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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.

ContextLandscape ecologists often use thematic map data in their research. Greater familiarity with thematic map accuracy assessment protocols will enhance appropriate use and interpretation of map quality data.ObjectivesProvide an overview of thematic map accuracy assessment protocols and simple, non-quantitative guidelines to assess the quality of the thematic map data that landscape ecologists use in their research.MethodsSynthesis and interpretation of salient literature on map accuracy assessment.ConclusionsLandscape ecologists can adopt three simple rules to improve their use and interpretation of map data: (1) use the map quality data only if the accuracy assessment protocols adhere to rigorous, well-established standards for the sampling design, response design, and analysis; (2) focus on class-specific accuracy via user’s and producer’s accuracies (or the complementary measures commission and omission error rates); and (3) use the criterion that an accuracy assessment that reports class-specific accuracies accompanied by standard errors is a strong indicator of a rigorous assessment.

Tropical forests provide global climate regulation ecosystem services and their clearing is a significant source of anthropogenic greenhouse gas (GHG) emissions and resultant radiative forcing of climate change. However, consensus on pan-tropical forest carbon dynamics is lacking. We present a new estimate that employs recommended good practices to quantify gross tropical forest aboveground carbon (AGC) loss from 2000 to 2012 through the integration of Landsat-derived tree canopy cover, height, intactness and forest cover loss and GLAS-lidar derived forest biomass. An unbiased estimate of forest loss area is produced using a stratified random sample with strata derived from a wall-to-wall 30 m forest cover loss map. Our sample-based results separate the gross loss of forest AGC into losses from natural forests (0.59 PgC yr−1) and losses from managed forests (0.43 PgC yr−1) including plantations, agroforestry systems and subsistence agriculture. Latin America accounts for 43% of gross AGC loss and 54% of natural forest AGC loss, with Brazil experiencing the highest AGC loss for both categories at national scales. We estimate gross tropical forest AGC loss and natural forest loss to account for 11% and 6% of global year 2012 CO2 emissions, respectively. Given recent trends, natural forests will likely constitute an increasingly smaller proportion of tropical forest GHG emissions and of global emissions as fossil fuel consumption increases, with implications for the valuation of co-benefits in tropical forest conservation.

Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer's accuracy of 75.4% and user's accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level for REDD+ applications.

Recent advances in remote sensing enable the mapping and monitoring of carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where national forest inventories (NFI) are either non-existent or out of date. Here we demonstrate a method for estimating national-scale gross forest aboveground carbon (AGC) loss and associated uncertainties using remotely sensed-derived forest cover loss and biomass carbon density data. Lidar data were used as a surrogate for NFI plot measurements to estimate carbon stocks and AGC loss based on forest type and activity data derived using time-series multispectral imagery. Specifically, DRC forest type and loss from the FACET (Forêts d'Afrique Centrale Evaluées par Télédétection) product, created using Landsat data, were related to carbon data derived from the Geoscience Laser Altimeter System (GLAS). Validation data for FACET forest area loss were created at a 30-m spatial resolution and compared to the 60-m spatial resolution FACET map. We produced two gross AGC loss estimates for the DRC for the last decade (2000-2010): a map-scale estimate (53.3 ± 9.8 Tg C yr−1) accounting for whole-pixel classification errors in the 60-m resolution FACET forest cover change product, and a sub-grid estimate (72.1 ± 12.7 Tg C yr−1) that took into account 60-m cells that experienced partial forest loss. Our sub-grid forest cover and AGC loss estimates, which included smaller-scale forest disturbances, exceed published assessments. Results raise the issue of scale in forest cover change mapping and validation, and subsequent impacts on remotely sensed carbon stock change estimation, particularly for smallholder dominated systems such as the DRC.

From 1971 to 2012, in New York State, years with human Eastern equine encephalitis (EEE) were more strongly associated with the presence of Aedes canadensis, Coquillettidia perturbans and Culiseta melanura mosquitoes infected with the EEE virus (Fisher's exact test, one-sided 𝑃 = 0.005, 0.03, 0.03) than with Culiseta morsitans, Aedes vexans, Culex pipiens-restuans, Anopheles quadrimaculatus or Anopheles punctipennis (𝑃 = 0.05, 0.40, 0.33, 1.00, 1.00). The estimated relative risk of a case in a year in which the virus was detected 𝑣𝑠. not detected was 14.67 for Ae. canadensis, 6.38 for Cq. perturbans and 5.50 for Cs. morsitans. In all 5 years with a case, Cs. melanura with the virus was detected. In no year was there a case in the absence of Cs. melanura with the virus. There were 18 years with no case in the presence of Cs. melanura with the virus. Such observations may identify the time of increased risk, and when the methods may be used to prevent or reduce exposure to vector mosquito species in this geographic region.

Landscape pattern and composition metrics are potential indicators for broad-scale monitoring of change and for relating change to human and ecological processes. We used a probability sample of 20-km x 20-km sampling blocks to characterize landscape composition and pattern in five US ecoregions: the Middle Atlantic Coastal Plain, Southeastern Plains, Northern Piedmont, Piedmont, and Blue Ridge Mountains. Land use/and cover (LULC) data for five dates between 1972 and 2000 were obtained for each sample block. Analyses focused on quantifying trends in selected landscape pattern metrics by ecoregion and comparing trends in land cover proportions and pattern metrics among ecoregions. Repeated measures analysis of the landscape pattern documented a statistically significant trend in all five ecoregions towards a more fine-grained landscape from the early 1970s through 2000. The ecologically important forest cover class also became more fine-grained with time (i.e., more numerous and smaller forest patches). Trends in LULC, forest edge, and forest percent like adjacencies differed among ecoregions. These results suggest that ecoregions provide a geographically coherent way to regionalize the story of national land use and land cover change in the United States. This study provides new information on LULC change in the southeast United States. Previous studies of the region from the 1930s to the 1980s showed a decrease in landscape fragmentation and an increase in percent forest, while this study showed an increase in forest fragmentation and a loss of forest cover.

Fixed-width belt transects employed in surveys of irregular shaped regions will differ in length and, therefore, in area. When estimating density from such a sample, the unequal transect areas must be taken into account.A density estimator dividing the mean number of objects (e.g., plants or animals) per transect by the mean transect area is recommended. An alternative estimator, the mean density per transect, is applicable for equal-area transects but often has undesirable properties for unequal-area transects. The recommended density estimator is identified as a ratio estimator, and its standard error is derived from ratio estimation theory.

Three strategies are described for combining land-cover-map accuracy assessment with environmental monitoring sampling efforts. These strategies represent a gradient of integration from virtually none to almost complete integration. The non-integrated strategy is one in which no changes are imposed on the monitoring program's sampling and response designs, such that the monitoring data are used in whatever way possible to contribute to accuracy assessment. A more integrated strategy treats accuracy assessment as an add-on or attachment to monitoring, whereby the monitoring protocol is augmented to include an accuracy assessment response design protocol to obtain reference data. A fully integrated accuracy assessment and environmental monitoring strategy involves the three-tier monitoring framework proposed by the Committee on the Environment and Natural Resources. The three tiers comprise remotely sensed data that generate geographically extensive land-cover information, an extensive probability sample of ground-based measurements, and intensive study sites.