Landsat Time Series Research Articles

Analyzing Gradual Vegetation Changes in the Athabasca Oil Sands Region Using Landsat Data

Oil sand mining in northern Alberta/Canada in the Athabasca region is a major intrusion into the otherwise pristine natural environment. The various types of oil sands mining, transport, and processing are causing large-scale discharge of pollutants. Accordingly, this study examined the gradual changes in the physically undisturbed vegetation, that occurred from 1984 to 2021 in the Athabasca oil sands monitoring region. First, the abrupt changes were masked out with the help of auxiliary and Landsat data. Subsequently, a normalized burn ratio Landsat time-series was applied to the LandTrendr algorithm on the Google Earth Engine. In order to interpret gradual changes, measurement criteria were used to describe vegetation development, vulnerability, and variability. In addition, the spatial and temporal relationship of these to oil sand opencast mines, processing facilities, and steam assisted gravity drainage (SAGD) mines was examined. The results showed that a major part of the vegetation in the Athabasca oil sand monitoring region underwent a positive development (65.9%). However, around the opencast mines a negative vegetation development and stability within a radius of 10 km could be observed. In the surroundings of processing facilities, the development and stability of vegetation was disturbed within a radius of 2 km. Thereby the analysis of land cover classes showed that deciduous, coniferous, and mixed forest are disproportionately affected. Conversely, no negative influences on neighboring vegetation could be detected around SAGD mines. The temporal analysis showed that vegetation disturbance was most pronounced between 1990 and 2000, but recovered in recent years.

Estimation of High-Resolution Fractional Tree Cover Using Landsat Time-Series Observations

Estimation of High-Resolution Fractional Tree Cover Using Landsat Time-Series Observations

Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion

Multi-temporal Landsat-8 satellite images with fine spatial resolution (i.e., 30 m) are crucial for modern digital soil mapping (DSM). Generally, cloud-free images covering bare topsoil are common choices for DSM. However, the number of effective Landsat-8 data is greatly limited due to cloud contamination coupled with the coarse temporal resolution, and interference of material covering topsoil in most of the months, hindering the development of accurate DSM. To address this issue, temporally dense Landsat images were predicted using a spatio-temporal fusion method to improve DSM. Specifically, the recently developed virtual image pair-based spatio-temporal fusion (VIPSTF) method was adopted to produce simulated Landsat-8 time-series, by fusing with 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) time-series with frequent observations. Subsequently, the simulated Landsat-8 data were used for distinguishing different soil classes via a random forest (RF) model. Training and validation samples of soil classes were collected from legacy soil data. Our results indicate that the simulated data were beneficial for improving DSM owing to the increase in class separability. More precisely, after combining the observed and simulated data, the overall accuracy (OA) and kappa coefficient (Kappa) were increased by 3.099% and 0.047, respectively. This research explored the potential of the spatio-temporal fusion method for DSM, providing a new solution for remote sensing-based DSM.

Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images

The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment.

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Saltmarshes in coastal wetlands provide important ecosystem services. Satellite remote sensing has been widely used for mapping and classification of saltmarsh vegetation, however, medium-spatial-resolution satellite datasets such as Landsat-series imagery may induce mixed pixel problems over saltmarsh landscapes which are spatially heterogeneous. Sub-pixel fractional cover estimation of saltmarsh vegetation at species level are required to better understand the distribution and canopy structure of saltmarsh vegetation. In this study, we presented an approach framework for estimating and mapping the fractional cover of major saltmarsh species in the Yellow River Delta, China based on time series Landsat 8 Operational Land Imager data. To solve the problem that the coastal area is frequently covered by clouds, we adopted the recently developed virtual image-based cloud removal (VICR) algorithm to reconstruct missing image values under the cloud/cloud shadows over the time series Landsat imagery. Then, we developed an ensemble learning model (ELM), which incorporates Random Forest Regression (RFR), K-Nearest Neighbor Regression (KNNR) and Gradient Boosted Regression Tree (GBRT) based on temporal-spectral features derived from the time-series cloudless images to estimate the fractional cover of major vegetation types, i.e., Phragmites australis, Suaeda salsa and the invasive species, Spartina alterniflora. High spatial resolution imagery acquired by the Unmanned Aerial Vehicle and Gaofen-6 satellites were used for reference sample collections. The results showed that our approach successfully estimated the fractional cover of each saltmarsh species (average of R-square:0.891, RMSE: 7.48%). Through four scenarios of experiments, we found that the ELM is advantageous over each individual model. When the images during key months were absent, cloud removal for the Landsat images considerably improved the estimation accuracies. In the study area, Spartina alterniflora covers the largest area (5753.97 ha), followed by Phragmites australis with spatial extent area of 4208.4 ha and Suaeda salsa of 1984.41 ha. The average fractional cover of S. alterniflora was 58.45%, that of P. australis was 51.64% and that of S.salsa was 51.64%.

Examining human disturbances and inundation dynamics in China's marsh wetlands by using time series remote sensing data

The hydrological regime is one of the most significant characteristics of wetlands, which maintains the structural and functional integrity of wetland ecosystems. China experienced rapid economic development since the 1990s, which caused severe degradation of all types of wetlands, especially marsh wetlands that are easily converted through filling or draining. Therefore, it is crucial to examine the inundation alterations in marshes as well as the forces behind the changes. In this study, the inundation dynamics in marsh wetlands of China were documented using time-series Landsat observations from 1992 to 2018. Then, nighttime light data was utilized to indicate the intensity of urbanization and infrastructure construction, which was incorporated with historical statistics to conduct attribution analyses of wetland inundation changes. Great spatial heterogeneity in the water distribution and change trajectory was observed in different areas. Severe wetland desiccation took place in Inner Mongolia and East China, in which the inundation area decreased by 51.3 % and 20.9 %, respectively. By contrast, the water area in North China and Tibetan Plateau increased by 58.2 % and 21.0 %, respectively. Behind the tremendous changes, anthropogenic factors played dominant roles. The marsh wetlands in East China, North China, and Southwest China took up only 1.9 % of the total marsh area but accounted for 26.0 % of the entire nighttime light volume. In East China and Southwest China, urbanization and infrastructure construction had significantly negative effects on wetland inundation. Overgrazing or unregulated irrigation altered the original inundation dynamics of marsh wetlands in Inner Mongolia, Southwest China, the Tibetan plateau, and Northeast China. This study illustrated the possible driving forces behind wetland inundation changes, which could help to locate degrading marsh wetlands triggered by anthropogenic activities. Then, targeted management and conservation actions could be implemented.

Detecting annual anthropogenic encroachment on intertidal vegetation using full Landsat time-series in Fujian, China

ABSTRACT Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Landsat time-series (FLTS) is proposed to detect the year of change (YOC) of intertidal vegetation converted to impervious surfaces (ISs) and artificial ponds (APs), and the condition of the remaining intertidal vegetation was also assessed by FLTS, in the Fujian province, a subtropical coastal area lying in southeast China. The accuracies of the YOC detection of intertidal vegetation converted to IS and AP were 91.84% and 72.73%, with mean absolute errors of 0.26 and 1.06, respectively. The total areas of intertidal vegetation encroached by IS and AP were 31.68 km2 and 23.85 km2, respectively. Most ISs were developed later than 2010, and most APs were developed earlier than 2005, which are highly related to the implementation of local policies for economic development. The remaining intertidal vegetation in growing, stable, and degraded conditions were 43.05%, 56.38%, and 0.57%, respectively. The results indicated that areas of intertidal vegetation were reclaimed for anthropogenic uses at a considerable rate, although the intertidal vegetation still increased owing to natural development after the establishment of natural reserves. The study demonstrates that the FLTS has capacities in monitoring the dynamics in coastal zones solely for its dense earth observations.

Assessment of the Impact of Rubber Plantation Expansion on Regional Carbon Storage Based on Time Series Remote Sensing and the InVEST Model

Rubber plantations in southeast Asia have grown at an unprecedented rate in recent decades, leading to drastic changes in regional carbon storage. To this end, this study proposes a systematic approach for quantitatively estimating and assessing the impact of rubber expansions on regional carbon storage. First, using Sentinel-1 and Sentinel-2 satellite data, the distributions of forest and rubber, respectively, were extracted. Then, based on the Landsat time series (1999–2019) remote sensing data, the stand age estimation of rubber plantations was studied with the improved shapelet algorithm. On this basis, the Ecosystem Services and Tradeoffs model (InVEST) was applied to assess the regional carbon density and storage. Finally, by setting up two scenarios of actual planting and hypothetical non-planting of rubber forests, the impact of the carbon storage under these two scenarios was explored. The results of the study showed the following: (1) The area of rubber was 1.28 × 105 ha in 2019, mainly distributed at an elevation of 200–400 m (accounting for 78.47% of the total of rubber). (2) The average age of rubber stands was 13.85 years, and the total newly established rubber plantations were converted from cropland and natural forests, accounting for 54.81% and 45.19%, respectively. (3) With the expansion of rubber plantations, the carbon density increased from only 2.25 Mg·C/ha in 1999 to more than 15 Mg·C/ha in 2018. Among them, the carbon sequestration increased dramatically when the cropland was replaced by rubber, while deforestation and replacement of natural forests will cause a significant decrease. (4) The difference between the actual and the hypothetical carbon storage reached −0.15 million tons in 2018, which means that the expansion of rubber led to a decline in carbon storage in our study area. These research findings can provide a theoretical basis and practical application for sustainable regional rubber forest plantation and management, carbon balance maintenance, and climate change stabilization.

An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series

Large-scale afforestation in arid and semi-arid areas with fragile ecosystems for the purpose of restoring degradation and mitigating climate change has raised issues of decreased groundwater recharge and ambiguous climatic benefits. An accurate planted forest mapping method is necessary to explore the impacts of afforestation expansion on fragile ecosystems. However, distinguishing planted forests from natural forests using remote sensing technology is not a trivial task due to their strong spectral similarities, even when assisted by phenological variables. In this study, we developed an object- and shapelet-based (OASB) method for mapping the planted forests of the Ningxia Hui Autonomous Region (NHAR), China in 2020 and for tracing the planting years between 1991 and 2020. The novel method consists of two components: (1) a simple non-iterative clustering to yield homogenous objects for building an improved time series; (2) a shapelet-based classification to distinguish the planted forests from the natural forests and to estimate the planting year, by detecting the temporal characteristics representing the planting activities. The created map accurately depicted the planted forests of the NHAR in 2020, with an overall accuracy of 87.3% (Kappa = 0.82). The area of the planted forest was counted as 0.56 million ha, accounting for 67% of the total forest area. Additionally, the planting year calendar (RMSE = 2.46 years) illustrated that the establishment of the planted forests matched the implemented ecological restoration initiatives over the past decades. Overall, the OASB has great potential for mapping the planted forests in the NHAR or other arid and semi-arid regions, and the map products derived from this method are conducive to evaluating forestry eco-engineering projects and facilitating the sustainable development of forest ecosystems.

Carbon uptake of the sugarcane agroecosystem is profoundly impacted by climate variations due to seasonality and topography

Sugarcane is an economically important crop whose growth and carbon uptake play vital roles in the carbon cycle, food security, and global trade development. However, the response of the carbon uptake of sugarcane to climate variations and the influencing factors are still unclear. In this study, seven sugarcane plantation distribution maps during 2000 and 2018 in Guangxi, China, were generated using time series Landsat and Sentinel-2 imagery, and these maps were used to produce stable sugarcane sites by identifying pixels without land cover transformation. Time series gross primary production (GPP) and climate datasets were used to analyze the response of annual GPP to temperature, precipitation, and their interacting effects under different landforms (rugged ground and flat ground) and growing periods. The results showed a significantly positive correlation between annual GPP change and temperature variations. Extreme precipitation, including extreme drought and extreme wetness, caused the annual GPP to decrease sharply, except for extreme wetness in the early growing period. The decrease in annual GPP mainly appeared when extreme drought and hot conditions or extreme wet and cold conditions coexisted, ranging from −20.37% to −0.23%, while the increase in annual GPP especially appeared when non-extreme precipitation and high temperature co-occurred, ranging from 0.02% to 8.44%. In addition, the responses of sugarcane annual GPP change to extreme temperature and precipitation were significantly different between rugged and flat ground. Overall, this study indicated that spatiotemporal heterogeneities existed in the response of sugarcane carbon uptake to climate variations. In the future, effective measures should be adopted in sugarcane management and cultivation to reduce the negative effects of climate change on crop production. • Sugarcane’s carbon uptake response discrepantly to heat and water stresses. • Impacts of climate variations on sugarcane GPP varied with growing stages. • Rugged grounds change the response of sugarcane GPP to extreme climates.

Using POI and time series Landsat data to identify and rebuilt surface mining, vegetation disturbance and land reclamation process based on Google Earth Engine

The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006–2011. By 2020, the total disturbed area is 627.8 km2, with an average annual disturbance of 18.5 km2, and the annual maximum disturbance to the ground reached 64.6 km2 in 2008. With the total reclaimed area being 236.3 km2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines’ location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making.

Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm

Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.

Tracking annual changes in the distribution and composition of saltmarsh vegetation on the Jiangsu coast of China using Landsat time series–based phenological parameters

Saltmarshes provide among the most valuable ecosystem services but have undergone continuous modification under the combined pressures of climate change and anthropogenic disturbance. Dynamic monitoring of the distribution and composition of saltmarsh vegetation is an urgent need for understanding the balance of saltmarshes. Previous studies have examined the possibility of identifying the plant species of saltmarshes using Land Surface Phenology (LSP). However, balancing species identification with the timespan of the monitoring remains challenging, given that the available observations are limited by the frequent occurrence of cloudy weather and periodic tidal fluctuations in coastal areas. To overcome the difficulty, we first filtered out observations contaminated by cloud cover and tidal flooding, then proposed an intra-annual time-series model by incorporating inter-annual observations using the inter-calibrated Landsat observations, finally classified the plant species of saltmarshes using phenological parameters derived from the intra-annual time-series model. The annual changes of the three dominant plant species—Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), and Phragmites australis (P. australis) during 2000–2020 in Jiangsu Province, the most abundant salt marsh area of China, were tracked. The results demonstrate the following: (i) The intrinsic phenological differences among the plant species (∼32 days between S. alterniflora and P. australis) are greater than the latitudinal and temporal phenological variations (9–24 days). Large-scale and long-term classification mapping using LSP was thus feasible. (ii) The annual classification maps achieved a favorable performance with an average overall accuracy of 84.8% with minor fluctuations of 5.5%. Sufficient available observations for the spring and autumn seasons (R2 = 0.78, p < 0.05) and for locations far from the sea (>240 m) were the reasons for this good performance. (iii) The saltmarsh vegetation was resilient to tidal flat reclamation on the Jiangsu coast, as indicated by a slight increase in the area from 300.30 to 336.38 km2. However, this was concentrated within the Nature Reserve (38.3%), with the increasing dominance of two species: S. alterniflora (49.6%) and P. australis (42.7%). Special conservation measures are needed for S. salsa to prevent its continuing decrease and possible future disappearance.

Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey

Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and forest degradation in the ecosystem. The precision of any change detection analysis is highly dependent upon its ability to separate actual changes and fluctuations on a seasonal scale. One of the efficient methods in this context is using the Breaks for Additive Seasonal and Trend (BFAST) set of algorithms. This study aims to perform a comprehensive and comparative evaluation of different Vis’ performance in forest degradation with the Landsat 8 images and BFASTMonitor approach. Through evaluation, the study also considers the potential effects of different forest types and deforestation scales in the Marmara region of Turkey. For this purpose, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR) vegetation indices (VI) were selected for a comparative evaluation. The overall accuracy of VIs in deciduous forests was around 85% for NDVI, NDMI, and NBR, and 78.80% for EVI, while in coniferous forests, the overall accuracy demonstrated higher values of about 88% for NDVI, NDMI, and EVI, and 87.28% for NBR. Consequently, water-sensitive VIs that utilize shortwave infrared bands proved to be slightly more sensitive in detecting forest disturbances while chlorophyll-sensitive VIs represented lower accuracy for both forest types. Overall, all VIs faced an underestimation error in deforested area detection that was observable through negative BIAS. The results illuminate that BFASTMonitor can be considered as a tool in monitoring forest environments due to its acceptable deforestation determination capability in deciduous and coniferous forests, with slightly higher performance for small-scale deforestation patterned regions.

An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data

This study proposes a new method for monitoring land cover change in urban areas using all available Landsat time series data, named the Ensemble of Bidirectional Time Series Analysis (EBTSA). In this method, the bidirectional Continuous Change Detection and Classification (CCDC) and the Chow Test are combined to improve the robustness against data scarcity in earlier times and reduce break detection errors and refine classification results. There are three key stages in this method: break detection using bidirectional CCDs, break refinement using the Chow Test, and bidirectional model integration and classification. The EBTSA method was evaluated over the Tianjin metropolitan area in China using Landsat data from 1989 to 2018. Results show that the proposed method improved spatial and temporal accuracies of both land cover classification and change detection, by reducing the influence of sparser Landsat data in the earlier years and the break detection errors. Using the land cover change results in the Tianjin area obtained using the EBTSA method, we analyzed the spatio-temporal distribution of land cover classification and change detection. It is found from the results that the Tianjin area experienced dramatic urban land changes, characterized by rapid urban expansion and noticeable transition from vegetation to urban land, with diverse changes from urban land to various nonurban land cover types. Results of this study showcase the effectiveness of the proposed method in land cover change monitoring in urban areas, which facilitates a more comprehensive understanding of urban land change dynamics and diversity.

Spatiotemporal Characterization and Analysis of River Morphology Using Long-Term Landsat Imagery and Stream Power

Meandering rivers are among the most dynamic Earth-surface systems, which generally appear in fertile valleys, the most valuable lands for agriculture and human settlement. Landsat time series and morphological parameters are complementary tools for exploring river dynamics. Karun River is the most effluent and largest meandering river in Iran, which keeps the Karun’s basin economy, agriculture, and industrial sections alive; hence, investigating morphological changes in this river is essential. The morphological characteristics of Karun have undergone considerable changes over time due to several tectonic, hydrological, hydraulic, and anthropogenic factors. This study has identified and analyzed morphological changes in Karun River using a time series of Landsat imagery from 1985–2015. On that basis, morphological dynamics, including the river’s active channel width, meander’s neck length, water flow length, sinuosity index, and Cornice central angle, were quantitatively investigated. Additionally, the correlation between the stream power and morphological factors was explored using the data adopted from the hydrometric stations. The results show that the dominant pattern of the Karun River, due to the sinuosity coefficient, is meandering, and the majority of the river falls in the category of developed meander rivers. Moreover, the number of arteries reduced in an anabranch pattern, and the river has been migrating towards the downstream and eastern sides since 1985. This phenomenon disposes a change in the future that can be hazardous to the croplands and demands specific considerations for catchment management.

Quantifying mangrove canopy regrowth and recovery after Hurricane Irma with large-scale repeat airborne lidar in the Florida Everglades

Hurricane Irma caused significant damages to mangrove forested wetlands in south Florida, including defoliation, tree snapping, and uprooting. Previous studies have used optical satellite imagery to estimate large-scale forest disturbance and resilience patterns. However, satellite images alone cannot provide measurements of vertical mangrove structure. In this study, we used dense point cloud data collected by NASA Goddard’s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager before (March 2017) and after (December 2017 and March 2020) Hurricane Irma to quantify the recovery, or lack thereof, of the three-dimensional (3D) mangrove forest structure. Recent resilience and vulnerability models developed from Landsat time series following the storm were used to group the lidar data into distinct disturbance-recovery classes. We then analyzed lidar-based forest canopy within each of the recovery classes to test a suite of forest structural characteristics. Our results indicate that 77.0 % of the survey area experienced canopy height loss three months after Hurricane Irma, whereby the majority of canopy height loss occurred in areas with the tallest mangrove forests (i.e., 15–25 m tall). Our analysis shows that the mangrove canopy height in South Florida increased by an average 0.26 m from December 2017 to March 2020, with most of the forest (84.7 % of the survey area) experiencing canopy height regrowth. However, only 38.1 % of the survey area has recovered to pre-storm canopy height. The distribution of canopy height was significantly altered by Hurricane Irma in the low and intermediate resilience classes, but were not significantly different 2.5 years later. Indeed, in areas of low resilience, little to no vertical change has occurred suggesting the absence of canopy regrowth and natural regeneration. Conversely, mangroves in high resilience class, which are dominated by shorter canopies (<5 m), were not heavily damaged by the storm and have maintained the same structural attributes as those before Hurricane Irma. Our findings highlight that hurricane disturbances significantly alter mangrove forest canopy structure, but recovery of vertical structure varies by resilience classes, species composition, and canopy height.

Monitoring of land use/land cover changes and its implications in the peri-urban areas using multi-temporal landsat satellite data: a case study of Guwahati city, Assam, India

Rapid population growth and urbanization in and around Guwahati city, the state capital of Assam have resulted in tremendous pressure on its limited land and civic infrastructures. Due to severe congestion and shortage of space, people are exploring peri-urban plain and hilly areas around the city and gradually expanding towards adjoining districts for settlement. Earlier studies focused mainly on Guwahati city only and neighboring districts, small and medium towns around the city, and adjacent Meghalaya state have received scant attention in urban policy research. This study evaluates land use land cover changes (LULC) from 1991 to 2021 within the GMDA area and encircling a 50 km buffer from the GMDA boundary. Landsat time series data were used to analyze land use and land cover changes. The result indicates the expansion in built-up areas with a net change of 2.27% from 1991 to 2021 within the 50 km buffer study area. The result indicates the loss of forest class and cultivated land with a net change percentage of 2.12 and 0.84% within the period of 30 years. Apart from the above, the study also reveals an increase in the built-up of 33.25% within the GMDA core area from 1991 to 2021. The impact of these LULC changes shows the expansion of the built-up area beyond the GMDA boundary in the eastern, western, and northwest direction and needs to be regulated for ensuring the planned growth of its outskirts and the surrounding region. Hence through adequate decentralized regional planning, the fast-expanding Guwahati city and its surrounding region can be managed.

Evaluating Spatiotemporal Patterns of Post-Eruption Vegetation Recovery at Unzen Volcano, Japan, from Landsat Time Series

Quantifying vegetation responses after natural disasters helps clarify complex relationships between vegetation and surface processes such as soil erosion. The heterogenous post-disaster landscape offers a naturally stratified environment for this study. Existing research tends to be frequently monitored but small-scale or sporadically monitored but large-scale. The availability of high-quality and free satellite imagery bridges this gap by offering continuous, longer-term observations at the landscape scale. Here we take advantage of a dense Landsat time series to investigate landscape-scale vegetation response rates and factors at Unzen volcano, Japan. We do this by first investigating differences between two popular vegetation indices—The Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), when applied to recovery studies. We then apply pixel-wise regressions to quantify spatio-temporal vegetation response and regression tree analyses to investigate drivers of recovery. Our findings showed that simple linear-log functions best model recovery rates reflecting primary succession trajectories caused by extreme disturbance and damage. Regression tree analyses showed that despite secondary disturbances, vegetation recovery in both the short and long-term is still dominated by eruption disturbance type and elevation. Finally, compared to NDVI, NBR is a better indicator of structural vegetation regrowth for the early years of revegetation.

MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM

Abstract. Forest dynamics are closely related to climate change, natural disasters, and ecological diversity. The accumulated Landsat archive provides an unprecedented opportunity for long-term forest dynamics monitoring globally. However, using Landsat time series to detect small-scale and low-intensity disturbance events is still challenging since the moderate spatial resolution of Landsat images and the mixed pixel problem. Towards improving the ability of vegetation index (VI) in characterizing sub-pixel forest dynamics, this paper introduced the spectral mixture analysis (SMA) to develop a novel Pure Forest Index (PFI). The Continuous Change Detection and Classification (CCDC) algorithm was used to detect forest disturbance based on the PFI time series. Cross-comparison shows that PFI is far superior to other conventional VI in indicating forest conditions since it can enhance the spectral signal of the forest and suppress noises from the background. Time series analysis further demonstrates the superiority of PFI in accurately characterizing forest dynamics. The high overall accuracy of 0.96 for the forest disturbance map generated by the proposed approach was achieved. This study highlights a novel VI for accurately tracking subtle forest changes in a heterogeneous landscape.