodiversity loss (e.g., Murphy 1988; Radeloff et al. 2005). Data on human impacts is often averaged across political boundaries rather than biogeographic boundaries, making it challenging to use existing data sets on human demography in ecological studies and relate human population change to the changes in populations of other species. Remotely sensed data can make major contributions to mapping human impacts in ecologically relevant ways. For example, Ricketts and Imhoff (2003) assigned conservation priorities (based on species richness and endemism) for the United States and Canada using several different types of remotely sensed data. For mapping urban cover, they used the map of “city lights at night” from the Defense Meteorological Satellite Program (Imhoff et al. 1997) to classify land as urbanized or not urbanized. For mapping agricultural cover, they used the USGS North America Seasonal Land Cover map (Loveland et al. 2000), derived from the Advanced Very High Resolution Radiometer (AVHRR), lumping five categories to create an agricultural land class. For ecological data, they used a compilation of ecoregion boundaries combined with range maps for over 20,000 species in eight taxa (birds, mammals, butterflies, amphibians, reptiles, land snails, tiger beetles, and vascular plants; Ricketts et al. 1999). Analyzing these data, Ricketts and Imhoff (2003) identified a strong correlation between species richness and urbanization. Of the 110 ecoregions studied, 18 ranked in the top third for both urbanization and biodiversity (species richness, endemism, or both); some of the ecoregions identified as priorities were not identified by a previous biodiversity assessment that did not include the remotely sensed mapping of urbanization (Ricketts et al. 1999).

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Tropical montane forests are a larger than expected global carbon store

Biogeosciences ◽

10.5194/bg-11-2741-2014 ◽

2014 ◽

Vol 11 (10) ◽

pp. 2741-2754 ◽

Cited By ~ 49

Author(s):

D. V. Spracklen ◽

R. Righelato

Keyword(s):

Land Surface ◽

Forest Cover ◽

Slope Angle ◽

Remotely Sensed ◽

Data Sets ◽

Remotely Sensed Data ◽

Montane Forests ◽

Tropical Montane Forests ◽

Global Land ◽

Steep Slopes

Abstract. Tropical montane forests (TMFs) are recognized for the provision of hydrological services and the protection of biodiversity, but their role in carbon storage is not well understood. We synthesized published observations (n = 94) of above-ground biomass (AGB) from forest inventory plots in TMFs (defined here as forests between 23.5° N and 23.5° S with elevations ≥ 1000 m a.s.l.). We found that mean (median) AGB in TMFs is 271 (254) t per hectare of land surface. We demonstrate that AGB declines moderately with both elevation and slope angle but that TMFs store substantial amounts of biomass, both at high elevations (up to 3500 m) and on steep slopes (slope angles of up to 40°). We combined remotely sensed data sets of forest cover with high resolution data of elevation to show that 75% of the global planimetric (horizontal) area of TMF are on steep slopes (slope angles greater than 27°). We used our remote sensed data sets to demonstrate that this prevalence of steep slopes results in the global land surface area of TMF (1.22 million km2) being 40% greater than the planimetric area that is the usual basis for reporting global land surface areas and remotely sensed data. Our study suggests that TMFs are likely to be a greater store of carbon than previously thought, highlighting the need for conservation of the remaining montane forests.

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