Active Projects

Mapping Original Forest Cover in Haiti

Ecosystem Threat Assessment and Protected Area Strategy for the Massif de la Hotte Key Biodiversity Area, Haiti

Deforestation in Haiti is well documented, with an estimated original forest cover remaining of approximately 1%. This widespread deforestation is primarily a result of hundreds of years of spreading subsistence agriculture and cutting for cooking fuel. Most of the remnant stands of original forest cover in Haiti are highly fragmented, with the last remnants primarily found in Massif de la Hotte mountain range of the southwest. This area has been identified as a Key Biodiversity Area (KBA), defined by the International Union for Conservation of Nature (IUCN) as a “place of international importance for the conservation of biodiversity”. While much of this region itself has been completely denuded of forest cover, it is a critical refuge for Haiti’s remaining biodiversity, as it has been estimated that over 80% of the animal species in the Massif de la Hotte identified by the IUCN as threatened are endemic to Haiti and the majority are found only within the Massif de la Hotte. Outside of the KBA, it is not known where or if any remnant original forest stands exist that are capable of supporting Haiti’s threatened species. It is therefore critical that all of these original forest patches be located and prioritized for conservation.

The role of our lab is to provide high-resolution forest cover data for the entire country, based on the most recent LANDSAT satellite imagery. These maps indicate the current location of original forest, allowing the project teams to best assess the current conservation threats and their precise locations.

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Carbon Monitoring System (CMS)

An Historically Consistent and Broadly Applicable Monitoring, Reporting, and Verification System Based on Lidar Sampling and Landsat Time-series (Tested in the US, and applied to the US NGHGI reporting system)

We are developing a pilot Monitoring, Reporting, and Verification (MRV) accounting system that could be used by developing countries within the context of the United Nations (UN) REDD (Reducing Emissions from Deforestation and Forest Degradation) Programme. Because one system will not fit all needs, we consider different biomass estimation frameworks and different components for inclusion in the system. Design-based inference is commonly applied to a sample field plot network. But field plot networks are expensive to install and maintain. Sampling with lidar strips, supported by a smaller set of plots may be an attractive alternative that is highly relevant to many REDD countries, as is the use of Landsat for disturbance monitoring. Biomass estimation uncertainties associated with use of these different datasets in a design-based inference framework are being examined. We are also developing and testing estimators that rely primarily on Landsat data within a model-based inference framework. The contributions from Landsat are the current (e.g., 2013) spectral response and metrics that describe disturbance history derived from a time series leading up to the current date. In this context, either plot data or lidar data can be used to parameterize the model and we are contrasting the uncertainty effects of these datasets.

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NAFD

North American Forest Dynamics

The North American Forest Dynamics (NAFD) project is exploiting the Landsat historical record to develop a quantitative understanding of forest disturbance patterns across the conterminous US.  The primary components of this study are: 

  • Mapping. We are implementing algorithms to develop wall-to-wall annual maps of US forest disturbance history between 1985 and 2010.  This approach not only reduces the errors encountered in early sampling efforts but also tests automation of processing and analysis procedures that have previously been carried out in a handcrafted fashion. 
     
  • Quality assessment (QA). The products of this comprehensive analysis, maps and statistics, will be subjected to a rigorous QA to provide quantitative assessments of the accuracy of these products, and provide independently derived, sample based disturbance estimates.  This will support interested users in understanding the reliability of our maps. 
     
  • Disturbance causal agent assignment. We are evaluating various attributes of the disturbance analyses, including the recovery trajectories and spatial patterns of the disturbed forest areas, to determine how successfully the causal factors that led to the observed disturbances may be extracted from the Landsat and related observations.
 
     
  • Post-disturbance recovery characterization. An investigation of the satellite-observed forest recovery trajectories is being modeled in conjunction with USFS inventory measurements.  This work supports extended use of forest disturbance history analysis by linking the observed forest recovery with field measured growth dynamics.

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LCMS


 

 

Landscape Change Monitoring System

The Landscape Change Monitoring (LCMS) is an emerging remote sensing-based system for mapping and monitoring land cover and land use change across the US. Envisioned as a framework for integrating Landsat-based products and other datasets, LCMS will produce spatially, temporally, and thematically comprehensive data and information from which landscape change can be consistently assessed, documented, and analyzed at the national scale.

LCMS is a direct outgrowth of two separate efforts: (1) the Monitoring Trends in Burn Severity (MTBS) program co-lead by the US Geological Survey (USGS) and the US Forest Service (USFS); and (2) the NASA-funded North American Forest Dynamics (NAFD) project which had significant involvement of the USFS, both in terms of science development and forest inventory applications. As an interagency USGS-USFS program, LCMS supports the change detection needs of a range of federal and non-federal land managers, and specifically targets the information needs of initiatives such as LANDFIRE, a joint USGS-USFS program for mapping vegetation, fire, and fuel characteristic for the US, and the National Land Cover Database (NLCD). When successfully implemented, LCMS could become the dominant provider of national-level Landsat-based change information for all lands of the US, with agency partners and specific programs augmenting the change products to suit their needs. LCMS is currently in development, with commitments for partial funding from the USFS to conduct pilot studies.

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NWFP Effectiveness Monitoring

 

Monitoring Existing Forest Vegetation in Support of Northwest Forest Plan Effectiveness Monitoring

The Northwest Forest Plan (NWFP) was approved in 1994 to cover management of federal forestlands in Washington, Oregon and California within the range of the northern spotted owl. The basic monitoring approach is periodic assessment of late-successional and old-growth forest.

Monitoring reports are based on the best available science, technology, and data, which continue to evolve. The 15-year NWFP monitoring report, completed in 2010, was based on maps of existing vegetation for two 'bookend' dates (1994/6 and 2006/7), developed using Gradient Nearest Neighbors (GNN) imputation) and disturbances mapped annually with LandTrendr methods for Landsat change detection. The current map dates, used in the 20-year report, are for 1993 and 2012 across the entire NWFP area. Whereas all maps were developed using gradient nearest neighbor (GNN) imputation based on Landsat time-series (LandTrendr) data, the current maps incorporate new plot data and several improvements to modeling techniques. This approach offers the benefits of both methods: the detailed vegetation attributes and analytical flexibility of nearest neighbor methods, and information on stand and landscape dynamics through time from LandTrendr. Further refinement of our methods will allow us to provide nearest neighbor (NN) vegetation maps of sufficient reliability on a five-year refresh cycle. Additionally, on a five-year cycle, the TimeSync tool will be used to validate resulting time series of NN (existing vegetation) and disturbance maps to evaluate whether depiction of areas of landscape change are sufficiently reliable.

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