This work sets out to simulate landscape model of Mu Us Desert in Inner Mongolia Autonomous Region of China at different spatial resolution using remote sensing images and distinguished landscape heterogeneity among d...This work sets out to simulate landscape model of Mu Us Desert in Inner Mongolia Autonomous Region of China at different spatial resolution using remote sensing images and distinguished landscape heterogeneity among different spatial resolutions. Landscape models were created from classification image of SPOT satellite data with 20m resolution and NOAA data with 1 km resolution. This study created landscape models of different scales by resampling the SPOT classified image using majority rule. The pixel resolution was increased from the finest scale of 20m by 20m up to 1000m by 1000m that was the coarsest spatial resolution. The Shannon diversity index was used to compare landscape models between different scales. At the finer scale the verify small patches such as deciduous forest, shrub and reedswamp with high vegetation coverage set on matrices with low vegetation cover (moving sand dune and sparse grassland) were verified. Broadening of scale resulted to the loss of small patches and at 1000m resolution, matrix classes were dominant. At 1km resolution of NOAA data, the matrix classes which greatly related to the topography of Mu Us Desert were detected. Diversity index decreased during scale broadening and the difference between SPOT 1km scale model and AVHRR data was not significant. The results showed that SPOT 20m model is good for the use of ecotone oriented revegetation planning, and NOAA 1km model is good for the seasonal and annual monitoring of each landscape unit, and revegetation planning at the regional level.展开更多
A monitoring program was developed to assess the cover of saltgrass managed for dust control on the saline dry Owens Lake. Although the original intent was to manage the vegetation as total cover that included green a...A monitoring program was developed to assess the cover of saltgrass managed for dust control on the saline dry Owens Lake. Although the original intent was to manage the vegetation as total cover that included green and senesced leaf and stem material, aged leaves that make up a large proportion of total cover were not differentiable spectrally from the background salt and lakebed. Hence, greenness-based indices were explored for detection of plant recruitment. Since all plant cover begins as green and growing, greenness indices provide a measure of all future cover whether living or senesced. The criteria for judging compliance were changed so that spatially variable vegetation cover measured as a milestone will need to be met in the future. A derivative of NDVI, NDVIx, calculated using scene statistics, proved highly accurate, to about 0.001 of this index and with an average signal to noise ratio of 64. This high level of accuracy allowed detection of small changes in vegetation growth and vigor. Performance according to the benchmark-as-par standard was determined through combined use of cumulative distribution functions and derivative maps.展开更多
Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentu...Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentuated in recent years by signifi cant human pressure and eff ects of climate change;hence,the health of the stands needs to be monitored.In this study,the Google Engine Earth platform was leveraged to extract the normalized diff erence vegetation index(NDVI)and soil-adjusted vegetation index,from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco.Our results highlight the importance of interannual variations in NDVI in forest monitoring;the variations had a signifi cantly high relationship(p<0.001)with dieback severity.NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coeffi cients of 0.49 and−0.67,highlighting its ability to predict phenotypic changes in forest species.Monthly interannual variation in NDVI between 2016 and 2017 seemed to confi rm fi eld observations of cork oak dieback in 2018,with the largest decreases in NDVI(up to−38%)in December in the most-aff ected plots.Analysis of the infl uence of ecological factors on dieback highlighted the role of substrate as a driver of dieback,with the most severely aff ected plots characterized by granite-granodiorite substrates.展开更多
Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning suc...Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.展开更多
This research investigates the recent distribution variation trends of vegetation in the Tibet region using Normalized Difference Vegetation Index （NDVI） data from 2000 to 2007. It also discusses the causes of veget...This research investigates the recent distribution variation trends of vegetation in the Tibet region using Normalized Difference Vegetation Index （NDVI） data from 2000 to 2007. It also discusses the causes of vegetation degradation in typical regions （such as Nagqu） based on climatic conditions, human activity, and other influencing factors. Results show that the areas with the best vegetation cover are in Nyingchi and the southern part of Shannan, followed by Chamdo, the Lhasa area, and the eastern part of Nagqu. Vegetation in various regions exhibits significant seasonal differences. The vegetation status has improved in some parts of the Tibet region in the past few years, while the areas with the most serious degradation are in the middle and southem parts of the Nagqu region. On average, distinct vegetation degradation occurred between 2003 and 2006 in the whole Tibet region but vegetation has been increasing since 2006. The vegetation cover in summer basically determines the annual vegetation status. An increase in precipitation and decrease in wind speed generally corresponds to an increase in vegetation cover. The reverse is also true： a decrease in precipitation and increase in wind speed correspond to the decrease in vegetation cover. NDVI is thus positively related to temperature and precipitation but has a negative relation with wind speed. Increasing temperature and decreasing precipitation have led to the present vegetation degradation in Nagqu, and vegetation in all of these regions has been affected by growth of human population, intensified urbanization, livestock overgrazing leading to the proliferation of noxious plants, extraction of underground minerals and alluvial gold, extensive harvesting of traditional Chinese medicinal plants [e.g., Cordyceps sinensis, Caladium spp., and saffron crocus （Crocus sativus）], and serious rodent and other pest damage.展开更多
Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in...Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.展开更多
East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a Wo...East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a World Heritage in Danger in 2013.For East Rennell World Heritage Site(ERWHS)to‘shed’its‘Danger’status the management must monitor forest cover both within and outside of ERWHS.We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998.95%of the island is still covered by undisturbed forests;annual average normalized difference vegetation index(NDVI)for the whole island was above 0.91 in 2015.However,vegetation cover in the island has been slowly decreasing,at a rate of–0.0011 NDVI per year between 2000 and 2015.This decrease less pronounced inside ERWHS compared to areas outside.While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%.We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.展开更多
文摘This work sets out to simulate landscape model of Mu Us Desert in Inner Mongolia Autonomous Region of China at different spatial resolution using remote sensing images and distinguished landscape heterogeneity among different spatial resolutions. Landscape models were created from classification image of SPOT satellite data with 20m resolution and NOAA data with 1 km resolution. This study created landscape models of different scales by resampling the SPOT classified image using majority rule. The pixel resolution was increased from the finest scale of 20m by 20m up to 1000m by 1000m that was the coarsest spatial resolution. The Shannon diversity index was used to compare landscape models between different scales. At the finer scale the verify small patches such as deciduous forest, shrub and reedswamp with high vegetation coverage set on matrices with low vegetation cover (moving sand dune and sparse grassland) were verified. Broadening of scale resulted to the loss of small patches and at 1000m resolution, matrix classes were dominant. At 1km resolution of NOAA data, the matrix classes which greatly related to the topography of Mu Us Desert were detected. Diversity index decreased during scale broadening and the difference between SPOT 1km scale model and AVHRR data was not significant. The results showed that SPOT 20m model is good for the use of ecotone oriented revegetation planning, and NOAA 1km model is good for the seasonal and annual monitoring of each landscape unit, and revegetation planning at the regional level.
文摘A monitoring program was developed to assess the cover of saltgrass managed for dust control on the saline dry Owens Lake. Although the original intent was to manage the vegetation as total cover that included green and senesced leaf and stem material, aged leaves that make up a large proportion of total cover were not differentiable spectrally from the background salt and lakebed. Hence, greenness-based indices were explored for detection of plant recruitment. Since all plant cover begins as green and growing, greenness indices provide a measure of all future cover whether living or senesced. The criteria for judging compliance were changed so that spatially variable vegetation cover measured as a milestone will need to be met in the future. A derivative of NDVI, NDVIx, calculated using scene statistics, proved highly accurate, to about 0.001 of this index and with an average signal to noise ratio of 64. This high level of accuracy allowed detection of small changes in vegetation growth and vigor. Performance according to the benchmark-as-par standard was determined through combined use of cumulative distribution functions and derivative maps.
文摘Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity,playing an important ecological and socioeconomic role.Considerable degradation of the forests has been accentuated in recent years by signifi cant human pressure and eff ects of climate change;hence,the health of the stands needs to be monitored.In this study,the Google Engine Earth platform was leveraged to extract the normalized diff erence vegetation index(NDVI)and soil-adjusted vegetation index,from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco.Our results highlight the importance of interannual variations in NDVI in forest monitoring;the variations had a signifi cantly high relationship(p<0.001)with dieback severity.NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coeffi cients of 0.49 and−0.67,highlighting its ability to predict phenotypic changes in forest species.Monthly interannual variation in NDVI between 2016 and 2017 seemed to confi rm fi eld observations of cork oak dieback in 2018,with the largest decreases in NDVI(up to−38%)in December in the most-aff ected plots.Analysis of the infl uence of ecological factors on dieback highlighted the role of substrate as a driver of dieback,with the most severely aff ected plots characterized by granite-granodiorite substrates.
基金National Natural Science Foundation of China,No.42371121Joint Fund for Regional Innovation and Development of the National Natural Science Foundation of China,No.U22A20570Science and Technology Innovation Program of Hunan Province,China,No.2022RC4027。
文摘Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.
基金supported by programs of the National Natural Science Foundation of China(No.40665003)the Institute of Plateau Meteorology(No.BROP200705)
文摘This research investigates the recent distribution variation trends of vegetation in the Tibet region using Normalized Difference Vegetation Index （NDVI） data from 2000 to 2007. It also discusses the causes of vegetation degradation in typical regions （such as Nagqu） based on climatic conditions, human activity, and other influencing factors. Results show that the areas with the best vegetation cover are in Nyingchi and the southern part of Shannan, followed by Chamdo, the Lhasa area, and the eastern part of Nagqu. Vegetation in various regions exhibits significant seasonal differences. The vegetation status has improved in some parts of the Tibet region in the past few years, while the areas with the most serious degradation are in the middle and southem parts of the Nagqu region. On average, distinct vegetation degradation occurred between 2003 and 2006 in the whole Tibet region but vegetation has been increasing since 2006. The vegetation cover in summer basically determines the annual vegetation status. An increase in precipitation and decrease in wind speed generally corresponds to an increase in vegetation cover. The reverse is also true： a decrease in precipitation and increase in wind speed correspond to the decrease in vegetation cover. NDVI is thus positively related to temperature and precipitation but has a negative relation with wind speed. Increasing temperature and decreasing precipitation have led to the present vegetation degradation in Nagqu, and vegetation in all of these regions has been affected by growth of human population, intensified urbanization, livestock overgrazing leading to the proliferation of noxious plants, extraction of underground minerals and alluvial gold, extensive harvesting of traditional Chinese medicinal plants [e.g., Cordyceps sinensis, Caladium spp., and saffron crocus （Crocus sativus）], and serious rodent and other pest damage.
基金supported by the National Natural Science Foundation of China (41471335, 41271407)the National Remote Sensing Survey and Assessment of Eco-Environment Change between 2000 and 2010, China (STSN-1500)+2 种基金the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2013BAD05B03)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050601)the International Science and Technology (S&T) Cooperation Program of China (2012DFG22050)
文摘Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.
基金supported by the National Key Research and Development Programs of China(Grant No.2016YFA0600302 and 2016YFB0501502)the Hainan Provincial key technology research and demonstration programs of farmland improvement(HNGDhs2015)+1 种基金the programs of the National Natural Science Foundation of China(Grant No.61801443 and 61401461)the Hainan Provincial Department of Science and Technology under the Grant No.ZDKJ2016021 and ZDKJ2016015-1.
文摘East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a World Heritage in Danger in 2013.For East Rennell World Heritage Site(ERWHS)to‘shed’its‘Danger’status the management must monitor forest cover both within and outside of ERWHS.We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998.95%of the island is still covered by undisturbed forests;annual average normalized difference vegetation index(NDVI)for the whole island was above 0.91 in 2015.However,vegetation cover in the island has been slowly decreasing,at a rate of–0.0011 NDVI per year between 2000 and 2015.This decrease less pronounced inside ERWHS compared to areas outside.While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%.We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.
