Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Gwenzi, David; Helmer, Eileen H.; Zhu, Xiaolin; Lefsky, M. A.; Marcano‐Vega, Humfredo

doi:10.3390/rs9020123

Cited by 19 publications

(11 citation statements)

References 49 publications

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“…Missing values in any composite were excluded from the harmonic regression. Bands and phenology metrics from multiseason imagery improve satellite-image-based maps of tropical forest types and structure [29][30][31][32]. Here, eliminating missing-data pixels while estimating the phenology metrics with the Fourier transform simultaneously removes noise from the 16-day composites while characterizing vegetation phenology.…”

Section: Remotely Sensed Imagerymentioning

confidence: 99%

“…These data were processed according to methods described previously [38]. All of these data types can improve discrimination of tropical forest biomass, structure, successional stage, physiognomic (i.e., formation) types, or tree species communities [30,31,36,[40][41][42][43]. Pixel-level percent tree canopy and impervious surface cover (PR only) 30 [48] Land cover in 1978 (mainland PR only) 28.5 [37] 1 TS = Developed for this study (e.g., satellite image composites, Fourier transforms of monthly MODIS and climate, Mona Island land cover, digitized VI geology).…”

Section: Other Predictor Datamentioning

confidence: 99%

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Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

et al. 2018

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We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.

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Section: Remotely Sensed Imagerymentioning

confidence: 99%

Section: Other Predictor Datamentioning

confidence: 99%

Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

et al. 2018

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“…On the contrary, some issues related to the climate change/carbon cycle linkage emerged in the 2009-2018 decade, for example, ecosystem respiration, net ecosystem exchange (NEE), and carbon uptake. These represent new research issues that scientists started to face at the beginning of the 1990s, and that in the last decade became the most relevant topic in rs+pheno studies, i.e., the most significant, with specific objectives and questions, mainly dealing with issues like forest ecophysiology [78,79] and biomass estimation and anomalies [73,80]. In this global energy balance context, special attention should be given to themes like ice cover dynamics and phytoplankton blooming.…”

Section: Emerging Research Topicsmentioning

confidence: 99%

“…On the contrary, in 2009-2018, the different topics identified involved different study regions, according to their specific characteristics and requirements. For instance, the pheno-climatic cluster included regions like Cerrado, Tropic, Savanna, North America, and Tibetan Plateau, which are broad, highly natural, and homogeneous areas where climate change effects can be clearly identified and monitored [80,[109][110][111][112][113]. In the agricultural cluster, Thailandia, Northern China, and Germany were the most occurrent study regions, maybe due to the high food demand/production, water management, and the farming practices typical of these regions [114][115][116].…”

Section: Regions Of Studymentioning

confidence: 99%

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

et al. 2019

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As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

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“…Indices, such as the shadow index, have also been used for forest applications [81,82]. Improvements in assessing AGB have been achieved using temporal series to monitor phenological changes [83], however, in our study, we were limited to single data imagery.…”

Section: Extraction Of Remote Sensing Parameters For Developing Modelmentioning

confidence: 99%

The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania

Gascon

Ceccherini

Garcı́a-Haro

et al. 2019

Forests

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In this paper, we review the potential of high resolution optical satellite data to reduce the significant investment in resources required for a national field survey for producing estimates of above ground biomass (AGB). We use 5 m resolution RapidEye optical data to support a country wide biomass inventory with the objective of bringing to the attention of the traditional forestry sector the advantages of integrating remote sensing data in the planning and execution of field data acquisition. We analysed the relationship between AGB estimates from a subset of the national survey field plot data collected by the Tanzania Forest Service, with a set of remote sensing biophysical parameters extracted from a sample of fine spatial (5 m) resolution RapidEye images using a regression estimator. We processed RapidEye data using image segmentation for 76 sample sites each of 20 km by 20 km (covering 2.3% of the land area of the country) to image objects of 1 ha. We extracted reflectance and texture information from those objects which overlapped with the field plot data and tested correlations between the two using four different models: Two models from inferential statistics and two models from machine learning. The best results were found using the random forests algorithm (R2 = 0.69). The most important explicative factor extracted from the remote sensing data was the shadow index, measuring the absorption of light in the visible bands. The model was then applied to all image objects on the RapidEye images to obtain AGB for each of the 76 sample sites, which were then interpolated to estimate the AGB stock at the national scale. Using the relative efficiency measure, we assessed the improvement that the introduction of remote sensing data brings to obtain an AGB estimate at the national level, with the same precision as the full survey. The improvement in the precision of the estimate (by reducing its variance) resulted in a relative efficiency of 3.2. This demonstrates that the introduction of remote sensing data at this fine resolution can substantially reduce the number of field plots required, in this case threefold.

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Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Cited by 19 publications

References 49 publications

Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania

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