The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed withi...The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.展开更多
Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories es...Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.展开更多
The feasibility of ERS SAR Tandem data for mapping forest and non-forest cover in China was evaluated over Zengcheng County in the South China. An accuracy of 75% has been achieved. Then, the MACFERST (Mapping China F...The feasibility of ERS SAR Tandem data for mapping forest and non-forest cover in China was evaluated over Zengcheng County in the South China. An accuracy of 75% has been achieved. Then, the MACFERST (Mapping China Forest with ERS SAR Tandem data) project started by the Ministry of Science and Technology (MOST) of China and the European Space Agency (ESA) in 1999. The generation of a large-scale forest map requires solving problems such as the georeferencing and mosaicking of very long image strips cov...展开更多
The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS se...The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.展开更多
To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk （FFR） maps. To pro...To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk （FFR） maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model （susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map） and was produced according to Portuguese Forest Authority＇s （AFN, Autoridade Florestal Nacional） rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category ＂very high＂ or ＂high＂ fire risk. The ＂very high＂ fire risk area is mainly classified as broad-leaved forest and has the steepest slopes （〉15 %）. The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the ＂high＂ and ＂very high＂ risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro- prietary software. Although the results were obviously the same, the model developed here presents several advan- tages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs （i.e., to the rules of different countries）, and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena （e.g., floods, earthquakes, landslides）. The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.展开更多
In 1965, the first forest map of Lebanon was produced. It is the oldest spatial distribution representation of junipers. Landcover maps of 2002 and 2010 are the most detailed spatial distribution that spatially shows ...In 1965, the first forest map of Lebanon was produced. It is the oldest spatial distribution representation of junipers. Landcover maps of 2002 and 2010 are the most detailed spatial distribution that spatially shows forests. Juniper forests are found in Lebanon as mainly as clear to low density coverage. High-density juniper forests are rarely found and only on Mount-Lebanon. Juniper forests are also mixed with oaks on the Eastern flank of Mount-Lebanon. Mapping juniper forests have demonstrated high degree of complexity, especially because of their low density and being mixed. The spatial representation of juniper forests was compared between the 1965 forest map and the landcover maps of 2002 and 2010. GIS environment was used to extract juniper forests from all maps. The degree of matching between juniper forests was investigated regarding the total area and spatial overlapping. Juniper forests were examined to their spatial locations, comparing the three maps. Spatial changes and anthropogenic effect were obtained, using Google Earth facilities. Google earth had satellite images acquired since 2014. Landcover maps of 2002 and 2010 have spatially matched forest map of 1965 by about 90% and 50% respectively. Spatial coverage of juniper forests were about 12,000, 26,000 and 28,000 ha on the 1965 forest map, landcover maps of 2003 and 2010 respectively. Anti-Lebanon juniper forests were not well represented on both landcover maps. Anthropogenic activities were mainly agriculture that affected juniper forests. Cultivations have replaced about 2% of the spatial coverage of 1965 Juniper forests. Quarries and urban existed inside juniper forests but in very limited areas. Juniper forests delineation did not completely match neither between the available maps, nor to the ground. Some juniper forests were not spatially represented on all maps or existing maps represented only portion of juniper forests. Juniper forest mapping requires more consideration and field investigation. High spatial resolution satellite images are among the solutions but delimiting juniper would require extensive fieldwork and specific remote sensing treatments. Being centuries old forests and characterized by High Mountain elevations, these important conifer forests are needed to be mapped with higher accuracy for better statistics and conservation.展开更多
As part of operational guidance of mangrove forest rehabilitation in the Mahakam delta, Indonesia, site suitability mapping for 14 species of mangrove was modelled by combining 4 underlying factors—clay, sand, salini...As part of operational guidance of mangrove forest rehabilitation in the Mahakam delta, Indonesia, site suitability mapping for 14 species of mangrove was modelled by combining 4 underlying factors—clay, sand, salinity and tidal inundation. Semivariogram analysis and a geographic information system (GIS) were used to apply a site-suitability model, while kriging interpolation generated surface layers, based on sample point data collection. The tidal inundation map was derived from a tide table and a digital elevation model from topographic maps. The final site-suitability maps were produced using spatial analysis technique, by overlaying all surface layers. We used a Gaussian model to adjust a semivariogram graph in order to help to understand the variation of sample data values, and create a natural surface layer of data distribution over the area of study. By examining the statistical value and the visual inspection of surface layers, we saw that the models were consistent with the expected data behavior;therefore, we assumed that interpolation has been carried out appropriately. Our site-suitability map showed that Avicennia species was the most suitable species and matched with 50% of the study area, followed by Nypa fruticans, which occupied about 42%. These results were actually consistent with the mangrove zoning pattern in the region prior to deforestation and conversion.展开更多
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-b...Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging(LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m×18 m map units was found to range between 9 and 447 Mg·ha^-1. The corresponding root mean square errors ranged between 10 and 162 Mg·ha^-1. For the entire study region, the mean aboveground biomass was 55 Mg·ha^-1 and the corresponding relative root mean square error 8%. At this level 75%of the mean square error was due to the uncertainty associated with tree-level models.Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.展开更多
基金Knowledge Innovation Project of CAS,No. KZCX02-308
文摘The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.
文摘Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.
文摘The feasibility of ERS SAR Tandem data for mapping forest and non-forest cover in China was evaluated over Zengcheng County in the South China. An accuracy of 75% has been achieved. Then, the MACFERST (Mapping China Forest with ERS SAR Tandem data) project started by the Ministry of Science and Technology (MOST) of China and the European Space Agency (ESA) in 1999. The generation of a large-scale forest map requires solving problems such as the georeferencing and mosaicking of very long image strips cov...
基金the Faculty of Science and Technology of Beni Mellal for their logistical and financial support for the PhD project No. RNES44/13
文摘The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.
文摘To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk （FFR） maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model （susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map） and was produced according to Portuguese Forest Authority＇s （AFN, Autoridade Florestal Nacional） rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category ＂very high＂ or ＂high＂ fire risk. The ＂very high＂ fire risk area is mainly classified as broad-leaved forest and has the steepest slopes （〉15 %）. The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the ＂high＂ and ＂very high＂ risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro- prietary software. Although the results were obviously the same, the model developed here presents several advan- tages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs （i.e., to the rules of different countries）, and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena （e.g., floods, earthquakes, landslides）. The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.
文摘In 1965, the first forest map of Lebanon was produced. It is the oldest spatial distribution representation of junipers. Landcover maps of 2002 and 2010 are the most detailed spatial distribution that spatially shows forests. Juniper forests are found in Lebanon as mainly as clear to low density coverage. High-density juniper forests are rarely found and only on Mount-Lebanon. Juniper forests are also mixed with oaks on the Eastern flank of Mount-Lebanon. Mapping juniper forests have demonstrated high degree of complexity, especially because of their low density and being mixed. The spatial representation of juniper forests was compared between the 1965 forest map and the landcover maps of 2002 and 2010. GIS environment was used to extract juniper forests from all maps. The degree of matching between juniper forests was investigated regarding the total area and spatial overlapping. Juniper forests were examined to their spatial locations, comparing the three maps. Spatial changes and anthropogenic effect were obtained, using Google Earth facilities. Google earth had satellite images acquired since 2014. Landcover maps of 2002 and 2010 have spatially matched forest map of 1965 by about 90% and 50% respectively. Spatial coverage of juniper forests were about 12,000, 26,000 and 28,000 ha on the 1965 forest map, landcover maps of 2003 and 2010 respectively. Anti-Lebanon juniper forests were not well represented on both landcover maps. Anthropogenic activities were mainly agriculture that affected juniper forests. Cultivations have replaced about 2% of the spatial coverage of 1965 Juniper forests. Quarries and urban existed inside juniper forests but in very limited areas. Juniper forests delineation did not completely match neither between the available maps, nor to the ground. Some juniper forests were not spatially represented on all maps or existing maps represented only portion of juniper forests. Juniper forest mapping requires more consideration and field investigation. High spatial resolution satellite images are among the solutions but delimiting juniper would require extensive fieldwork and specific remote sensing treatments. Being centuries old forests and characterized by High Mountain elevations, these important conifer forests are needed to be mapped with higher accuracy for better statistics and conservation.
文摘As part of operational guidance of mangrove forest rehabilitation in the Mahakam delta, Indonesia, site suitability mapping for 14 species of mangrove was modelled by combining 4 underlying factors—clay, sand, salinity and tidal inundation. Semivariogram analysis and a geographic information system (GIS) were used to apply a site-suitability model, while kriging interpolation generated surface layers, based on sample point data collection. The tidal inundation map was derived from a tide table and a digital elevation model from topographic maps. The final site-suitability maps were produced using spatial analysis technique, by overlaying all surface layers. We used a Gaussian model to adjust a semivariogram graph in order to help to understand the variation of sample data values, and create a natural surface layer of data distribution over the area of study. By examining the statistical value and the visual inspection of surface layers, we saw that the models were consistent with the expected data behavior;therefore, we assumed that interpolation has been carried out appropriately. Our site-suitability map showed that Avicennia species was the most suitable species and matched with 50% of the study area, followed by Nypa fruticans, which occupied about 42%. These results were actually consistent with the mangrove zoning pattern in the region prior to deforestation and conversion.
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
基金Funding was provided by the Swedish NFI Development Foundationthe Swedish Kempe Foundation (SMK-1847)。
文摘Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging(LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m×18 m map units was found to range between 9 and 447 Mg·ha^-1. The corresponding root mean square errors ranged between 10 and 162 Mg·ha^-1. For the entire study region, the mean aboveground biomass was 55 Mg·ha^-1 and the corresponding relative root mean square error 8%. At this level 75%of the mean square error was due to the uncertainty associated with tree-level models.Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.
