- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
摘要： Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.
关键词： afforestation,forest inventory,Three-North Shelter Forest Program,time-series,forest monitoring
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC) - Bucharest, Romania (2018.10.29-2018.10.31)] 2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC) - Remote Sensing Computing Model for Forest Monitoring in Cloud
摘要： Natural resource management deals with managing the way in which people and natural landscapes interact. Recent years have made significant progress in developing and improving technology that provides multiple ways of monitoring natural resources. This paper propose a remote sensing computing model in Cloud environment in order to monitor forests. We tackle the question of creating a monitoring system, using satellites images of forest areas. Furthermore, the paper analyses the performance of current technologies for storage management, automation deployment, system scaling, integration with a public or private cloud. Also, we take into consideration the possibility of parallelizing current algorithms for image processing.
关键词： Remote Sensing Computing,Forest Monitoring,Cloud Computing,Microservices
更新于2025-09-23 15:22:29
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Towards high throughput assessment of canopy dynamics: The estimation of leaf area structure in Amazonian forests with multitemporal multi-sensor airborne lidar
摘要： Leaf area dynamics offer information about changes in forest biomass and canopy function critical to understanding the role of forests in the climate system and carbon cycle. Airborne small footprint lidar is a potential major source for the detection of variation in leaf area density (LAD), LAD vertical profiles, and total leaf area (leaf area index, LAI), from sites to regional scales. However, the sensitivities of lidar-based LAD and LAI estimation are not yet well known, particularly in dense forests, over landscape heterogeneity, sensor system, and survey differences, and through time. To address these questions, we compared 16 pairs of multitemporal airborne lidar surveys with four different laser sensors across six Amazon forest sites with resurvey intervals ranging from one to nine years. We tested whether the different laser sensors, and the pulse return density of laser sampling (variable between and within each survey) introduce systematic biases. Laser sensors created consistent biases that accounted for up to 18.20% of LAD differences between surveys, but biases could be corrected with a simple regression approach. Lidar pulse return density had little appreciable bias impact when above 20 returns per m2. After correction, repeated mean and site maximum LAI estimates became significantly correlated (R2 ~0.8), while LAD profiles revealed site differences. Heterogeneity and change in LAD structure were detectable at the ecologically relevant 1/4 ha forest neighborhood grid scale, as evidenced by the high correlation of profile variation between surveys, with the strength of correlation (R2 value) significantly decreasing with increasing survey interval (0.74 to 0.16 from one to nine years), consistent with accumulating effects of forest dynamics. Sensor-induced biases trended towards correlation with lidar footprint (beam width). The LAD estimation and bias correction approach developed in this study provides the standardization critical for heterogeneous lidar networks that offer high throughput functional ecological monitoring of climatically important forests like the Amazon.
关键词： Tropical forest monitoring,Forest dynamics,LAI,Lidar,Forest degradation,Leaf area estimation,Leaf area profiles
更新于2025-09-23 15:21:01
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Remote Sensing Image Restoration Based on an improved Landweber Iterative Method for Forest Monitoring and Management
摘要： As an advanced space exploration technologies, remote sensing technology has been widely used in forest monitoring and management. Forest current conditions could be reflected in real-time remote sensing images, but due to various imaging system and its environmental constraints, the original remote sensing images is often blurred. In this paper, two common remote-sensing imaging blurs, named motion blur and atmospheric turbulence blur, were studied and discussed the mechanism of the image blurred, and proposed a remote sensing image restoration method based on an improved Landweber iteration method which expedites the convergence only in the signal domain. As a result, we can still improve the image restoration accuracy of results at the same time of speeding up convergences.
关键词： remote sensing,image restoration,forest monitoring,motion blur,atmospheric turbulence blur,Landweber iteration
更新于2025-09-10 09:29:36
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Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band
摘要： For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.
关键词： Satellite images,Functional time series analysis,Forest monitoring,Online random regression forests,Autoregressive integrated moving average
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Semi-Automatic Deforestation Detection Algorithm with PALSAR-2/ScanSAR HH/HV Polarizations
摘要： A preliminary version of the semi-automatic deforestation detection algorithm was developed for the JJ-FAST system, which monitors deforestation and forest changes in tropical regions for approximately every 1.5 months and covers 77 countries. PALSAR-2/ScanSAR HH, HV, HH and HV ratio were used to detect various deforestation stages. Multi-temporal data were used to suppress the effect of seasonality and rainfall. Moreover, the deforestation accuracies for the active deforestation spots in Brazil and Peru were estimated. The user’s accuracies achieved as 80.5% in the dry season, but these decreased to 11.1% in the rainy season.
关键词： polarization,L-and SAR,Forest monitoring
更新于2025-09-04 15:30:14