The spatial distribution of disturbances in Andean tropical forests and protected areas has commonly been calculated using bi or tri-temporal analysis because of persistent cloud cover and complex topography. Long-term trends of vegetative decline (browning) or improvement (greening) have thus not been evaluated despite their importance for assessing conservation strategy implementation in regions where field-based monitoring by environmental authorities is limited. Using Colombia’s Cordillera de los Picachos National Natural Park as a case study, we provide a temporally rigorous assessment of regional vegetation change from 2001–2015 with two remote sensing-based approaches using the Breaks For Additive Season and Trend (BFAST) algorithm. First, we measured long-term vegetation trends using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-Angle Implementation of Atmospheric Correction (MAIAC) time series, and, second, we mapped short-term disturbances using all available Landsat images. MAIAC-derived trends indicate a net greening in 6% of the park, but in the surrounding 10 km area outside of the park, a net browning trend prevails at 2.5%. We also identified a 12,500 ha area within Picachos (4% of the park’s total area) that has shown at least 13 years of consecutive browning, a result that was corroborated with our Landsat-based approach that recorded a 12,642 ha (±1440 ha) area of disturbed forest within the park. Landsat vegetation disturbance results had user’s and producer’s accuracies of 0.95 ± 0.02 and 0.83 ± 0.18, respectively, and 75% of Landsat-detected dates of disturbance events were accurate within ±6 months. This study provides new insights into the contribution of short-term disturbance to long-term trends of vegetation change, and offers an unprecedented perspective on the distribution of small-scale disturbances over a 15-year period in one of the most inaccessible national parks in the Andes. Full article

Although quantifying the massive exchange of carbon that takes place over the Amazon Basin remains a challenge, progress is being made as the remote sensing community moves from using traditional, reflectance-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to the more functional Photochemical Reflectance Index (PRI). This new index, together with satellite-derived estimates of canopy light interception and Sun-Induced Fluorescence (SIF), provide improved estimates of Gross Primary Production (GPP). This paper traces the development of these new approaches, compares the results of their analyses from multiple years of data acquired across the Amazon Basin and suggests further improvements in instrument design, data acquisition and processing. We demonstrated that our estimates of PRI are in generally good agreement with eddy-flux tower measurements of photosynthetic light use efficiency (ε) at four sites in the Amazon Basin: r² values ranged from 0.37 to 0.51 for northern flux sites and to 0.78 for southern flux sites. This is a significant advance over previous approaches seeking to establish a link between global-scale photosynthetic activity and remotely-sensed data. When combined with measurements of Sun-Induced Fluorescence (SIF), PRI provides realistic estimates of seasonal variation in photosynthesis over the Amazon that relate well to the wet and dry seasons. We anticipate that our findings will steer the development of improved approaches to estimate photosynthetic activity over the tropics. Full article

Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. However, many different instrumentation designs exist and few cross-comparisons have been undertaken. This paper discusses the design evolution of the Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance (AMSPEC) series of instruments. Specifically, we assess the performance of the PP-Systems Unispec-DC and Ocean Optics JAZ-COMBO spectro-radiometers installed on an updated, tower-based AMSPEC-III system. We demonstrate the interoperability of these spectro-radiometers, and the results obtained suggest that JAZ-COMBO can successfully be used to substitute more expensive measurement units for detecting and investigating photosynthesis and canopy spectra. We demonstrate close correlations between JAZ-COMBO and Unispec-DC measured canopy radiance (0.75 ≤ R² ≤ 0.85) and solar irradiance (0.95 ≤ R² ≤ 0.96) over a three month time span. We also demonstrate close agreement between the bi-directional distribution functions obtained from each instrument. We conclude that cost effective alternatives may allow a network of AMSPEC-III systems to simultaneously monitor various vegetation types in different ecosystems. This will allow to scale and improve our understanding of the interactions between vegetation physiology and spectral characteristics, calibrate broad-scale observations to stand-level measurements, and ultimately lead to improved understanding of changing vegetation spectral features from satellite. Full article

A process-based forest growth model, 3-PG (Physiological Principles Predicting Growth), parameterized with values of soil properties constrained by satellite-derived estimates of maximum leaf area index (LAI_max), was run for Douglas-fir (Pseudotsuga menziesii) to contrast the extent to which site growth potential might vary across western North America between a cool, wet period (1950–1975) and a more recent, generally warmer and drier one (2000–2009). LAI_max represents a surrogate for overall site growth potential, as demonstrated from a strong correlation between the two variables, with the latter based on the culmination of mean annual increment estimates made at 3356 ground-based U.S. Forest Service survey plots across the states of Oregon and Washington. Results indicate that since 2000, predicted LAI_max has decreased more than 20% in portions of the Southwest USA and for much of the forested area in western Alberta. Similar percentage increases in LAI_max were predicted for parts of British Columbia, Idaho and Montana. The modeling analysis included an assessment of changes in seasonal constraints on gross primary production (GPP). A general reduction in limitations caused by spring frost occurred across the entire study area. This has led to a longer growing season, along with notable increases in summer evaporative demand and soil drought for much of the study area away from the maritime influence of the Pacific Ocean. Full article

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent. Full article