The objectives of the effectiveness monitoring plan for the marbled murrelet (Brachyramphus marmoratus) include mapping nesting habitat at the start of the Northwest Forest Plan (NWFP) and estimating changes in that habitat every 5 years. Using Maxent species distribution models, we modeled the amount and distribution of probable nesting habitat in the murrelet’s range in the NWFP area in 1993, 1 year prior to the start of the NWFP, and 25 years later (2017). Within the higher probability nesting habitat, we then estimated the amount of contiguous habitat (core) versus the amount of habitat bounding core habitat (edge) and habitat scattered in small forest fragments (scatter). We considered this “core habitat” as the best habitat. Our models indicate that there were 1.51 million acre of higher probability nesting habitat over all lands in the murrelet’s range in Washington, Oregon, and California 1 year prior to the start of the NWFP in 1993. Of this, 0.14 million acre were identified as core habitat, which we defined as intact patches of higher probability nesting habitat >5.56 acre in size. In core habitat, we expected nest predation to be relatively low and the microclimate most favorable for murrelets. Most (68 percent, or 1.04 million acre) higher probability nesting habitat in 1993 was on federally administered lands, with 0.97 million acre (66 percent) in reserved land use allocations. We estimated that nonfederal lands contained 29 percent of all higher probability nesting habitat, but only 13 percent of all core habitat. Thus, the bulk of core habitat was on federal lands. We estimated a net loss of about 1.4 percent in higher probability nesting habitat across the NWFP area and 1.8 percent in core habitat from 1993 to 2017. Timber harvest and wildfire were the major causes of habitat loss on federal lands since the NWFP was implemented. Timber harvest was the primary cause of loss on state and other nonfederal lands, accounting for 99 percent of all attributable losses since 1993. The NWFP has been successful in conserving higher probability nesting habitat on federal lands across the NWFP area, but has been less successful in conserving core habitat. We anticipate that losses of habitat on federal lands will continue because of fires and timber harvest. As forests mature, some of these losses may be exceeded by recovery of currently unsuitable habitat within reserves. However, climate change offers a very real threat, and thus many gains may not be realized as the climate in the NWFP area becomes warmer, drier, and less favorable for developing forest conditions necessary for nesting murrelets. In addition, because losses of nesting habitat continue on private lands, incentives are needed to curb losses to better meet conservation objectives.
The Northwest Forest Plan (NWFP) is an ecosystem management plan for federal lands in the U.S. Pacific Northwest. To evaluate the NWFP’s effectiveness at conserving the marbled murrelet (Brachyramphus marmoratus), we estimated murrelet abundance at sea annually from 2000 to 2018 in inshore marine waters associated with the NWFP area. We divided this area of coastal waters into five geographic subareas corresponding with conservation zones established in the U.S. Fish and Wildlife Service’s recovery plan for the marbled murrelet. We used line transect distance estimation methods to account for detectability. Our abundance estimate for the planwide area in 2017 was about 23,000 murrelets. We did not find evidence for a linear trend for the overall NWFP area (0.3 percent per year). At the state scale, we found strong evidence for a declining linear trend in Washington (-3.9 percent per year). For Oregon, we found strong evidence for an increasing linear trend (2.0 percent per year). In California, we found strong evidence for an increasing linear trend (4.5 percent per year). At the individual conservation zone scale, we found strong evidence for a linear decline in Conservation Zone 1 (-4.9 percent per year), some evidence for a negative trend in Conservation Zone 2 (-3.0 percent per year, some evidence for positive linear trend in Conservation Zone 3 (1.4 percent per year), and strong evidence for a linear increase in Conservation Zone 4 (3.7 percent per year). Because of the extreme variability associated with the trend in Conservation Zone 5 (7.3 percent annual rate of change; 95 percent confidence interval: -4.4 to 20.3 percent, years 2000 to 2017), we concluded that there was no evidence for a trend in that conservation zone. These results indicate a pattern of decreasing at-sea abundance in the northern part of the plan area and increasing abundance to the south. We have no definitive explanation for this north-south pattern; however, one potential explanation might be the emigration of birds from other areas of the species’ range. A large-scale “marine heatwave” influenced the California Current during 2014–2016, which may have influenced distribution of murrelets, though the mechanism for this change in distribution is not yet clear. These at-sea population monitoring results indicate that the NWFP goal to stabilize and increase marbled murrelet population sizes has not yet been achieved.
While deforestation has traditionally been the focus for forest canopy disturbance detection, forest degradation must not be overlooked. Both deforestation and forest degradation influence carbon loss and greenhouse gas emissions and thus must be included in activity data reporting estimates, such as for the Reduced Emissions from Deforestation and Degradation (REDD+) program. Here, we report on efforts to develop forest degradation mapping capacity in Nepal based on a pilot project in the country’s Terai region, an ecologically complex physiographic area. To strengthen Nepal’s estimates of deforestation and forest degradation, we applied the Continuous Degradation Detection (CODED) algorithm, which uses a time series of the Normalized Degradation Fraction Index (NDFI) to monitor forest canopy disturbances. CODED can detect low-grade degradation events and provides an easy-to-use graphical user interface in Google Earth Engine (GEE). Using an iterative process, we were able to create a model that provided acceptable accuracy and area estimates of forest degradation and deforestation in Terai that can be applied to the whole country. We found that between 2010 and 2020, the area affected by disturbance was substantially larger than the deforested area, over 105,650 hectares compared to 2753 hectares, respectively. Iterating across multiple parameters using the CODED algorithm in the Terai region has provided a wealth of insights not only for detecting forest degradation and deforestation in Nepal in support of activity data estimation but also for the process of using tools like CODED in applied settings. We found that model performance, measured using producer’s and user’s accuracy, varied dramatically based on the model parameters specified. We determined which parameters most altered the results through an iterative process; those parameters are described here in depth. Once CODED is combined with the description of each parameter and how it affects disturbance monitoring in a complex environment, this degradation-sensitive detection process has the potential to be highly attractive to other developing countries in the REDD+ program seeking to accurately monitor their forests.
Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands,Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.
Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.
Forest succession is a fundamental ecological process which can impact the functioning of many terrestrial processes, such as water and nutrient cycling and carbon sequestration. Therefore, knowing the distribution of forest successional stages over a landscape facilitates a greater understanding of terrestrial ecosystems. One way of characterizing forest succession over the landscape is to use satellite imagery to map forest successional stages continuously over a region. In this study we use a forest succession model (ZELIG) and a canopy reflectance model (GORT) to produce spectral trajectories of forest succession from young to old-growth stages, and compared the simulated trajectories with those constructed from Landsat Thematic Mapper (TM) imagery to understand the potential of mapping forest successional stages with remote sensing. The simulated successional trajectories captured the major characteristics of observed regional mean succession trajectory with Landsat TM imagery for Tasseled Cap indices based on age information from the Pacific Northwest Forest Inventory and Analysis Integrated Database produced by Pacific Northwest Research Station, USDA Forest Service. Though the successional trajectories are highly nonlinear in the early years of succession, a linear model fits well the regional mean successional trajectories for brightness and greenness due to significant cross-site variations that masked the nonlinearity over a regional scale (R2=0.8951 for regional mean brightness with age; R2=0.9348 for regional mean greenness with age). Regression analysis found that Tasseled Cap brightness and greenness are much better predictors of forest successional stages than wetness index based on the data analyzed in this study. The spectral history based on multitemporal Landsat imagery can be used to effectively identify mature and old-growth stands whose ages do not match with remote sensing signals due to change occurred during the time between ground data collection and image acquisition. Multitemporal Landsat imagery also improves prediction of forest successional stages. However, a linear model on a stand basis has a limited predictive power of forest stand successional stages (adjusted R2=0.5435 using the Tasseled Cap indices from all four images used in this study) due to significant variations in remote sensing signals for stands at the same successional stage. Therefore, accurate prediction of forest successional stage using remote sensing imagery at stand scale requires accounting for site-specific factors influence remotely sensed signals in the future.
Passive acoustic monitoring is a promising method for monitoring rare and nocturnal species, and for tracking changes in forest wildlife biodiversity. We conducted simulations to compare and evaluate various passive acoustic sampling designs effectiveness for monitoring spotted owl (Strix occidentalis caurina) population trends. We found that each design was effective for detecting a decline (or stability) in spotted own populations within 10 years with even a moderate amount of sampling. There are however, important considerations and tradeoffs among the various design options. Often, estimated changes in use of the landscape were biased with a consistently lower magnitude of change compared to simulated changes in the population. Although this method has challenges, passive acoustic monitoring can be used to effectively monitor northern spotted owls in the Pacific Northwest.
