scholarly journals Combining satellite data for better tropical forest monitoring

2016 ◽  
Vol 6 (2) ◽  
pp. 120-122 ◽  
Author(s):  
Johannes Reiche ◽  
Richard Lucas ◽  
Anthea L. Mitchell ◽  
Jan Verbesselt ◽  
Dirk H. Hoekman ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Satellite Data ◽  
Forest Monitoring

scholarly journals The Road to Operationalization of Effective Tropical Forest Monitoring Systems

2021 ◽  
Vol 13 (7) ◽  
pp. 1370
Author(s):  
Carlos Portillo-Quintero ◽  
Jose L. Hernández-Stefanoni ◽  
Gabriela Reyes-Palomeque ◽  
Mukti R. Subedi
Keyword(s):  
Tropical Forest ◽  
Forest Cover ◽  
Functional Trait ◽  
Forest Monitoring ◽  
Monitoring Systems ◽  
User Engagement ◽  
Forest Attributes ◽  
Computing Platforms ◽  
Multiple Challenges ◽  
Categorical Maps

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

Global tropical forest area measurements derived from coarse resolution satellite imagery: a comparison with other approaches

1998 ◽  
Vol 25 (1) ◽  
pp. 37-52 ◽  
Author(s):  
PHILIPPE MAYAUX ◽  
FRÉDÉRIC ACHARD ◽  
JEAN-PAUL MALINGREAU
Keyword(s):  
Tropical Forest ◽  
Satellite Data ◽  
Information Technologies ◽  
Forest Cover ◽  
Forest Area ◽  
Base Line ◽  
High Resolution Data ◽  
Forest Policies ◽  
Forest Distribution ◽  
Definition Of

Definition of appropriate tropical forest policies must be supported by better information about forest distribution. New information technologies make possible the development of advanced systems which can accurately report on tropical forest area issues. The European Commission TREES (Tropical Ecosystem Environment observation by Satellite) project has produced a consistent map of the humid tropical forest cover based on 1 km resolution satellite data. This base-line reference information can be further calibrated using a sample of high-resolution data, in order to produce accurate forest area estimates. There is good general agreement with other pantropical inventories (Food & Agriculture Organization of the United Nations Forest Resources Assessment 90, World Conservation Union Conservation Atlas of Tropical Forests, National Aeronautics & Space Administration [USA] Landsat Pathfinder) using different approaches (compilation of existing data, statistical sampling, exhaustive survey with satellite data). However, for some countries, large differences appear among the assessments. Discrepancies arising from this comparison are here analysed in terms of limitations associated with each approach and they are generally associated with differences in forest definition, data source and processing methodology. According to the different inventories, the total area of closed tropical forest is estimated at 1090–1220 million hectares with the following continental distribution: 185–215 million hectares in Africa, 235–275 million hectares in Asia, and 670–730 million hectares in Latin America. A proposal for improving the current state of forest statistics by combining the contribution of the various methods under review is made.

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

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