Monitoring sustainable logging in an Amazon forest under management using deep learning and Planet NICFI imagery - Daniel Braga; Luiz E. O. C. Aragão; Henrique Leão; Fiona Osborn; Fabien H. Wagner; Beatriz F. Cabral; Débora J. Dutra; Breno Izidoro; Liana O. Anderson; José H. Chaves; Nara Pantoja; Sassan Saatchi; et al. (International Journal of Remote Sensing, 2026)

Applying a convolutional neural network (CNN) to Virginia’s forests: how forest type and age can impact individual tree segmentation - Alison L. Ritz; Randolph H. Wynne; Valerie A. Thomas; Fabien H. Wagner; P. Corey Green; Todd A. Schroeder; Sassan Saatchi (International Journal of Remote Sensing, 2026)

Congo Basin Carbon Cycle Responses to Global Change - Sarah Worden; Rong Fu; A. Anthony Bloom; Marijn Bauters; Hans Verbeeck; Temilola Fatoyinbo; Wannes Hubau; Lydie‐Stella Koutika; Steve Kwatcho Kengdo; Sybryn L. Maes; Vincent Medjibe; Nicholas J. Russo; et al. (Global Change Biology, 2026)
The Congo Basin and its contiguous forests harbor globally significant carbon stocks, estimated at 65 gigatons of C (GtC) above and belowground. Despite rising temperatures and intensifying droughts, they have remained a carbon sink, albeit weak: 0.26–0.50 GtC yr. −1 carbon uptake since 1980. However, these forests' carbon stocks and fluxes, including gross primary productivity, respiration, net primary productivity, and riverine carbon transport, remain poorly quantified. This limits understanding of the region's role in the global carbon cycle, its vulnerability to environmental change, and its potential as a long‐term carbon sink. We review and quantify Congo Basin and contiguous forest carbon stocks and fluxes and synthesize the current knowledge on how key global change drivers shape the region's carbon cycle. We find limited responses to long‐term precipitation variability, but declining stocks and fluxes in response to long‐term and increasing temperature and drought frequency. Land cover and land use changes, largely driven by small‐scale agriculture, logging, and agro‐industrial expansion, reduce carbon stocks, ecosystem structure and functioning, and animal‐mediated ecosystem services. In contrast, large‐scale savanna biomass burning delivers phosphorus and nitrogen to Congo Basin forests via cross‐equatorial winds, providing additional nutrients and supporting carbon sequestration. In situ studies suggest that CO 2 fertilization has increased intrinsic water‐use efficiency, although its effects may be modulated by climate change, and its impacts on biomass accumulation remain uncertain. Legacy effects from historical land use and climate change likely shaped present‐day vegetation structure, yet their relative influence is unclear. High‐resolution carbon monitoring, improved remote sensing, and strengthened in situ measurement networks are needed to quantify the impacts of these key drivers and their interactions on the Central African carbon cycle. This is needed to inform conservation strategies and advance understanding of the region's future as a carbon sink under global change pressures.

Corrigendum to “Stem taper and volume across biogeographic regions of Colombia” [For. Ecol. Manag. 595 (2025) 123046] - Miguel A. Peña; Nicolas Castaño; Jerôme Chave; Sebastián González-Caro; Sassan Saatchi; Daniel Zuleta; Alvaro Duque (Forest Ecology and Management, 2025)

Stem taper and volume across biogeographic regions of Colombia - Miguel A. Peña; Nicolas Castaño; Jerôme Chave; Sebastián González-Caro; Sassan Saatchi; Daniel Zuleta; Alvaro Duque (Forest Ecology and Management, 2025)

We Need the Largest Experiments on Earth to Achieve Our Climate Targets - Bronson W. Griscom; Susan C. Cook‐Patton; Erin Sills; Kelsey Jack; Thomas W. Crowther; Sebastien Costedoat; Christopher Field; David Hole; Lisa M. McCullough; Subhrendu K. Pattanayak; Sassan Saatchi; Will R. Turner (Global Change Biology, 2025)

Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model - Fabien H. Wagner; Sophia Roberts; Alison L. Ritz; Griffin Carter; Ricardo Dalagnol; Samuel Favrichon; Mayumi C.M. Hirye; Martin Brandt; Philippe Ciais; Sassan Saatchi (Remote Sensing of Environment, 2024)

Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height - Ibrahim Fayad; Philippe Ciais; Martin Schwartz; Jean-Pierre Wigneron; Nicolas Baghdadi; Aurélien de Truchis; Alexandre d'Aspremont; Frederic Frappart; Sassan Saatchi; Ewan Sean; Agnes Pellissier-Tanon; Hassan Bazzi (Remote Sensing of Environment, 2024)

Mapping tropical forest degradation with deep learning and Planet NICFI data - Ricardo Dalagnol; Fabien Hubert Wagner; Lênio Soares Galvão; Daniel Braga; Fiona Osborn; Le Bienfaiteur Sagang; Polyanna da Conceição Bispo; Matthew Payne; Celso Silva Junior; Samuel Favrichon; Vinicius Silgueiro; Liana O. Anderson; et al. (Remote Sensing of Environment, 2023)

Toward a forest biomass reference measurement system for remote sensing applications - Nicolas Labrière; Stuart J. Davies; Mathias I. Disney; Laura I. Duncanson; Martin Herold; Simon L. Lewis; Oliver L. Phillips; Shaun Quegan; Sassan S. Saatchi; Dmitry G. Schepaschenko; Klaus Scipal; Plinio Sist; et al. (Global Change Biology, 2023)
Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA‐ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site‐based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high‐quality sites through an in‐depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass‐related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory.

Detecting forest response to droughts with global observations of vegetation water content - Alexandra G. Konings; Sassan S. Saatchi; Christian Frankenberg; Michael Keller; Victor Leshyk; William R. L. Anderegg; Vincent Humphrey; Ashley M. Matheny; Anna Trugman; Lawren Sack; Elizabeth Agee; Mallory L. Barnes; et al. (Global Change Biology, 2021)
Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil‐plant‐atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem‐scale analog of the pressure–volume curve, the non‐linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem‐scale pressure‐volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.

Old growth Afrotropical forests critical for maintaining forest carbon - John R. Poulsen; Vincent P. Medjibe; Lee J. T. White; Zewei Miao; Ludovic Banak‐Ngok; Chris Beirne; Connie J. Clark; Aida Cuni‐Sanchez; Mathias Disney; Jean‐Louis Doucet; Michelle E. Lee; Simon L. Lewis; et al. (Global Ecology and Biogeography, 2020)
Aim Large trees [≥ 70 cm diameter at breast height (DBH)] contribute disproportionately to aboveground carbon stock (AGC) across the tropics but may be vulnerable to changing climate and human activities. Here we determine the distribution, drivers and threats to large trees and high carbon forest. Location Central Africa. Time period Current. Major taxa studied Trees. Methods Using Gabon's new National Resource Inventory of 104 field sites, AGC was calculated from 67,466 trees from 578 species and 97 genera. Power and Michaelis–Menten models assessed the contribution of large trees to AGC. Environmental and anthropogenic drivers of AGC, large trees, and stand variables were modelled using Akaike’s information criterion (AIC) weights to calculate average regression coefficients for all p ossible models. Results Mean AGC for trees ≥ 10 cm DBH in Gabonese forestlands was 141.7 Mg C/ha, with averages of 166.6, 171.3 and 96.6 Mg C/ha in old growth, concession and secondary forest. High carbon forests occurred where large trees are most abundant: 31% of AGC was stored in large trees (2.3% of all stems). Human activities largely drove variation in AGC and large trees, but climate and edaphic conditions also determined stand variables (basal area, tree height, wood density, stem density). AGC and large trees increased with distance from human settlements; AGC was 40% lower in secondary than primary and concession forests and 33% higher in protected than non‐managed areas. Main conclusions AGC and large trees were negatively associated with human activities, highlighting the importance of forest management. Redefining large trees as ≥ 50 cm DBH (4.3% more stems) would account for 20% more AGC. This study demonstrates that protecting relatively undisturbed forests can be disproportionately effective in conserving carbon and suggests that including sustainable forestry in programs like reduced emissions for deforestation and forest degradation could maintain carbon dense forests in logging concessions that are a large proportion of remaining Central African forests.

Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them - Maxime Réjou-Méchain; Nicolas Barbier; Pierre Couteron; Pierre Ploton; Grégoire Vincent; Martin Herold; Stéphane Mermoz; Sassan Saatchi; Jérôme Chave; Florian de Boissieu; Jean-Baptiste Féret; Stéphane Momo Takoudjou; et al. (Surveys in Geophysics, 2019)

Ground Data are Essential for Biomass Remote Sensing Missions - Jérôme Chave; Stuart J. Davies; Oliver L. Phillips; Simon L. Lewis; Plinio Sist; Dmitry Schepaschenko; John Armston; Tim R. Baker; David Coomes; Mathias Disney; Laura Duncanson; Bruno Hérault; et al. (Surveys in Geophysics, 2019)

Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass - Ronald E. McRoberts; Erik Næsset; Greg C. Liknes; Qi Chen; Brian F. Walters; Sassan Saatchi; Martin Herold (Surveys in Geophysics, 2019)

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland - Saeideh Maleki; Alireza Soffianian; Saeid Soltani Koupaei; Sassan Saatchi; Saeid Pourmanafi (Journal of the Indian Society of Remote Sensing, 2018)

Pan‐tropical prediction of forest structure from the largest trees - Jean‐François Bastin; Ervan Rutishauser; James R. Kellner; Sassan Saatchi; Raphael Pélissier; Bruno Hérault; Ferry Slik; Jan Bogaert; Charles De Cannière; Andrew R. Marshall; John Poulsen; Patricia Alvarez‐Loyayza; et al. (Global Ecology and Biogeography, 2018)
Aim Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan‐tropical model to predict plot‐level forest structure properties and biomass from only the largest trees. Location Pan‐tropical. Time period Early 21st century. Major taxa studied Woody plants. Methods Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the i th largest trees. Results Measuring the largest trees in tropical forests enables unbiased predictions of plot‐ and site‐level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium‐sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate‐diameter classes relative to other continents. Main conclusions Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.

Application of the metabolic scaling theory and water–energy balance equation to model large‐scale patterns of maximum forest canopy height - Sungho Choi; Christopher P. Kempes; Taejin Park; Sangram Ganguly; Weile Wang; Liang Xu; Saikat Basu; Jennifer L. Dungan; Marc Simard; Sassan S. Saatchi; Shilong Piao; Xiliang Ni; et al. (Global Ecology and Biogeography, 2016)
Aim Forest height, an important biophysical property, underlies the distribution of carbon stocks across scales. Because in situ observations are labour intensive and thus impractical for large‐scale mapping and monitoring of forest heights, most previous studies adopted statistical approaches to help alleviate measured data discontinuity in space and time. Here, we document an improved modelling approach which links metabolic scaling theory and the water–energy balance equation with actual observations in order to produce large‐scale patterns of forest heights. Methods Our model, called allometric scaling and resource limitations (ASRL), accounts for the size‐dependent metabolism of trees whose maximum growth is constrained by local resource availability. Geospatial predictors used in the model are altitude and monthly precipitation, solar radiation, temperature, vapour pressure and wind speed. Disturbance history (i.e. stand age) is also incorporated to estimate contemporary forest heights. Results This study provides a baseline map ( c . 2005; 1‐km 2 grids) of forest heights over the contiguous United States. The Pacific Northwest/California is predicted as the most favourable region for hosting large trees ( c . 100 m) because of sufficient annual precipitation (> 1400 mm), moderate solar radiation ( c . 330 W m −2 ) and temperature ( c . 14 °C). Our results at sub‐regional level are generally in good and statistically significant ( P ‐value Main conclusions We improved the metabolic scaling theory to address variations in vertical forest structure due to ecoregion and plant functional type. A clear mechanistic understanding embedded within the model allowed synergistic combinations between actual observations and multiple geopredictors in forest height mapping. This approach shows potential for prognostic applications, unlike previous statistical approaches.

Seeing the forest beyond the trees - Sassan Saatchi; Joseph Mascaro; Liang Xu; Michael Keller; Yan Yang; Paul Duffy; Fernando Espírito‐Santo; Alessandro Baccini; Jeffery Chambers; David Schimel (Global Ecology and Biogeography, 2015)
In a recent paper ( M itchard et al . 2014, G lobal E cology and B iogeography , 23 , 935–946) a new map of forest biomass based on a geostatistical model of field data for the A mazon (and surrounding forests) was presented and contrasted with two earlier maps based on remote‐sensing data Saatchi et al . (2011; RS1) and Baccini et al . (2012; RS2). M itchard et al . concluded that both the earlier remote‐sensing based maps were incorrect because they did not conform to Mitchard et al . interpretation of the field‐based results. In making their case, however, they misrepresented the fundamental nature of primary field and remote‐sensing data and committed critical errors in their assumptions about the accuracy of research plots, the interpolation methodology and the statistical analysis. By ignoring the large uncertainty associated with ground estimates of biomass and the significant under‐sampling and spatial bias of research plots, M itchard et al . reported erroneous trends and artificial patterns of biomass over A mazonia. Because of these misrepresentations and methodological flaws, we find their critique of the satellite‐derived maps to be invalid.

Observing terrestrial ecosystems and the carbon cycle from space - David Schimel; Ryan Pavlick; Joshua B. Fisher; Gregory P. Asner; Sassan Saatchi; Philip Townsend; Charles Miller; Christian Frankenberg; Kathy Hibbard; Peter Cox (Global Change Biology, 2015)
Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon–climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space‐based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.

Ten ways remote sensing can contribute to conservation - Robert A. Rose; Dirck Byler; J. Ron Eastman; Erica Fleishman; Gary Geller; Scott Goetz; Liane Guild; Healy Hamilton; Matt Hansen; Rachel Headley; Jennifer Hewson; Ned Horning; et al. (Conservation Biology, 2015)
In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Compositional shifts in C osta R ican forests due to climate‐driven species migrations - Kenneth J. Feeley; Johanna Hurtado; Sassan Saatchi; Miles R. Silman; David B. Clark (Global Change Biology, 2013)
Species are predicted to shift their distributions upslope or poleward in response to global warming. This prediction is supported by a growing number of studies documenting species migrations in temperate systems but remains poorly tested for tropical species, and especially for tropical plant species. We analyzed changes in tree species composition in a network of 10 annually censused 1‐ha plots spanning an altitudinal gradient of 70–2800 m elevation in Costa Rica. Specifically, we combined plot data with herbarium records (accessed through GBIF ) to test if the plots' community temperature scores ( CTS , average thermal mean of constituent species weighted by basal area) have increased over the past decade as is predicted by climate‐driven species migrations. In addition, we quantified the contributions of stem growth, recruitment, and mortality to the observed patterns. Supporting our a priori hypothesis of upward species migrations, we found that there have been consistent directional shifts in the composition of the plots, such that the relative abundance of lowland species, and hence CTS , increased in 90% of plots. The rate of the observed compositional shifts corresponds to a mean thermal migration rate ( TMR ) of 0.0065 °C yr −1 (95% CI = 0.0005–0.0132 °C yr −1 ). While the overall TMR is slower than predicted based on concurrent regional warming of 0.0167 °C yr −1 , migrations were on pace with warming in 4 of the 10 plots. The observed shifts in composition were driven primarily by mortality events (i.e., the disproportionate death of highland vs. lowland species), suggesting that individuals of many tropical tree species will not be able to tolerate future warming and thus their persistence in the face of climate change will depend on successful migrations. Unfortunately, in Costa Rica and elsewhere, land area inevitably decreases at higher elevations; hence, even species that are able to migrate successfully will face heightened risks of extinction.

Modeling the spatial and temporal heterogeneity of deforestation‐driven carbon emissions: the INPE ‐ EM framework applied to the Brazilian Amazon - Ana Paula Dutra Aguiar; Jean Pierre Ometto; Carlos Nobre; David Montenegro Lapola; Claudio Almeida; Ima Célia Vieira; João Vianei Soares; Regina Alvala; Sassan Saatchi; Dalton Valeriano; Juan Carlos Castilla‐Rubio (Global Change Biology, 2012)
We present a generic spatially explicit modeling framework to estimate carbon emissions from deforestation ( INPE ‐ EM ). The framework incorporates the temporal dynamics related to the deforestation process and accounts for the biophysical and socioeconomic heterogeneity of the region under study. We build an emission model for the Brazilian Amazon combining annual maps of new clearings, four maps of biomass, and a set of alternative parameters based on the recent literature. The most important results are as follows: (a) Using different biomass maps leads to large differences in estimates of emission; for the entire region of the Brazilian Amazon in the last decade, emission estimates of primary forest deforestation range from 0.21 to 0.26 Pg C yr −1 . (b) Secondary vegetation growth presents a small impact on emission balance because of the short duration of secondary vegetation. In average, the balance is only 5% smaller than the primary forest deforestation emissions. (c) Deforestation rates decreased significantly in the Brazilian Amazon in recent years , from 27 Mkm 2 in 2004 to 7 Mkm 2 in 2010. INPE ‐ EM process‐based estimates reflect this decrease even though the agricultural frontier is moving to areas of higher biomass. The decrease is slower than a non‐process instantaneous model would estimate as it considers residual emissions (slash, wood products, and secondary vegetation). The average balance, considering all biomass, decreases from 0.28 in 2004 to 0.15 Pg C yr −1 in 2009; the non‐process model estimates a decrease from 0.33 to 0.10 Pg C yr −1 . We conclude that the INPE ‐ EM is a powerful tool for representing deforestation‐driven carbon emissions. Biomass estimates are still the largest source of uncertainty in the effective use of this type of model for informing mechanisms such as REDD +. The results also indicate that efforts to reduce emissions should focus not only on controlling primary forest deforestation but also on creating incentives for the restoration of secondary forests.

A time series of urban forestry in Los Angeles - Thomas Welch Gillespie; Stephanie Pincetl; Shea Brossard; Jenny Smith; Sassan Saatchi; Diane Pataki; Jean-Daniel Saphores (Urban Ecosystems, 2012)

Radar interferometry time series analysis of Mashhad subsidence - Maryam Dehghani; Mohammad Javad Valadan Zoej; Sassan Saatchi; Juliet Biggs; Barry Parsons; Tim Wright (Journal of the Indian Society of Remote Sensing, 2009)

Predicting species distributions across the Amazonian and Andean regions using remote sensing data - Wolfgang Buermann; Sassan Saatchi; Thomas B. Smith; Brian R. Zutta; Jaime A. Chaves; Borja Milá; Catherine H. Graham (Journal of Biogeography, 2008)
Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1‐km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian–Andean elevational gradients. Within remote sensing subsets, microwave‐based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large‐scale land‐use change.

Evolutionary consequences of human disturbance in a rainforest bird species from Central Africa - THOMAS B. SMITH; BORJA MILÁ; GREGORY F. GRETHER; HANS SLABBEKOORN; IREM SEPIL; WOLFGANG BUERMANN; SASSAN SAATCHI; JOHN P. POLLINGER (Molecular Ecology, 2008)
Relatively little attention has been directed towards understanding the impacts of human disturbance on evolutionary processes that produce and maintain biodiversity. Here, we examine the influence of anthropogenic habitat changes on traits typically associated with natural and sexual selection in the little greenbul ( Andropadus virens ), an African rainforest bird species. Using satellite remote‐sensing and field survey data, we classified habitats into nonhuman‐altered mature and human‐altered secondary forest. Mature rainforest consisted of pristine rainforest, with little or no human influence, and secondary forest was characterized by plantations of coffee and cacao and high human impacts. Andropadus viren s abundance was higher in secondary forest, and populations inhabiting mature rainforest were significantly larger in wing and tarsus length and bill size; characters often correlated with fitness. To assess the extent to which characters important in sexual section and mate choice might be influenced by habitat change, we also examined differences in plumage colour and song. Plumage colour and the variance in plumage luminance were found to differ between forest types, and song duration was found to be significantly longer in mature forest. The possible adaptive significance of these differences in traits is discussed. Despite relatively high levels of gene flow across habitats, amplified fragment length polymorphism analysis revealed that a small proportion of high‐ F ST loci differentiated mature from secondary forest populations. These loci were significant outliers against neutral expectations in a simulation analysis, suggesting a role for divergent selection in differentiation across habitats. A distance‐based redundancy analysis further showed that forest type as defined by remote‐sensing variables was significantly associated with genetic dissimilarities between habitats, even when controlling for distance. The observed shifts in morphology, plumage and song were consistent with divergent selection on heritable variation, but a role for plasticity cannot be ruled out. Results suggest that anthropogenic habitat changes may have evolutionary consequences, with implications for conservation and restoration.

Predicting and quantifying the structure of tropical dry forests in South Florida and the Neotropics using spaceborne imagery - Thomas W. Gillespie; Brian R. Zutta; Michael K. Early; Sassan Saatchi (Global Ecology and Biogeography, 2006)