Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Evaluation of mangrove restoration effectiveness using remote sensing indices - a case study in Guangxi Shankou Mangrove National Natural Reserve, China

As one of the most productive marine ecosystems in the coastal wetlands, mangrove forests have been severely threatened by intensive human activities. Many countries and regions have carried out mangrove restoration projects. The evaluation of mangrove restoration effectiveness is of great significance for scientific decision-making for restoration engineering and wetland management. In this study, we presented a remote-sensing-based Mangrove Restoration Effectiveness Index (MREI) to evaluate mangrove restoration effectiveness. We took the Guangxi Shankou Mangrove National Natural Reserve (GSMNNR) in China, a UNESCO Biosphere Reserve, as our study area, where four phases of afforestation were implemented during 1990-2022. The MREI was developed based on Landsat-series images by considering the change in mangrove area and the Normalized Difference Vegetation Index (NDVI) from the start year to the end year of each afforestation phase (evaluation period). We further evaluated the Persistence of Restoration Effectiveness (PRE) based on the MREI change trajectory during the whole evaluation period, and the Process-based Restoration Effectiveness Index (PREI) was developed to evaluate the restoration effectiveness at village scale. The results showed that MREI can effectively represent the trajectory of mangrove restoration and showed consistent pattern with high-spatial-resolution imagery. From 1990 to 2022, the mangrove forest area increased from 235.26 ha in 1990 to 873.27 ha in 2022, and 84.59% of the mangrove forest was converted from tidal flats in the reserve. The average value of MREI in the five evaluation phases were 0.48, 0.24, 0.29, 0.17, and 0.72, respectively. The dynamic change of MREI showed that 5.24% of the zones had poor PRE, 44.17% of the zones had excellent PRE. From the perspective of spatial distribution, the Zones with PREI values ranging from high to low were follows: Zone A, E, J, G, C, H, I (D), F, B. Overall, the high value zones of PREI were mainly distributed in the central of the Dandou Sea and the northern part of the Yingluo Bay. The low value zones were distributed in the northwest of the Dandou Sea. We expect the MREI and PREI have great potential to be applied to other regions to evaluate mangrove restoration effectiveness

Evaluation of Cumulative Ecosystem Response to Restoration Projects in the Lower Columbia River and Estuary, 2010

This is the seventh and final annual report of a project (2004–2010) addressing evaluation of the cumulative effects of habitat restoration actions in the 235-km-long lower Columbia River and estuary. The project, called the Cumulative Effects (CE) study, was conducted for the U.S. Army Corps of Engineers Portland District by a collaboration of research agencies led by the Pacific Northwest National Laboratory. We achieved the primary goal of the CE study to develop a methodology to evaluate the cumulative effects of habitat actions in the Columbia Estuary Ecosystem Restoration Program. We delivered 1) standard monitoring protocols and methods to prioritize monitoring activities; 2) the theoretical and empirical basis for a CE methodology using levels-of-evidence; 3) evaluations of cumulative effects using ecological relationships, geo-referenced data, hydrodynamic modeling, and meta-analyses; and 4) an adaptive management process to coordinate and coalesce restoration efforts in the LCRE. A solid foundation has been laid for future comprehensive evaluations of progress made by the Columbia Estuary Ecosystem Restoration Program to understand, conserve, and restore ecosystems in the lower Columbia River and estuary

Mapping 8-day evapotranspiration at 30m spatial resolution by fusion of MODIS and Landsat data and machine learning approach

ABSTRACT Evapotranspiration (ET) is one of the essential factors to understand hydro-meteorological cycle and interaction between the land surface and atmosphere. Estimated ET has been widely used at local to regional scale for sustainable management water resource. Moderate Resolution Imaging Spectroradiometer (MODIS) provides 8-day and monthly global ET products (MOD16) at 1 km. Although MODIS provides ET with high temporal resolution, the application of MOD16 at local or field scale has limitation due to its course resolution. Therefore, ET estimation through data fusion is necessary to provide ET on a high spatiotemporal domain. This study aims at improving both spatial and temporal resolution of ET through fusing MODIS ET and Landsat 8 data. Results show that the rRMSE of the downscaled ET was within 20% and more consistent with in situ ET than MODIS ET

Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

This study evaluated the synergistic use of high spatial resolution multispectral imagery (i.e., QuickBird, 2.4 m) and low-posting-density LIDAR data (3 m) for forest species classification using an object-based approach. The integration of QuickBird multispectral imagery and LIDAR data was considered during image segmentation and the subsequent object-based classification. Three segmentation schemes were examined: (1) segmentation based solely on the spectral image layers; (2) segmentation based solely on LIDAR-derived layers; and (3) segmentation based on both the spectral and LIDAR-derived layers. For each segmentation scheme, objects were generated at twelve different scales in order to determine optimal scale parameters. Six categories of classification metrics were generated for each object based on spectral data alone, LIDAR data alone and the combination of both data sources. Machine learning decision trees were used to build classification rule sets. Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently. Better segmentation quality led to higher classification accuracy. The highest classification accuracy (Kappa = 91.6%) was acquired when using both spectral- and LIDAR-derived metrics based on objects segmented from both spectral and LIDAR layers at scale parameter 250, where best segmentation quality was achieved. Optimal scales were analyzed for each segmentation-classification scheme. Statistical analysis of classification accuracies at different scales revealed that there was a range of optimal scales that provided statistically similar accuracy.close686

Assessment of Spatiotemporal Dynamics of Mangrove in Five Typical Mangrove Reserve Wetlands in Asia, Africa and Oceania

Mangrove wetlands play a key role in global biodiversity conservation, though they have been damaged in recent decades. Therefore, mangrove habitats have been of great concern at the international level since the latter half of the 20th century. We focused on the key issue of the dynamics of mangrove habitats. A comprehensive review of their typicality and status from the global perspective was evaluated before the landscape dynamics of the mangrove habitats at the five sites were interpreted from Landsat satellite images covering 20 years, from 2000 to 2020. Ground-truthing was undertaken after comparing the results with the other published international mangrove datasets. We reached three conclusions: Firstly, within the period from 2000 to 2020, the mangrove area in Dongzhaigang increased by 414 ha, with an increase of 24.6%. In Sembilang NP, Sundarban, Kakadu NP, and RUMAKI, the mangrove area decreased by 1652 ha, 16,091 ha, 83 ha, and 2012 ha, with a decrease of 1.8%, 2.7%, 0.9%, and 3.9%, respectively. Secondly, other types of wetlands play a key role in degradating the mangrove wetlands in all of five protected areas. Thirdly, the rate of mangrove degradation has slowed dramatically based on the five sites over the past two decades, which are generally consistent with the findings of other researchers

Assessment of Spatiotemporal Dynamics of Mangrove in Five Typical Mangrove Reserve Wetlands in Asia, Africa and Oceania

Mangrove wetlands play a key role in global biodiversity conservation, though they have been damaged in recent decades. Therefore, mangrove habitats have been of great concern at the international level since the latter half of the 20th century. We focused on the key issue of the dynamics of mangrove habitats. A comprehensive review of their typicality and status from the global perspective was evaluated before the landscape dynamics of the mangrove habitats at the five sites were interpreted from Landsat satellite images covering 20 years, from 2000 to 2020. Ground-truthing was undertaken after comparing the results with the other published international mangrove datasets. We reached three conclusions: Firstly, within the period from 2000 to 2020, the mangrove area in Dongzhaigang increased by 414 ha, with an increase of 24.6%. In Sembilang NP, Sundarban, Kakadu NP, and RUMAKI, the mangrove area decreased by 1652 ha, 16,091 ha, 83 ha, and 2012 ha, with a decrease of 1.8%, 2.7%, 0.9%, and 3.9%, respectively. Secondly, other types of wetlands play a key role in degradating the mangrove wetlands in all of five protected areas. Thirdly, the rate of mangrove degradation has slowed dramatically based on the five sites over the past two decades, which are generally consistent with the findings of other researchers

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples

Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration

Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1 km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30 m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to downscale MODIS 1 km ET to 30 m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2 = 0.52-0.97, RMSE = 0.47-3.0 mm/8 days and rRMSE = 6.4-37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The downscaled 30 m ET had good agreement with MODIS ET (RMSE = 0.42-3.4 mm/8 days, rRMSE = 3.2-26%). Comparison with the in situ ET measurements showed that the downscaled ET had higher accuracy than MODIS ET.clos

Mechanism of Land Subsidence Mutation in Beijing Plain under the Background of Urban Expansion

Under the background of over-exploitation of groundwater and urban expansion, the land subsidence in the Beijing Plain has dramatically increased recently, and has demonstrated obvious mutation characteristics. Firstly, this paper used the land-use transfer matrix (LUTM) to quantify the urban expansion of Beijing, from 1990 to 2015. Secondly, the gravity center migration model (GCM) and standard deviation ellipse (SDE) methods were employed in order to quantitatively reveal the response relationship between urban expansion and land subsidence in the study area. Finally, the research innovatively combines multi-disciplinary (remote sensing, geophysical prospecting, spatial analysis, and hydrogeology), to analyze the mechanism of land subsidence mutation in the Beijing Plain, at multiple scales. The results showed the following: 1. The development direction of the urban expansion and the subsidence bowl (subsidence rate > 50 mm/year) were highly consistent, with values of 116.8° and 113.3°, respectively. 2. At the regional scale, the overall spatial distribution of subsidence mutations is controlled by the geological conditions, and the subsidence mutation time was mainly in 2005 and 2015. The area where mutation occurred in 2005 was basically located in the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively high (r > 0.62). The area where the settlement mutation occurred in 2015, was mainly located outside the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively low (r < 0.71). 3. In the typical subsidence area, the subsidence mutation occurred mostly in the places where the stratigraphic density is reduced, due to human activities (such as groundwater exploitation). Human activities caused the reduction in stratigraphic density, at 20 m and 90 m vertical depth in urban and rural areas, respectively. 4. At the local scale, clusters of subsidence mutation were located in the fault buffer zone, with a lateral influence range of nearly 1 km in Tongzhou. The scattered settlement mutation is distributed as a spot pattern, and the affected area is relatively small, which basically includes high-rise buildings