Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes

Abstract

The presence and distribution of green vegetation cover in the biosphere are of paramount importance in investigating cause-effect phenomena at the land/atmosphere interface, estimating primary production rates as part of global carbon and water cycle assessments and evaluating soil protection and land use change over time. The fraction of green vegetation cover (FCover) as estimated from satellite observations has already been demonstrated to be an extraordinarily useful product for understanding vegetation cover changes, for supporting ecosystem service assessments over areas with variable extents and for processes spanning a variable period of time (abrupt events or long-term processes). This study describes a methodology implemented to estimate global FCover (from 2001 to 2015) by applying a linear spectral mixture analysis with global endmembers to an entire temporal series of MODIS satellite observations and gap-filling missing FCover observations in temporal series using the DINEOF algorithm. The resulting global MODV1 FCover product was validated with two global validation datasets and showed an overall good thematic absolute accuracy (RMSE = 0.146) consistent with the validation performance of other FCover global products. Basic statistics performed on the product show changes in average and trend values and allow for the quantification of gross vegetation loss and gain over different temporal scales. To demonstrate the capacity of this global product to monitor specific dynamics, a multitemporal analysis was performed on selected sites and vegetation responses (i.e., cover changes), and specific dynamics resulting from cause-effect phenomena are briefly discussed. The product is intended to be used for monitoring vegetation dynamics, but it also has the potential to be integrated in other modeling frameworks (e.g., the carbon cycle, primary production, and soil erosion) in conjunction with other spatial datasets such as those on climate and soil type.

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

“…However, when considering all vegetation types, NDVI is preferred, although the overall correlation would decrease if all vegetation types were considered. However, depending on different biomes this dynamic may also show trend changes [17]. Therefore, the relationship found here may be suitable mainly for IFP sites within Indonesia for the Acacia species.…”

“…Various researchers and institutions have performed related studies of quantitative analyzations of both local and global vegetation coverage. The products of MODIS Vegetation Continuous Fields or the fCover (fraction of vegetation cover, hereafter denoted as FVC) [16] were used, while other researchers utilized the MODIS reflectance data provided by Land Processes Distributed Active Archive Center (LP DAAC) for developing improved FVC data [17]. The remote sensing techniques for FVC development utilize the multispectral information observed from space and validates its product with ground truth information (e.g., field surveys).…”

“…One of the conventional issues observed is the cloud cover, which blocks otherwise available information for analyzing a terrestrial environment [22]. The missing information can be aided with a gap-filling technique [17] for including continuous land information. However, this technique can result in large differences in the spectral information of the area, and then the possibility of an incorrect FVC estimation rises.…”

Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products

“…Such data, especially when freely available, up-to-date and in near-real-time, shows great potential, considering the constant changes undergone by Earth's ecosystems due to natural land and atmospheric conditions and under the pressure of human activities [31][32][33]. Its applications include the parametrization of a wide variety of ecosystem models, the reproduction of ecosystem dynamics, and the estimation of natural risks to ecosystems or future scenarios, which can improve the formulation of environmental and conservation policies [34][35][36].…”

“…In order to monitor and manage ecosystem changes, scientists have developed, in the framework of remote sensing applications, highly useful indices and approaches for evaluating both qualitatively and quantitatively the vegetative-water-energy nexus of natural surfaces through the execution of spectral measurements [33,34,[36][37][38][39]. The worldwide known Copernicus Global Land Service (CGLS) [40], a component of the Land Monitoring Core Service of the Copernicus Earth Observation program, provides in particular a freely-available set of products based on qualified bio-geophysical indices.…”