Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR

Red band bidirectional reflectance factor data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) acquired over the southwestern United States were interpreted through a simple geometric–optical (GO) canopy reflectance model to provide maps of fractional crown cover (dimensionless), mean canopy height (m), and aboveground woody biomass (Mg ha−1) on a 250 m grid. Model adjustment was performed after dynamic injection of a background contribution predicted via the kernel weights of a bidirectional reflectance distribution function (BRDF) model. Accuracy was assessed with respect to similar maps obtained with data from the NASA Multiangle Imaging Spectroradiometer (MISR) and to contemporaneous US Forest Service (USFS) maps based partly on Forest Inventory and Analysis (FIA) data. MODIS and MISR retrievals of forest fractional cover and mean height both showed compatibility with the USFS maps, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively, compared with MISR MAE of 0.10 and 2.2 m, respectively. The respective MAE for aboveground woody biomass was ~10 Mg ha−1, the same as that from MISR, although the MODIS retrievals showed a much weaker correlation, noting that these statistics do not represent evaluation with respect to ground survey data. Good height retrieval accuracies with respect to averages from high resolution discrete return lidar data and matches between mean crown aspect ratio and mean crown radius maps and known vegetation type distributions both support the contention that the GO model results are not spurious when adjusted against MISR bidirectional reflectance factor data. These results highlight an alternative to empirical methods for the exploitation of moderate resolution remote sensing data in the mapping of woody plant canopies and assessment of woody biomass loss and recovery from disturbance in the southwestern United States and in parts of the world where similar environmental conditions prevail

Changes in Tall Shrub Abundance on the North Slope of Alaska, 2000-2010

The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades has likely had profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding the magnitude, direction, and implications of this vegetation shift calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. Due to the extent of the North Slope of Alaska and its extreme environments, remote sensing may be the most suitable tool to produce wall-to-wall fractional shrub cover maps for the entire region, however, most regional maps have relied on vegetation indices or needed many years worth of data to cover the whole region. Here, a new mapping approach is presented that uses satellite imagery from the Multi-angle Imaging SpectroRadiometer (MISR) sensor and some landscape variables to predict tall shrub (\u3e 0.5 m) cover with the ultimate goal of evaluating temporal changes in tall shrub fractional cover during the period of 2010-2000. Specifically, we: 1) undertook two field surveys in the North Slope of Alaska to obtain estimates of tall shrub cover, canopy height, crown radius, and total number of shrubs at 26 sites (250 m × 250 m each); 2) evaluated the ability of the semi-automated image interpretation algorithm CANAPI - CANopy Analysis from Panchromatic Imagery, to derive structural data for tall (\u3e 0.5 m) shrubs in the Arctic; 3) constructed a robust reference database with estimates of shrub structural parameters; 4) trained and validated the boosted regression tree model to predict tall shrub fractional cover from moderate resolution imagery; 5) created the 2000 and the 2010 tall shrub fractional cover map for the North Slope of Alaska; and 6) evaluated the changes in shrub abundance during the period 2010-2000 in the North Slope of Alaska. Results from the field surveys suggested that tall shrub fractional cover was less than 5% at 250 m scales. The evaluation of the CANAPI algorithm showed that CANAPI could successfully retrieve fractional cover (R2 = 0.83, P \u3c 0.001), mean crown radius (R2 = 0.81, P \u3c 0.001), and total number of shrubs (R2 = 0.54, P \u3c 0.001) from very-high resolution imagery. As a result, a robust reference database was constructed with estimates of tall shrub fractional cover, canopy radius, and total number of shrubs for 1,039 sites across the domain of the North Slope. After the training and validation of the Boosted Regression Tree (BRT), the best model used 14 predictor variables and explained 52% of the variation in the response variable, fractional cover. The red reflectance, slope, nadir Bidirectional Reflectance Distribution Function (BRDF) adjusted weight of determination, and isotropic scattering kernel were the variables more often used to generate the regression trees, and therefore they contributed the most to the model. The trained BRT model was used to construct the tall shrub fractional cover map for the year 2000 and 2010 using moderate resolution imagery. The maps revealed that cover ranged from 0.00 to 0.21 and about 75% of the sites had a fractional cover less than 0.013. High cover values were predicted along floodplains, creeks, and sloped terrain. The 2000 MISR-derived fractional cover map presented here outperformed the 2000 Landsat-derived tall shrub fractional cover map when compared to the robust validation data set (R2= 0.38, Root Mean Square Error (RMSE) = 0.08). Temporal comparisons of tall shrub abundance in the MISR-derived maps suggested that shrubs expanded during the period 2000-2010. The extent of the area that unequivocally experienced a robust change in tall shrub cover was less than 1 % (1,487 km2) of the total area of the North Slope of Alaska (213,090 km2). It is possible that tall shrubs may have expanded throughout a larger area but there is insufficient precision in the MISR-based estimates to make an unequivocal determination. Nevertheless, it seems that there was a positive trend toward an increase in shrub cover considering that 95% of the locations that had a robust change saw an increase. The tall shrub cover expansion rate varied between 0.006 yr-1 and 0.017 yr-1, being higher along the forest-tundra ecotone, north of the Brooks Range. More research is necessary to determine if the increase in cover corresponded to the advance of the tree line, or to the expansion of the tall shrubs, or both

Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm

Remote sensing based quantification of semiarid rangeland vegetation provides the large scale observations required for monitoring native plant distribution, estimating fuel loads, modeling climate and hydrological dynamics, and measuring carbon storage. Fine scale 3-dimensional vertical structural information from airborne lidar and improved signal to noise ratio and radiometric resolution of recent satellite imagery provide opportunities for refined measurements of vegetation structure. In this study, we leverage a large number of time series Landsat 8 vegetation indices and lidar point cloud - based vegetation metrics with ground validation for scaling aboveground shrub and herb biomass and cover from small scale plot to large, regional scales in the Morley Nelson Snake River Birds of Prey National Conservation Area (NCA), Idaho. The Landsat vegetation indices were trained and linked to in-situ measurements (n = 141) with the random forest regression to impute vegetation biomass and cover across the NCA. We also validated our model with an independent dataset (n = 44), explaining up to 63% and 53% of variation in shrub cover and biomass, respectively. Forty six of the in-situ plots were used in a model to compare the performance of lidar and Landsat data in estimating vegetation characteristics. Our results demonstrate that Landsat performs better in estimating both herb (R2 ~ 0.60) and shrub cover (R2 ~ 0.75) whereas lidar performs better in estimating shrub and total biomass (R2 ~ 0.75 and 0.68, respectively). Using the lidar only model, we demonstrate that lidar metrics based on shrub height have a strong correlation with field-measured shrub biomass (R2 ~ 0.76). We also compare processing the lidar data with raster-based and point cloud-based approaches. The results are scale-dependent, with improved results of biomass estimation at coarser scales with point cloud processing. Overall, the results of this study indicate that Landsat and lidar can be efficiently utilized independently and together to estimate biomass and cover of vegetation in this semi-arid rangeland environment

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics

Carbon Sequestration and Agents of Woody Encroachment in Southeastern Arizona Semi-arid Grasslands

Woody encroachment and proliferation within dryland ecosystems is potentially the second largest portion of the North American carbon sink and one of the largest areas of uncertainty. This dissertation examines a semi-arid grassland located in southeastern Arizona to better understand woody encroachment, agents of change, and the resultant carbon storage from 1984-2008. The objectives were to quantify changes in woody cover, rank agent importance, estimate carbon density, and calculate voluntary market value. The first objective of mapping changes in woody cover was addressed using a Landsat time-series to measure woody cover and calculate the change, rate of change, and change relative to initial cover over the 25-year time period. Results show the change in woody cover varies spatially and ranges from approximately -2 to 11% with most areas experiencing a 5% increase and 92% relative increase over initial cover, indicating woody cover nearly doubled in the region. The second objective of ranking the importance of agents was achieved using an ensemble classifier. Agents examined included grazing, number of times burned, soil texture, soil productivity, elevation, slope, aspect, and initial woody cover. Initial woody cover, number of times burned, elevation, and grazing were ranked as the most important agents of woody encroachment, indicating the importance of historical land management and disturbance, frequent fire, topography and correlated precipitation, and land use. The third objective of producing carbon estimates and calculating economic opportunity in the voluntary carbon markets was accomplished by applying cover to biomass, root:shoot, and carbon equations to the final woody plant cover maps to calculate carbon stocks, carbon density, and voluntary market value. Results show very low carbon density in the study area relative to similar ecosystems and other ecosystems in general. Given the insignificant annual accumulation of carbon on the small ownership parcels, current low carbon trading prices, and high beef prices, management for storage is not economically viable in the study area at this time

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion