Stéphane Couturier

Tropical Dry Forests in the Global Picture: The Challenge of Remote Sensing-Based Change Detection in Tropical Dry Environments

Global environmental change has recently pushed the scientific community in the quest for more comprehensive spatial information on the continental biosphere. In terms of climate change, ecosystem monitoring has become one of the priorities to better understand the evolution of terrestrial carbon stocks, as well as to foster conservation policies for these carbon stocks. According to IPCC (2002), deforestation and land clearing activities, mostly from sub-tropical regions, contributed with one fifth of the greenhouse gas emission during the 1990s. The tropical dry forests are one of the most extended tropical forested ecosystems, and yet have received only recent attention from the scientific community. This ecosystem is also scarcely represented in the international protection schemes, which perhaps causes increased vulnerability of this ecosystem to the tropical fingerprint of global human development. Additionally, the climatic conditions are relatively attractive for human settlement and the ecosystem has historically supported dense agriculture activity. In megadiverse Mexico for example, these forests extend up to 60% of tropical forests, and an estimated 30% of this extent is considered as highly modified under anthropic pressure. The annual deforestation rate of the deciduous tropical dry forest in Mexico has been evaluated at around 1.4 2 %. The contribution of the latter to climate change is manifolds, including carbon emissions, increased albedo and regional hydrographic cycle alteration. Moreover, the very loss of biodiversity derived from the conversion of forest to grassland for pasture is considered as a triggering factor for future forest fires and conversion to more grassland. The monitoring and analysis of the forest distribution pattern, including phenological and anthropogenic modifications, contributes to the uneasy task of slowing down the tendency of forest loss. Remote sensing has proved a fundamental tool for such monitoring, owing to its contribution to the study and understanding of the global environment through time, and the calibration of models which help building environmental scenarios in the future.

An Accuracy Index with Positional and Thematic Fuzzy Bounds for Land-use/Land-cover Maps

This paper proposes a comprehensive framework for the accuracy assessment of taxonomically diverse LULC maps. A widely accepted difficulty in assessing such maps is associated with the vagueness in the interpretation of complex landscapes. For every class of the map, this method quantified the thematic and positional fuzziness of accuracy, induced by this difficulty. The labeling protocol consisted of a fuzzy comparison between the map and a reference maplet, for which degrees of positional and thematic tolerance can be user-defined. The construction of reference maplets permitted a flexible analysis (comparable with the assessment of other maps) of the positional fuzziness of the reference dataset and of the vagueness of the assessment process, while the alternate evaluation protocol, based on traditional point like data collection, did not allow such analysis.

Building a Forward-Mode Three-Dimensional Reflectance Model for Topographic Normalization of High-Resolution (1–5 m) Imagery: Validation Phase in a Forested Environment

The aim of the topographic normalization of remotely sensed imagery (TNRSI) is to reduce reflectance variability caused by steep terrain and, subsequently, to improve land-cover classification. Recently, multiple-forward-mode (FM) (MFM) reflectance models for topographic normalizations of medium-resolution (20-30 m) satellite imagery have improved the classification of forested covers with respect to more conventional topographic corrections. We propose an FM 3-D reflectance (FM3DR) model, based on the Discrete Anisotropic Radiative Transfer simulator, for the topographic normalization of high-resolution (1-5 m) imagery. The feasibility of this approach was first verified on real IKONOS imagery for three forest types within major biomes (oak, pine, and high tropical forest) in Mexico. Next, we formalized the topographic normalization performance index and variability as relevant criteria to test TNRSI across incident angles in terms of maximum likelihood classification effectiveness. The FM3DR model outperformed five previously published topographic corrections (cosine, Minnaert, sun-canopy-sensor (SCS), Civco two-stage, and slope matching corrections), and image-based statistical strategies (Civco two-stage and slope matching corrections) tended to perform better than more analytical strategies (cosine, Minnaert, and SCS corrections). An asset of this approach versus former models is the realistic account of terrain-related variation of understory and crown cover within a cover type. On top of that, once validated across forest types, the model is sufficient for the application of a full MFM 3-D reflectance-based topographic normalization without additional field measurement.

Comment on Gebhardt et al. MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data. Remote Sens. 2014, 6, 3923–3943

Gebhardt et al. (2014) presented the Monitoring Activity Data for the Mexican REDD+ program (MAD-MEX), an automatic nation-wide land cover monitoring system for the Mexican REDD+ MRV. Though MAD-MEX represents a valuable first effort toward establishing a national reference emissions level for the implementation of REDD+ in Mexico, in this paper, we argue that this land cover system has important limitations that may prevent it from becoming operational for REDD+ MRV. Specifically, we show that (1) the accuracy assessment of MAD-MEX land cover maps is optimistically biased; (2) the ability of MAD-MEX to monitor land cover change, including deforestation and forest degradation; is poor and (3) the use of an entirely automatic classification approach, such as that followed by MAD-MEX, is highly problematic in the case of a large and heterogeneous country like Mexico. We discuss these limitations and call into question the ability of a land cover monitoring system, such as MAD-MEX, both to elaborate a national reference emissions level and to monitor future forest cover change, as part of a REDD+ MRV system. We provide some insights with the aim of improving the development of nation-wide land cover monitoring systems in Mexico and elsewhere.