Pub Date : 2024-11-09DOI: 10.1016/j.jag.2024.104261
Meiling Huang , Yusuyunjiang Mamitimin , Abudukeyimu Abulizi , Rebiya Yimaer , Bahejiayinaer Tiemuerbieke , Han Chen , Tongtong Tao , Yunfei Ma
Precise forecasting of land use modifications and carbon storage (CS) alterations is essential for effective regulatory measures and ecological quality enhancement. However, there are limited studies on land use dynamics and its impact on CS in the arid regions of Northwest China. Therefore, this study explores land use and CS changes in the Tulufan-Hami Basin from 2000 to 2050. The SD-FLUS and InVEST models were employed to simulate land use patterns and assess CS under three scenarios (SSP126-EP, SSP245-ND and SSP585-ED). The results of our study indicate that the area of cropland and built-up land both increased dramatically from 2000 to 2020, expanding by 347 km2 and 505 km2 respectively. CS initially rose by 0.74 × 106t from 2000 to 2010 but then declined by 1.37 × 106t from 2010 to 2020. Construction expansion and grassland degradation drove the decline. By 2050, the SSP126-EP scenario predicts an increase in CS of 3.64 × 106t compared to 2020. However, both the SSP245-ND and SSP585-ED scenarios show significant decreases, with a decline of 0.55 × 106t and 1.87 × 106t respectively. These findings provide a foundation for global ecological preservation and CS enhancement in arid regions.
{"title":"Integrated assessment of land use and carbon storage changes in the Tulufan-Hami Basin under the background of urbanization and climate change","authors":"Meiling Huang , Yusuyunjiang Mamitimin , Abudukeyimu Abulizi , Rebiya Yimaer , Bahejiayinaer Tiemuerbieke , Han Chen , Tongtong Tao , Yunfei Ma","doi":"10.1016/j.jag.2024.104261","DOIUrl":"10.1016/j.jag.2024.104261","url":null,"abstract":"<div><div>Precise forecasting of land use modifications and carbon storage (CS) alterations is essential for effective regulatory measures and ecological quality enhancement. However, there are limited studies on land use dynamics and its impact on CS in the arid regions of Northwest China. Therefore, this study explores land use and CS changes in the Tulufan-Hami Basin from 2000 to 2050. The SD-FLUS and InVEST models were employed to simulate land use patterns and assess CS under three scenarios (SSP126-EP, SSP245-ND and SSP585-ED). The results of our study indicate that the area of cropland and built-up land both increased dramatically from 2000 to 2020, expanding by 347 km<sup>2</sup> and 505 km<sup>2</sup> respectively. CS initially rose by 0.74 × 10<sup>6</sup>t from 2000 to 2010 but then declined by 1.37 × 10<sup>6</sup>t from 2010 to 2020. Construction expansion and grassland degradation drove the decline. By 2050, the SSP126-EP scenario predicts an increase in CS of 3.64 × 10<sup>6</sup>t compared to 2020. However, both the SSP245-ND and SSP585-ED scenarios show significant decreases, with a decline of 0.55 × 10<sup>6</sup>t and 1.87 × 10<sup>6</sup>t respectively. These findings provide a foundation for global ecological preservation and CS enhancement in arid regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104261"},"PeriodicalIF":7.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1016/j.jag.2024.104254
Jing Ling , Rui Liu , Shan Wei , Shaomei Chen , Luyan Ji , Yongchao Zhao , Hongsheng Zhang
Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insufficient understanding of large-scale cloud distribution and its relationship with land surface cover and urbanization. This study investigates the cloud distribution characteristics of typical urban agglomerations in different climatic regions of China using high-resolution Sentinel-2 satellite imagery and the Google Earth Engine platform. A cloud probability descriptor was constructed based on three years of high spatiotemporal resolution observations. The results revealed significant differences in cloud distribution among urban agglomerations, challenging the traditional understanding based on climate zoning. The Northeast urban agglomeration in the temperate zone exhibited high cloud coverage (37.54%), while the Chengdu-Chongqing urban agglomeration in the subtropical zone and the Qinghai-Tibet Plateau urban agglomeration in the plateau climate zone had even higher average cloud probabilities (50.72% and 43.27%, respectively). The analysis suggests land surface cover, urbanization, and other surface factors may influence cloud distribution patterns. These findings emphasize the ubiquity of cloud cover and highlight the importance of considering the complex interactions among geographical factors when characterizing cloud cover diversity. This study contributes to providing new insights for enhancing meteorological models and remote sensing observation strategies in cloudy environments across different climate zones.
{"title":"Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing","authors":"Jing Ling , Rui Liu , Shan Wei , Shaomei Chen , Luyan Ji , Yongchao Zhao , Hongsheng Zhang","doi":"10.1016/j.jag.2024.104254","DOIUrl":"10.1016/j.jag.2024.104254","url":null,"abstract":"<div><div>Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insufficient understanding of large-scale cloud distribution and its relationship with land surface cover and urbanization. This study investigates the cloud distribution characteristics of typical urban agglomerations in different climatic regions of China using high-resolution Sentinel-2 satellite imagery and the Google Earth Engine platform. A cloud probability descriptor was constructed based on three years of high spatiotemporal resolution observations. The results revealed significant differences in cloud distribution among urban agglomerations, challenging the traditional understanding based on climate zoning. The Northeast urban agglomeration in the temperate zone exhibited high cloud coverage (37.54%), while the Chengdu-Chongqing urban agglomeration in the subtropical zone and the Qinghai-Tibet Plateau urban agglomeration in the plateau climate zone had even higher average cloud probabilities (50.72% and 43.27%, respectively). The analysis suggests land surface cover, urbanization, and other surface factors may influence cloud distribution patterns. These findings emphasize the ubiquity of cloud cover and highlight the importance of considering the complex interactions among geographical factors when characterizing cloud cover diversity. This study contributes to providing new insights for enhancing meteorological models and remote sensing observation strategies in cloudy environments across different climate zones.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104254"},"PeriodicalIF":7.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1016/j.jag.2024.104238
Ryan C. Joshi , Annalise Jensen , Madeleine Pascolini-Campbell , Joshua B. Fisher
Aim
Examine the effects of evapotranspiration (ET), water use efficiency (WUE), and evaporative stress index (ESI) on wildfire temperature and extent. Compare land cover type proportions in burned area with land cover type proportions in New Mexico.
Methods
We used remotely sensed data from NASA’s ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to collect ET, WUE, & ESI data. Data were analyzed for burned areas of 10 wildfires that occurred in New Mexico between 2020 and 2022, segmenting the following land cover types: evergreen needleleaf forests, closed shrublands, open shrublands, savannas, woody savannas, grasslands, and other.
Results
ET & ESI increased throughout the duration of the wildfires, while WUE decreased. ET vs. WUE were more strongly correlated post-fire (R2 = 0.85) than pre-fire (R2 = 0.20), as was WUE vs. ESI (post-fire, R2 = 0.59; pre-fire, R2 = 0.04). Pre- and post-fire ET and ESI were positively correlated (R2 = 0.61 pre-fire, R2 = 0.53 post-fire), while post-fire WUE was negatively correlated with both post-fire ET (R2 = 0.85) and ESI (R2 = 0.59). We found that the land cover composition of the areas burned by the 10 studied wildfires differs from the land cover composition of New Mexico as a whole (p < 0.05).
Conclusions
Our findings present increasing trends in ET and ESI, and decreasing trends in WUE before, during, and after a wildfire. By monitoring changes in those three variables, we can identify areas that are at high risk for wildfires. Savannas and woody savannas should be closely monitored because a disproportionately large proportion of acres burned in 2022 were savannas and woody savannas.
{"title":"Coupling between evapotranspiration, water use efficiency, and evaporative stress index strengthens after wildfires in New Mexico, USA","authors":"Ryan C. Joshi , Annalise Jensen , Madeleine Pascolini-Campbell , Joshua B. Fisher","doi":"10.1016/j.jag.2024.104238","DOIUrl":"10.1016/j.jag.2024.104238","url":null,"abstract":"<div><h3>Aim</h3><div>Examine the effects of evapotranspiration (ET), water use efficiency (WUE), and evaporative stress index (ESI) on wildfire temperature and extent. Compare land cover type proportions in burned area with land cover type proportions in New Mexico.</div></div><div><h3>Methods</h3><div>We used remotely sensed data from NASA’s ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to collect ET, WUE, & ESI data. Data were analyzed for burned areas of 10 wildfires that occurred in New Mexico between 2020 and 2022, segmenting the following land cover types: evergreen needleleaf forests, closed shrublands, open shrublands, savannas, woody savannas, grasslands, and other.</div></div><div><h3>Results</h3><div>ET & ESI increased throughout the duration of the wildfires, while WUE decreased. ET vs. WUE were more strongly correlated post-fire (R<sup>2</sup> = 0.85) than pre-fire (R<sup>2</sup> = 0.20), as was WUE vs. ESI (post-fire, R<sup>2</sup> = 0.59; pre-fire, R<sup>2</sup> = 0.04). Pre- and post-fire ET and ESI were positively correlated (R<sup>2</sup> = 0.61 pre-fire, R<sup>2</sup> = 0.53 post-fire), while post-fire WUE was negatively correlated with both post-fire ET (R<sup>2</sup> = 0.85) and ESI (R<sup>2</sup> = 0.59). We found that the land cover composition of the areas burned by the 10 studied wildfires differs from the land cover composition of New Mexico as a whole (p < 0.05).</div></div><div><h3>Conclusions</h3><div>Our findings present increasing trends in ET and ESI, and decreasing trends in WUE before, during, and after a wildfire. By monitoring changes in those three variables, we can identify areas that are at high risk for wildfires. Savannas and woody savannas should be closely monitored because a disproportionately large proportion of acres burned in 2022 were savannas and woody savannas.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104238"},"PeriodicalIF":7.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1016/j.jag.2024.104219
Xi Cheng , Jieyu Yang , Zhiyong Han , Guozhong Shi , Deng Pan , Likang Meng , Zhuojun Zeng , Zhanfeng Shen
Accurate assessment of distribution patterns and dynamic insights into rural populations is pivotal for comprehending domestic waste generation, recycling, and transportation in rural territories. Given that the dispersion of rural inhabitants exhibits minimal variation and maintains stability, this research endeavors to establish a pragmatic model for rural domestic waste collection and routing, leveraging the capabilities of very high-resolution remote sensing combined with geographic information system (GIS) techniques. Specifically, the Dilated LinkNet model was employed to discern features such as buildings, roads, water bodies, farmlands, and forests from the high-resolution remote sensing imagery. A novel multiple K-means clustering approach was devised for building segmentation. Within these clusters, an assortment of spatial regulations and evaluations facilitated the judicious selection of environmentally-conscious waste collection sites (WCSs). The Pointer Network, augmented with reinforcement learning, executed a traveling salesman analysis on these chosen WCSs, yielding the optimal collection trajectory. Validated in Huangtu Town, a quintessential rural region in China, our model manifested superior recognition precision, recording IoU accuracies of 0.902, 0.926, 0.933, 0.891, and 0.849 for buildings, roads, water bodies, farmlands, and forests respectively. Notably, when compared to our field survey data, the optimized daily collection route in a rural context decreased from 256.40 km before optimization to 140.44 km, reflecting a substantial reduction of 45.23% in total distance. This study furnishes an effective model that relies solely on information from remote-sensing images for efficient rural waste collection and extends invaluable insights to planners and administrators in the realm of rural and township waste management.
{"title":"Optimizing rural waste management: Leveraging high-resolution remote sensing and GIS for efficient collection and routing","authors":"Xi Cheng , Jieyu Yang , Zhiyong Han , Guozhong Shi , Deng Pan , Likang Meng , Zhuojun Zeng , Zhanfeng Shen","doi":"10.1016/j.jag.2024.104219","DOIUrl":"10.1016/j.jag.2024.104219","url":null,"abstract":"<div><div>Accurate assessment of distribution patterns and dynamic insights into rural populations is pivotal for comprehending domestic waste generation, recycling, and transportation in rural territories. Given that the dispersion of rural inhabitants exhibits minimal variation and maintains stability, this research endeavors to establish a pragmatic model for rural domestic waste collection and routing, leveraging the capabilities of very high-resolution remote sensing combined with geographic information system (GIS) techniques. Specifically, the Dilated LinkNet model was employed to discern features such as buildings, roads, water bodies, farmlands, and forests from the high-resolution remote sensing imagery. A novel multiple K-means clustering approach was devised for building segmentation. Within these clusters, an assortment of spatial regulations and evaluations facilitated the judicious selection of environmentally-conscious waste collection sites (WCSs). The Pointer Network, augmented with reinforcement learning, executed a traveling salesman analysis on these chosen WCSs, yielding the optimal collection trajectory. Validated in Huangtu Town, a quintessential rural region in China, our model manifested superior recognition precision, recording IoU accuracies of 0.902, 0.926, 0.933, 0.891, and 0.849 for buildings, roads, water bodies, farmlands, and forests respectively. Notably, when compared to our field survey data, the optimized daily collection route in a rural context decreased from 256.40 km before optimization to 140.44 km, reflecting a substantial reduction of 45.23% in total distance. This study furnishes an effective model that relies solely on information from remote-sensing images for efficient rural waste collection and extends invaluable insights to planners and administrators in the realm of rural and township waste management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104219"},"PeriodicalIF":7.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1016/j.jag.2024.104224
Lu Zhang , Andrew O. Finley , Arne Nothdurft , Sudipto Banerjee
Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised inference and predictive performance. To avoid losing potential information provided by finer spatial resolution data and improve predictive performance, we propose a new Bayesian method that constructs a latent spatial process model at the finest spatial resolution. This model is tailored to settings where the outcome variable is measured on a coarser spatial resolution than predictor variables—a configuration seen increasingly when high spatial resolution remotely sensed predictors are used in analysis. A key contribution of this work is an efficient algorithm that enables full Bayesian inference using finer resolution data while optimizing computational and storage costs. The proposed method is applied to a forest damage assessment for the 2018 Adrian storm in Carinthia, Austria, that uses high-resolution laser imaging detection and ranging (LiDAR) measurements and relatively coarse resolution forest inventory measurements. Extensive simulation studies demonstrate the proposed approach substantially improves inference for small prediction units.
{"title":"Bayesian modeling of incompatible spatial data: A case study involving Post-Adrian storm forest damage assessment","authors":"Lu Zhang , Andrew O. Finley , Arne Nothdurft , Sudipto Banerjee","doi":"10.1016/j.jag.2024.104224","DOIUrl":"10.1016/j.jag.2024.104224","url":null,"abstract":"<div><div>Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised inference and predictive performance. To avoid losing potential information provided by finer spatial resolution data and improve predictive performance, we propose a new Bayesian method that constructs a latent spatial process model at the finest spatial resolution. This model is tailored to settings where the outcome variable is measured on a coarser spatial resolution than predictor variables—a configuration seen increasingly when high spatial resolution remotely sensed predictors are used in analysis. A key contribution of this work is an efficient algorithm that enables full Bayesian inference using finer resolution data while optimizing computational and storage costs. The proposed method is applied to a forest damage assessment for the 2018 Adrian storm in Carinthia, Austria, that uses high-resolution laser imaging detection and ranging (LiDAR) measurements and relatively coarse resolution forest inventory measurements. Extensive simulation studies demonstrate the proposed approach substantially improves inference for small prediction units.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104224"},"PeriodicalIF":7.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1016/j.jag.2024.104248
Ruize Xu , Jiahua Zhang , Fang Chen , Bo Yu , Shawkat Ali , Hidayat Ullah , Ali Salem Al-Sakkaf
Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate monthly GPP at a 30 m resolution across Hainan Island from 2000 to 2020. The finer GPP can capture more spatial details and show higher accuracy at site scales (R = 0.79 and NRMSE = 14.79 %). Trend analysis and Hurst exponent were used to reveal spatiotemporal dynamics and sustainability of GPP. Meanwhile, nonlinear Granger causality tests quantified both concurrent and lagged correlations between various environmental factors and GPP. The results indicated significant GPP increases across 98.5 % of vegetated areas, with an annual rise of 437.02 g C/m2, and a marked improvement in trends around 2011. Future projections suggest sustained high GPP sustainability (Hurst = 0.53), and reducing “positive-inconsistent” areas in the northeast and southwest is crucial for enhancing local carbon sinks. Furthermore, water availability, temperature, and radiation were primary drivers of GPP changes, affecting 53.55 %, 27.77 %, and 14.43 % of vegetated areas, respectively, with their compounded effects enhancing explanatory power by 35.84 %. Relative humidity dominated water availability impacts on GPP (10.02 % to 79.98 % variation), surpassing precipitation and soil moisture impacts. Lag effects were observed in 68.83 % of vegetated areas, with 1 to 4-month delays in responses to net solar radiation and surface temperature, especially in forest and shrubland ecosystems. This study provides deeper insights into fine-scale GPP simulations and analysis of climate interactions, which are crucial for effective carbon cycle management in tropical ecosystems.
{"title":"Quantitative assessment of spatiotemporal variations and drivers of gross primary productivity in tropical ecosystems at higher resolution","authors":"Ruize Xu , Jiahua Zhang , Fang Chen , Bo Yu , Shawkat Ali , Hidayat Ullah , Ali Salem Al-Sakkaf","doi":"10.1016/j.jag.2024.104248","DOIUrl":"10.1016/j.jag.2024.104248","url":null,"abstract":"<div><div>Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate monthly GPP at a 30 m resolution across Hainan Island from 2000 to 2020. The finer GPP can capture more spatial details and show higher accuracy at site scales (R = 0.79 and NRMSE = 14.79 %). Trend analysis and Hurst exponent were used to reveal spatiotemporal dynamics and sustainability of GPP. Meanwhile, nonlinear Granger causality tests quantified both concurrent and lagged correlations between various environmental factors and GPP. The results indicated significant GPP increases across 98.5 % of vegetated areas, with an annual rise of 437.02 g C/m<sup>2</sup>, and a marked improvement in trends around 2011. Future projections suggest sustained high GPP sustainability (Hurst = 0.53), and reducing “positive-inconsistent” areas in the northeast and southwest is crucial for enhancing local carbon sinks. Furthermore, water availability, temperature, and radiation were primary drivers of GPP changes, affecting 53.55 %, 27.77 %, and 14.43 % of vegetated areas, respectively, with their compounded effects enhancing explanatory power by 35.84 %. Relative humidity dominated water availability impacts on GPP (10.02 % to 79.98 % variation), surpassing precipitation and soil moisture impacts. Lag effects were observed in 68.83 % of vegetated areas, with 1 to 4-month delays in responses to net solar radiation and surface temperature, especially in forest and shrubland ecosystems. This study provides deeper insights into fine-scale GPP simulations and analysis of climate interactions, which are crucial for effective carbon cycle management in tropical ecosystems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104248"},"PeriodicalIF":7.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jag.2024.104244
Xiao Zhu , Tiejun Wang , Andrew K. Skidmore , Stephen J. Lee , Isla Duporge
Very high-resolution (VHR) optical satellite imagery offers significant potential for detailed land cover mapping. However, terrain shadows, which appear dark and lack texture and detail, are especially acute at low solar elevations. These shadows hinder the creation of spatially complete and accurate land cover maps, particularly in rugged mountainous environments. While many methods have been proposed to mitigate terrain shadows in remote sensing, they either perform insufficient shadow reduction or rely on high-resolution digital elevation models which are often unavailable for VHR image shadow mitigation. In this paper, we propose a bi-temporal image fusion approach to mitigate terrain shadows in VHR satellite imagery. Our approach fuses a WorldView-2 multispectral image, which contains significant terrain shadows, with a corresponding geometrically registered WorldView-1 panchromatic image, which has minimal shadows. This fusion is applied to improve the mapping of evergreen conifers in temperate mixed mountain forests. To evaluate the effectiveness of our approach, we first improve an existing shadow detection method by Silva et al. (2018) to more accurately detect shadows in mountainous, forested landscapes. Next, we propose a quantitative algorithm that differentiates dark and light terrain shadows in VHR satellite imagery based on object visibility in shadowed areas. Finally, we apply a state-of-the-art 3D U-Net deep learning method to detect evergreen conifers. Our study shows that the proposed approach significantly reduces terrain shadows and enhances the detection of evergreen conifers in shaded areas. This is the first time a bi-temporal image fusion approach has been used to mitigate terrain shadow effects for land cover mapping at a very high spatial resolution. This approach can also be applied to other VHR satellite sensors. However, careful image co-registration will be necessary when applying this technique to multi-sensor systems beyond the WorldView constellation, such as Pléiades or SkySat.
{"title":"Mitigating terrain shadows in very high-resolution satellite imagery for accurate evergreen conifer detection using bi-temporal image fusion","authors":"Xiao Zhu , Tiejun Wang , Andrew K. Skidmore , Stephen J. Lee , Isla Duporge","doi":"10.1016/j.jag.2024.104244","DOIUrl":"10.1016/j.jag.2024.104244","url":null,"abstract":"<div><div>Very high-resolution (VHR) optical satellite imagery offers significant potential for detailed land cover mapping. However, terrain shadows, which appear dark and lack texture and detail, are especially acute at low solar elevations. These shadows hinder the creation of spatially complete and accurate land cover maps, particularly in rugged mountainous environments. While many methods have been proposed to mitigate terrain shadows in remote sensing, they either perform insufficient shadow reduction or rely on high-resolution digital elevation models which are often unavailable for VHR image shadow mitigation. In this paper, we propose a bi-temporal image fusion approach to mitigate terrain shadows in VHR satellite imagery. Our approach fuses a WorldView-2 multispectral image, which contains significant terrain shadows, with a corresponding geometrically registered WorldView-1 panchromatic image, which has minimal shadows. This fusion is applied to improve the mapping of evergreen conifers in temperate mixed mountain forests. To evaluate the effectiveness of our approach, we first improve an existing shadow detection method by Silva et al. (2018) to more accurately detect shadows in mountainous, forested landscapes. Next, we propose a quantitative algorithm that differentiates dark and light terrain shadows in VHR satellite imagery based on object visibility in shadowed areas. Finally, we apply a state-of-the-art 3D U-Net deep learning method to detect evergreen conifers. Our study shows that the proposed approach significantly reduces terrain shadows and enhances the detection of evergreen conifers in shaded areas. This is the first time a bi-temporal image fusion approach has been used to mitigate terrain shadow effects for land cover mapping at a very high spatial resolution. This approach can also be applied to other VHR satellite sensors. However, careful image co-registration will be necessary when applying this technique to multi-sensor systems beyond the WorldView constellation, such as Pléiades or SkySat.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104244"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jag.2024.104184
Quanxing Wan , Magdalena Smigaj , Benjamin Brede , Lammert Kooistra
Unoccupied aerial vehicles (UAVs) equipped with thermal cameras show great promise for precision agriculture, but challenges persist in analyzing land surface temperature (LST). This study explores the influence of ambient environmental conditions and intrinsic characteristics of uncooled thermal cameras on the accuracy of temperature measurements obtained through UAV-based thermal cameras. The research utilized DJI Matrice 210 quad-rotor UAVs equipped with FLIR Tau 2 and WIRIS 2nd Gen thermal cameras. The experimental design involved strategically selected temperature reference materials of diverse compositions. UAV flights were conducted at varying altitudes, capturing thermal images correlated with ground-based thermocouple measurements. Results indicate that increasing flight altitude resulted in underestimated temperatures measured by UAVs for objects with higher kinematic temperatures, while objects with lower temperatures displayed higher measurements. The study integrates multiple environmental metrics, illustrating the complex influence of air temperature, humidity, net radiation, and wind speed on temperature measurements, with variations observed between FLIR Tau 2 and WIRIS 2nd Gen camera models. Linear regression models highlight the diverse impact of these metrics on UAV-based temperature observations. Furthermore, an analysis of uncooled thermal sensor characteristics reveals a correlation between UAV-measured temperatures and the focal plane array (FPA) temperature, emphasizing the importance of considering intrinsic sensor dynamics. These findings provide valuable insights for enhancing the reliability of UAV-based thermal measurements in agricultural and environmental monitoring. The research underscores the necessity for a comprehensive understanding of both ambient conditions and camera-model-specific dynamics to optimize thermal imaging accuracy for precision agriculture applications. Accordingly, the recommended procedures have been described to reduce the effect of identified sources of influence.
配备热像仪的无人飞行器（UAV）在精准农业方面大有可为，但在分析地表温度（LST）方面仍存在挑战。本研究探讨了周围环境条件和非制冷红外热像仪固有特性对无人飞行器红外热像仪温度测量精度的影响。研究使用了配备 FLIR Tau 2 和 WIRIS 第二代红外热像仪的大疆 Matrice 210 四旋翼无人机。实验设计包括战略性地选择不同成分的温度参考材料。无人机在不同高度飞行，捕捉与地面热电偶测量结果相关的热图像。结果表明，飞行高度的增加导致无人机对运动温度较高的物体所测得的温度被低估，而温度较低的物体则显示出较高的测量值。该研究整合了多个环境指标，说明了空气温度、湿度、净辐射和风速对温度测量的复杂影响，并观察到 FLIR Tau 2 和 WIRIS 第二代相机型号之间的差异。线性回归模型凸显了这些指标对无人机温度观测的不同影响。此外，对非制冷热传感器特性的分析表明，无人机测量的温度与焦平面阵列（FPA）温度之间存在相关性，强调了考虑传感器内在动态的重要性。这些发现为提高无人机热测量在农业和环境监测中的可靠性提供了宝贵的见解。研究强调，必须全面了解环境条件和相机模型的特定动态，以优化精准农业应用中的热成像精度。因此，已对建议的程序进行了说明，以减少已确定的影响源的影响。
{"title":"Optimizing UAV-based uncooled thermal cameras in field conditions for precision agriculture","authors":"Quanxing Wan , Magdalena Smigaj , Benjamin Brede , Lammert Kooistra","doi":"10.1016/j.jag.2024.104184","DOIUrl":"10.1016/j.jag.2024.104184","url":null,"abstract":"<div><div>Unoccupied aerial vehicles (UAVs) equipped with thermal cameras show great promise for precision agriculture, but challenges persist in analyzing land surface temperature (LST). This study explores the influence of ambient environmental conditions and intrinsic characteristics of uncooled thermal cameras on the accuracy of temperature measurements obtained through UAV-based thermal cameras. The research utilized DJI Matrice 210 quad-rotor UAVs equipped with FLIR Tau 2 and WIRIS 2<sup>nd</sup> Gen thermal cameras. The experimental design involved strategically selected temperature reference materials of diverse compositions. UAV flights were conducted at varying altitudes, capturing thermal images correlated with ground-based thermocouple measurements. Results indicate that increasing flight altitude resulted in underestimated temperatures measured by UAVs for objects with higher kinematic temperatures, while objects with lower temperatures displayed higher measurements. The study integrates multiple environmental metrics, illustrating the complex influence of air temperature, humidity, net radiation, and wind speed on temperature measurements, with variations observed between FLIR Tau 2 and WIRIS 2<sup>nd</sup> Gen camera models. Linear regression models highlight the diverse impact of these metrics on UAV-based temperature observations. Furthermore, an analysis of uncooled thermal sensor characteristics reveals a correlation between UAV-measured temperatures and the focal plane array (FPA) temperature, emphasizing the importance of considering intrinsic sensor dynamics. These findings provide valuable insights for enhancing the reliability of UAV-based thermal measurements in agricultural and environmental monitoring. The research underscores the necessity for a comprehensive understanding of both ambient conditions and camera-model-specific dynamics to optimize thermal imaging accuracy for precision agriculture applications. Accordingly, the recommended procedures have been described to reduce the effect of identified sources of influence.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104184"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification system suitable for remote sensing, incorporating the soil moisture, vegetation cover and temporal dynamic characteristics of wetlands; (2) introduces a theoretically plausible wetland sample identification method based on the ecological, geographical and temporal dynamic characteristics of wetland ecosystems; (3) develops an approach that combines the Inundation-Frequency and Ecological Remote Sensing Indicators for global wetland sampling based on global climatic zones. The global wetland sample set was finally produced with 64,486 samples. The dataset revealed that seasonal marsh, swamp, mangrove, floodplain, salt marsh, tidal flat and permanent marsh accounted for 22.99%, 20.05%, 18.06%, 14.58%, 12.38%, 10.62% and 1.29% of the total sample set, respectively. Furthermore, the water body sample set comprised 13,402 samples, distributed among permanent (45.50%), seasonal (31.35%) and temporary (23.15%) water bodies. The proposed knowledge-based method, which makes use of big earth-observing data and the Google Earth Engine platform, has been demonstrated to have the capability to generate reliable wetland samples with a high degree of accuracy. This represents the first effort to create a global wetland sample set, which has the potential to offer critical support for comprehensive wetland mapping initiatives
{"title":"Integration of ecological knowledge with Google Earth Engine for diverse wetland sampling in global mapping","authors":"Xuanlin Huo, Zhenguo Niu, Linsong Liu, Yuhang Jing","doi":"10.1016/j.jag.2024.104249","DOIUrl":"10.1016/j.jag.2024.104249","url":null,"abstract":"<div><div>Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification system suitable for remote sensing, incorporating the soil moisture, vegetation cover and temporal dynamic characteristics of wetlands; (2) introduces a theoretically plausible wetland sample identification method based on the ecological, geographical and temporal dynamic characteristics of wetland ecosystems; (3) develops an approach that combines the Inundation-Frequency and Ecological Remote Sensing Indicators for global wetland sampling based on global climatic zones. The global wetland sample set was finally produced with 64,486 samples. The dataset revealed that seasonal marsh, swamp, mangrove, floodplain, salt marsh, tidal flat and permanent marsh accounted for 22.99%, 20.05%, 18.06%, 14.58%, 12.38%, 10.62% and 1.29% of the total sample set, respectively. Furthermore, the water body sample set comprised 13,402 samples, distributed among permanent (45.50%), seasonal (31.35%) and temporary (23.15%) water bodies. The proposed knowledge-based method, which makes use of big earth-observing data and the Google Earth Engine platform, has been demonstrated to have the capability to generate reliable wetland samples with a high degree of accuracy. This represents the first effort to create a global wetland sample set, which has the potential to offer critical support for comprehensive wetland mapping initiatives</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104249"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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