In Cameroon in general and in the Highlands of Cameroon in particular, there is no fracture map since its realization is not easy. The region’s harsh accessibility and climatic conditions make it difficult to carry o...In Cameroon in general and in the Highlands of Cameroon in particular, there is no fracture map since its realization is not easy. The region’s harsh accessibility and climatic conditions make it difficult to carry out geological prospecting field missions that require large investments. This study proposes a semi-automatic lineament mapping approach to facilitate the elaboration of the fracture map in the West Cameroon Highlands. It uses neural networks in tandem with PCI Geomatica’s LINE algorithm to extract lineaments semi-automatically from an ALOS PALSAR 2 radar image. The cellular neural network algorithm of Lepage et al (2000) is implemented to enhance the pre-processed radar image. Then, the LINE module of Geomatica is applied </span><span style="font-family:Verdana;">to</span><span style="font-family:Verdana;"> the enhanced image for the automatic extraction of lineaments. Finally, a control and a validation of the expert by spatial analysis allows elaborat</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;"> the fracture map. The results obtained show that neural networks enhance and facilitate the identification of lineaments on the image. The resulting map contains more than 1800 fractures with major directions N20<span style="white-space:nowrap;">°</span> - 30<span style="white-space:nowrap;">°</span>, NS, N10<span style="white-space:nowrap;">°</span> - 20<span style="white-space:nowrap;">°</span>, N50<span style="white-space:nowrap;">°</span> - 60<span style="white-space:nowrap;">°</span>, N70<span style="white-space:nowrap;">°</span> - 80<span style="white-space:nowrap;">°</span>, N80<span style="white-space:nowrap;">°</span> - 90<span style="white-space:nowrap;">°</span>, N100<span style="white-space:nowrap;">°</span> - 110<span style="white-space:nowrap;">°</span>, N110<span style="white-space:nowrap;">°</span> - 120<span style="white-space:nowrap;">°</span> and N130<span style="white-space:nowrap;">°</span> - 140<span style="white-space:nowrap;">°</span> and N140<span style="white-space:nowrap;">°</span> - 150<span style="white-space:nowrap;">°</span>. It can be very useful for geological and hydrogeological studies, and especially to inform on the productivity of aquifers in this region of high agro-pastoral and mining interest for Cameroon and the Central African sub-region.展开更多
Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of target...Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.展开更多
Agricultural crop abandonment negatively impacts local economy and environment since land,as a resource for agriculture,is not optimally utilized.To take necessary actions to rehabilitate abandoned agricultural lands,...Agricultural crop abandonment negatively impacts local economy and environment since land,as a resource for agriculture,is not optimally utilized.To take necessary actions to rehabilitate abandoned agricultural lands,the identification of the spatial distribution of these lands must be acknowledged.While optical images had previously illustrated potentials in the identification of agricultural land abandonment,tropical areas often suffer cloud coverage problem that limits the availability of the imageries.Therefore,this study was conducted to investigate the potential of ALOS-1 and 2(Advanced Land Observing Satellite-1 and 2)PALSAR(Phased Array L-band Synthetic Aperture Radar)images for the identification and classification of abandoned agricultural crop areas,namely paddy,rubber and oil palm fields.Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR;nonetheless,oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes.The accuracy obtained for these abandoned lands of paddy,rubber and oil palm was 93.33%±0.06%,78%±2.32%and 63.33%±1.88%,respectively.This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops.The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.展开更多
In this study, we have performed an analysis between the L-band backscattering intensity derived from the slope corrected ALOS PALSAR remote sensing data and the?in-situ?stand biophysical parameter of Sugi (Cryptomeri...In this study, we have performed an analysis between the L-band backscattering intensity derived from the slope corrected ALOS PALSAR remote sensing data and the?in-situ?stand biophysical parameter of Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) trees at the forests of Chiba Prefecture, Japan. Diameter at breast height (DBH), tree height, and stem volume were statistically compared with the slope corrected sigma naught backscattering in an empirical approach. It was found that the relationship between the backscattering and the stand characteristics was strongly dependent on species showing different trends between the Sugi and Hinoki trees.?The Hinoki trees showed an increasing backscattering with increasing parameters (higher DBH, higher Tree height and higher stem volume), as it was mentioned on various researches, while the Sugi tree showed and decreasing backscattering with increasing parameters. We?have also found for the Sugi trees that the backscattering is affected strongly by the number of stems. We have assumed that this is because of the characteristics of the Sugi trees which have high moisture content in the heartwood of the stem, compared with other tree species in Japan. The results pave the way to the possibility for estimating biophysical parameters within the forests of Japan by considering such trends and at highly rugged areas by using slope corrected imagery of the SAR data.展开更多
Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time se...Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations.Using a study area in east Kalimantan,Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes(native forest,oil palm plantation,rubber plantation,non-forest).Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix;final map overall accuracy was>90%.Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm,with the highest losses between 2015 and 2016.Trending upwards from 2008 large areas of degraded or cleared forests,mapped as non-forest,were converted to oil palm plantation.Conversion of native forests to plantation mainly occurred in lowland and wetland forest,while significant forest regrowth was detected in degraded peatland.These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.展开更多
文摘In Cameroon in general and in the Highlands of Cameroon in particular, there is no fracture map since its realization is not easy. The region’s harsh accessibility and climatic conditions make it difficult to carry out geological prospecting field missions that require large investments. This study proposes a semi-automatic lineament mapping approach to facilitate the elaboration of the fracture map in the West Cameroon Highlands. It uses neural networks in tandem with PCI Geomatica’s LINE algorithm to extract lineaments semi-automatically from an ALOS PALSAR 2 radar image. The cellular neural network algorithm of Lepage et al (2000) is implemented to enhance the pre-processed radar image. Then, the LINE module of Geomatica is applied </span><span style="font-family:Verdana;">to</span><span style="font-family:Verdana;"> the enhanced image for the automatic extraction of lineaments. Finally, a control and a validation of the expert by spatial analysis allows elaborat</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;"> the fracture map. The results obtained show that neural networks enhance and facilitate the identification of lineaments on the image. The resulting map contains more than 1800 fractures with major directions N20<span style="white-space:nowrap;">°</span> - 30<span style="white-space:nowrap;">°</span>, NS, N10<span style="white-space:nowrap;">°</span> - 20<span style="white-space:nowrap;">°</span>, N50<span style="white-space:nowrap;">°</span> - 60<span style="white-space:nowrap;">°</span>, N70<span style="white-space:nowrap;">°</span> - 80<span style="white-space:nowrap;">°</span>, N80<span style="white-space:nowrap;">°</span> - 90<span style="white-space:nowrap;">°</span>, N100<span style="white-space:nowrap;">°</span> - 110<span style="white-space:nowrap;">°</span>, N110<span style="white-space:nowrap;">°</span> - 120<span style="white-space:nowrap;">°</span> and N130<span style="white-space:nowrap;">°</span> - 140<span style="white-space:nowrap;">°</span> and N140<span style="white-space:nowrap;">°</span> - 150<span style="white-space:nowrap;">°</span>. It can be very useful for geological and hydrogeological studies, and especially to inform on the productivity of aquifers in this region of high agro-pastoral and mining interest for Cameroon and the Central African sub-region.
基金the Natural Science Foundation of China(Nos.31670552,31971577)China Postdoctoral Science Foundation(No.2019 M651842)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.
基金supported by the Fakulti Pertanian,Universiti Putra Malaysia[Grant GP-IPM/2014/9434000].
文摘Agricultural crop abandonment negatively impacts local economy and environment since land,as a resource for agriculture,is not optimally utilized.To take necessary actions to rehabilitate abandoned agricultural lands,the identification of the spatial distribution of these lands must be acknowledged.While optical images had previously illustrated potentials in the identification of agricultural land abandonment,tropical areas often suffer cloud coverage problem that limits the availability of the imageries.Therefore,this study was conducted to investigate the potential of ALOS-1 and 2(Advanced Land Observing Satellite-1 and 2)PALSAR(Phased Array L-band Synthetic Aperture Radar)images for the identification and classification of abandoned agricultural crop areas,namely paddy,rubber and oil palm fields.Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR;nonetheless,oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes.The accuracy obtained for these abandoned lands of paddy,rubber and oil palm was 93.33%±0.06%,78%±2.32%and 63.33%±1.88%,respectively.This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops.The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.
文摘In this study, we have performed an analysis between the L-band backscattering intensity derived from the slope corrected ALOS PALSAR remote sensing data and the?in-situ?stand biophysical parameter of Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) trees at the forests of Chiba Prefecture, Japan. Diameter at breast height (DBH), tree height, and stem volume were statistically compared with the slope corrected sigma naught backscattering in an empirical approach. It was found that the relationship between the backscattering and the stand characteristics was strongly dependent on species showing different trends between the Sugi and Hinoki trees.?The Hinoki trees showed an increasing backscattering with increasing parameters (higher DBH, higher Tree height and higher stem volume), as it was mentioned on various researches, while the Sugi tree showed and decreasing backscattering with increasing parameters. We?have also found for the Sugi trees that the backscattering is affected strongly by the number of stems. We have assumed that this is because of the characteristics of the Sugi trees which have high moisture content in the heartwood of the stem, compared with other tree species in Japan. The results pave the way to the possibility for estimating biophysical parameters within the forests of Japan by considering such trends and at highly rugged areas by using slope corrected imagery of the SAR data.
文摘Significant areas of native forest in Kalimantan,on the island of Borneo,have been cleared for the expansion of plantations of oil palm and rubber.In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations.Using a study area in east Kalimantan,Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes(native forest,oil palm plantation,rubber plantation,non-forest).Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix;final map overall accuracy was>90%.Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm,with the highest losses between 2015 and 2016.Trending upwards from 2008 large areas of degraded or cleared forests,mapped as non-forest,were converted to oil palm plantation.Conversion of native forests to plantation mainly occurred in lowland and wetland forest,while significant forest regrowth was detected in degraded peatland.These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.
