Valéry Gond

Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: Methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

Broad-scale spatial pattern of forest landscape types in the Guiana Shield

Abstract Detecting broad scale spatial patterns across the South American rainforest biome is still a major challenge. Although several countries do possess their own, more or less detailed land-cover map, these are based on classifications that appear largely discordant from a country to another. Up to now, continental scale remote sensing studies failed to fill this gap. They mostly result in crude representations of the rainforest biome as a single, uniform vegetation class, in contrast with open vegetations. A few studies identified broad scale spatial patterns, but only when they managed to map a particular forest characteristic such as biomass. The main objective of this study is to identify, characterize and map distinct forest landscape types within the evergreen lowland rainforest at the sub-continental scale of the Guiana Shield (north-east tropical South-America 10° North-2° South; 66° West-50° West). This study is based on the analysis of a 1-year daily data set (from January 1st to December 31st, 2000) from the VEGETATION sensor onboard the SPOT-4 satellite (1-km spatial resolution). We interpreted remotely sensed landscape classes (RSLC) from field and high resolution remote sensing data of 21 sites in French Guiana. We cross-analyzed remote sensing data, field observations and environmental data using multivariate analysis. We obtained 33 remotely sensed landscape classes (RSLC) among which five forest-RSLC representing 78% of the forested area. The latter were classified as different broad forest landscape types according to a gradient of canopy openness. Their mapping revealed a new and meaningful broad-scale spatial pattern of forest landscape types. At the scale of the Guiana Shield, we observed a spatial patterns similarity between climatic and forest landscape types. The two most open forest-RSLCs were observed mainly within the north-west to south-east dry belt. The three other forest-RSLCs were observed in wetter and less anthropized areas, particularly in the newly recognized “Guianan dense forest arch”. Better management and conservation policies, as well as improvement of biological and ecological knowledge, require accurate and stable representations of the geographical components of ecosystems. Our results represent a decisive step in this way for the Guiana Shield area and contribute to fill one of the major shortfall in the knowledge of tropical forests.

Tracking land-cover changes with sedimentary charcoal in the Afrotropics

Fires have played an important role in creating and maintaining savannas over the centuries and are also one of the main natural disturbances in forests. The functional role of fires in savannas and forests can be investigated through examining sedimentary charcoal in order to reconstruct long-term fire history. However, the relationship between charcoal and vegetation structure in tropical grassy ecosystems remains to be elucidated. Here, we compared recent charcoal records from lake sediments in three tropical ecosystems (forest, savanna, and forest–savanna mosaic) with land cover inferred from remote-sensing images. Charcoal width-to-length (W/L) ratio is a good proxy for changes in fuel type. At one of the lakes, a significant W/L modification from values >0.5 (mainly wood) to <0.5 (~grass) was recorded simultaneously with changes in land cover. Indeed, a significant deforestation was recorded around this lake in the remote-sensing imagery between 1984 and 1994. The results also indicate that a riparian forest around a lake could act as a physical filter for charcoal accumulation; we used the mean charcoal size as a proxy to evaluate this process. Charcoal Accumulation Rates (CHAR), a burned biomass proxy, were combined with W/L ratio and the mean charcoal size to investigate the land-use history of the landscapes surrounding the study sites. This combined approach allowed us to distinguish between episodic slash-and-burn practices in the forest and managed fields or pastures burning frequently.

Vegetation structure and greenness in Central Africa from Modis multi-temporal data

African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.

Canopy height estimation in French Guiana with LiDAR ICESat/GLAS data using principal component analysis and random forest regressions

Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) or on Principal Component Analysis (PCA) of GLAS waveforms. Resultsshow that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m). For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs) and the waveform extent (RMSE = 3.8 m). Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m). Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index). Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF) based on the PCA of waveform samples with waveform extent information is an interesting alternative for canopy height estimation as it does not require several metrics that are difficult to derive from GLAS waveforms in dense forests, such as those in French Guiana.

The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests

In this study, the recently launched Sentinel-2 (S2) optical satellite and the active radar Sentinel-1 (S1) satellite supported by active fire data from the MODIS sensor were used to detect and monitor forest fires in the Congo Basin. In the context of a very strong El Nino event, an unprecedented outbreak of fires was observed during the first months of 2016 in open forests formations in the north of the Republic of Congo. The anomalies of the recent fires and meteorological situation compared to historical data show the severity of the drought. Burnt areas mapped by the S1 SAR and S2 Multi Spectral Instrument (MSI) sensors highlight that the fires occurred mainly in Marantaceae forests, characterized by open tree canopy cover and an extensive tall herbaceous layer. The maps show that the origin of the fires correlates with accessibility to the forest, suggesting an anthropogenic origin. The combined use of the two independent and fundamentally different satellite systems of S2 and S1 captured an extent of 36,000 ha of burnt areas, with each sensor compensating for the weakness (cloud perturbations for S2, and sensitivity to ground moisture for S1) of the other.

Timing and patterns of the ENSO signal in Africa over the last 30 years: insights from normalized difference vegetation index data.

A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI‐Ni~ correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic RepublicofCongotoEthiopia.However,theyarenotuniforminspaceandaremoderate(;0.3). Conversely, positive correlations are recorded over the winter rainfall region of South Africa. In October‐ November, negative correlations over Ethiopia, Sudan, and Uganda strengthen while positive correlations emerge in the Horn of Africa and in the southeast coast of South Africa. By December with the settlement of the ITCZ south of the equator, positive correlations over the Horn of Africa spread southward and westward while negative correlations appear over Mozambique, Zimbabwe, and South Africa. This pattern strengthens and a dipole at 188S is well established in February‐March with reduced (enhanced) greenness during ENSO years south (north) of 188S. At the same time, at ;28N negative correlations spread northward. Last, from April to June negative correlations south of 188S spread to the north (to 108S) and to the east (to the south of Tanzania).

Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran

The importance of measuring forest biophysical properties for ecosystem health monitoring and forest management encourages researchers to find precise, yet low-cost methods especially in mountainous and large areas. In the present study, geoscience laser altimeter system (GLAS) on board Ice, Cloud, and land Elevation Satellite (ICESat) was used to estimate three biophysical characteristics of forests located in the north of Iran: 1) maximum canopy height (Hmax); 2) Loreys height (HLorey); and 3) forest volume (V). A large number of multiple linear regressions (MLR) and also random forest (RF) regressions were developed using different sets of variables including waveform metrics, principal components (PCs) produced from principal component analysis (PCA) and wavelet coefficients (WCs) generated from wavelet transformation (WT). To validate and compare models, statistical criteria were calculated based on a fivefold cross validation. Best model concerning the maximum height was an MLR (RMSE = 5.0 m) which combined two metrics extracted from waveforms (waveform extent “ Wext” and height at 50% of waveform energy “ H50”), and one from digital elevation model (terrain index, TI). The mean absolute percentage error (MAPE) of maximum height estimates was 16.4%. For Loreys height, a simple MLR (including Wext and TI) represented the highest performance (RMSE = 5.1 m, MAPE = 24.0%). Generally, MLR models had a better performance when compared to the RF models. In addition, the accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, regression models estimating volume directly from GLAS data led to a better result (RMSE = 128.8 m3/ha) rather than volume- HLorey relationship (RMSE = 167.8 m3/ha).

Predators, Prey and Habitat Structure: Can Key Conservation Areas and Early Signs of Population Collapse Be Detected in Neotropical Forests?

Tropical forests with a low human population and absence of large-scale deforestation provide unique opportunities to study successful conservation strategies, which should be based on adequate monitoring tools. This study explored the conservation status of a large predator, the jaguar, considered an indicator of the maintenance of how well ecological processes are maintained. We implemented an original integrative approach, exploring successive ecosystem status proxies, from habitats and responses to threats of predators and their prey, to canopy structure and forest biomass. Niche modeling allowed identification of more suitable habitats, significantly related to canopy height and forest biomass. Capture/recapture methods showed that jaguar density was higher in habitats identified as more suitable by the niche model. Surveys of ungulates, large rodents and birds also showed higher density where jaguars were more abundant. Although jaguar density does not allow early detection of overall vertebrate community collapse, a decrease in the abundance of large terrestrial birds was noted as good first evidence of disturbance. The most promising tool comes from easily acquired LiDAR data and radar images: a decrease in canopy roughness was closely associated with the disturbance of forests and associated decreasing vertebrate biomass. This mixed approach, focusing on an apex predator, ecological modeling and remote-sensing information, not only helps detect early population declines in large mammals, but is also useful to discuss the relevance of large predators as indicators and the efficiency of conservation measures. It can also be easily extrapolated and adapted in a timely manner, since important open-source data are increasingly available and relevant for large-scale and real-time monitoring of biodiversity.

Spectral approach to model mountain lake catchment through landscape attributes

This study investigates the usefulness of satellite images for the study of moutain lake ecosystems and develops an effective approach to extract landscape features of interest for the determination of the lake characteristics. A methodology is proposed taking advantage of the unique spectral features of the land cover classes defined for this study along to their statistical information to determine different band pairs which contain key information for every class. The land cover classes are extracted separately from the selected pair of bands and the class images obtained are later added together in a final classified image. This methodology is applied to ten european sites representing glacial lake districts. The GIS ArcInfo is used to integrate the information obtained from the satellite images with the lake catchments thus the final classified images are merged with the lake catchment boundary vectors of the study areas therefore every pixel is assigned to the corresponding lake catchment. Moreover different patterns were observed in the spatial distribution of the land cover classes. These patterns were analyzed and classified as different lithological classes and every lake catchment was assigned to the corresponding lithological class. At the end every studied pixel has two attributes: land cover and lithology which together give a more detailed perception of the terrain.