Monitoring ecosystem reclamation recovery using optical remote sensing: Comparison with field measurements and eddy covariance

Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data

Riparian forests in Iran are valuable ecosystems which have many ecological values. Because of destruction of these forests in recent years, providing spatio-temporal information from area and distribution of these ecosystems has been receiving much attention. This study was performed for mapping distribution, area and density of riparian forests in southern Iran using Sentinel-2A, Google Earth, and field data. First Sentinel-2A satellite image of the study area was provided. The field work was performed to take the training areas and to assess the forest density of riparian forests in Khuzestan province. In the first part of this study, after selecting training areas as pixel-based samples on the Sentinel-2A satellite image, supervised classification of image was performed using support vector machine (SVM) algorithm to classify the distribution of riparian forests. After classification of Sentinel-2A satellite image, the boundary of riparian forests map was checked and corrected on Google Earth images. In the second part of this study, field data, Normalized Difference Vegetation Index (NDVI), and regression model were used to assess the density of riparian forests. Finally, the accuracy of the final riparian forest map (showing both distribution and density of riparian forests) was assessed using Google Earth images. Results showed that the final riparian forest map (showing both distribution and density of riparian forests) with overall accuracy 89% and kappa index 0.81 had a good accuracy for classifying the distribution and density of riparian forests in Khuzestan province. These results demonstrate the accuracy of SVM algorithm for classifying the distribution of riparian forests and also capability of NDVI for classifying the density of riparian forests in this study. Results also showed that regression model (R² = 0.97) is reliable for estimating riparian forest density. The results demonstrated that there are 68447.18 ha of riparian forest around the main rivers in Khuzestan province, mainly distributed in the northwest and southeast of the province. From this area, 54694.15 ha have been covered by dense forests and 13753.03 ha by sparse forests. Results of this research have created the useful data of area, distribution and density of riparian forests in 10-m spatial resolution which is necessary for conservation and management of these forests in southern Iran. It is suggested that mapping area, distribution and density of these forests would be performed using SVM algorithm and NDVI in the certain temporal periods for protective management of these ecosystems in time series.

Inventory of China’s Net Biome Productivity since the 21st Century

Net biome productivity (NBP), which takes into account abiotic respiration and metabolic processes such as fire, pests, and harvesting of agricultural and forestry products, may be more scientific than net ecosystem productivity (NEP) in measuring ecosystem carbon sink levels. As one of the largest countries in global carbon emissions, in China, however, the spatial pattern and evolution of its NBP are still unclear. To this end, we estimated the magnitude of NBP in 31 Chinese provinces (except Hong Kong, Macau, and Taiwan) from 2000 to 2018, and clarified its temporal and spatial evolution. The results show that: (1) the total amount of NBP in China was about 0.21 Pg C/yr1. Among them, Yunnan Province had the highest NBP (0.09 Pg C/yr1), accounting for about 43% of China’s total. (2) NBP increased from a rate of 0.19 Tg C/yr1 during the study period. (3) At present, NBP in China’s terrestrial ecosystems is mainly distributed in southwest and south China, while northwest and central China are weak carbon sinks or carbon sources. (4) The relative contribution rates of carbon emission fluxes due to emissions from anthropogenic disturbances (harvest of agricultural and forestry products) and natural disturbances (fires, pests, etc.) were 70% and 9.87%, respectively. This study emphasizes the importance of using NBP to re-estimate the net carbon sink of China’s terrestrial ecosystem, which is beneficial to providing data support for the realization of China’s carbon neutrality goal and global carbon cycle research.

A synthetic review of terrestrial biological research from the Alberta oil sands region: 10 years of published literature

In the past decade, a large volume of peer-reviewed papers has examined the potential impacts of oil and gas resource extraction in the Canadian oil sands (OS). A large proportion focuses on terrestrial biology: wildlife, birds, and vegetation. We provide a qualitative synthesis of the condition of the environment in the oil sands region (OSR) from 2009 to 2020 to identify gaps and progress cumulative effects assessments. Our objectives were to (1) qualitatively synthesize and critically review knowledge from the OSR; (2) identify consistent trends and generalizable conclusions; and (3) pinpoint gaps in need of greater monitoring or research effort. We visualize knowledge and terrestrial monitoring foci by allocating papers to a conceptual model for the OS. Despite a recent increase in publications, focus has remained concentrated on a few key stressors, especially landscape disturbance, and a few taxa of interest. Stressor and response monitoring is well represented, but direct monitoring of pathways (linkages between stressors and responses) is limited. Important knowledge gaps include understanding effects at multiple spatial scales, mammal health effects monitoring, focused monitoring of local resources important to Indigenous communities, and geospatial coverage and availability, including higher attribute resolution in human footprint, comprehensive land cover mapping, and up-to-date LiDAR coverage. Causal attribution based on spatial proximity to operations or spatial orientation of monitoring in the region is common but may be limited in the strength of inference that it provides. Integr Environ Assess Manag 2022;18:388-406. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.

Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.

Assessing vegetation recovery in reclaimed opencast mines of the Teruel coalfield (Spain) using Landsat time series and boosted regression trees

Opencast mining is an activity that caters to many economic sectors; however, it has a large impact on society and the environment. After mining, the major concern is to restore the previous land cover, which was generally a natural vegetation cover. Establishing permanent vegetation cover can restore landscape connectivity and previous ecosystem functions, enhance aesthetic values and prevent off-side effects associated with post-mining landscapes. Opencast mining reclamation deals with these issues with several strategies that aim to develop a vegetation cover after mining activity has stopped. However, not all reclamation actions are effective, and assessing their efficiency by monitoring vegetation development at reclaimed sites is a time-consuming task because it usually involves extensive field work. In this study, we present a semi-automatic approach based on analysing satellite data (Landsat) time series and using a machine learning technique to identify suitable conditions for vegetation development at reclaimed opencast mines. We analysed the Teruel coalfield (Aragón, central-eastern Spain). This area is a representative Mediterranean-Continental region that is of particular interest due the diversity of reclamation actions that have been applied and the increase in drier conditions during the last decades. Conditions were described with topography derived variables, technical reclamation features and drought-occurrence variables as potential explanatory factors. The implemented approach allowed us to identify the main abiotic filters for vegetation of this geographic region: the water availability and soil retention (both controlled by the topographic slope), and the proximity to seed sources. The analysis evidenced the negative influence of drought occurrence on vegetation development, and different responses were found depending on the timescale at which drought is calculated. Our results indicate that the reclamation landform model is the main key factor influencing vegetation development. A model such as the smooth berm-slope increases water availability and controls soil erosion, and hence, improves vegetation development. In addition, we found that further than 500-600 m from the mine, the effect of seed source declines dramatically. Therefore, all these issues should be considered in future reclamation designs in a Mediterranean-Continental environment. Our methodology could be adapted to other geographic regions where spatial environmental data are available.

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

A model of gross primary productivity based on satellite data suggests formerly afforested peatlands undergoing restoration regain full photosynthesis capacity after five to ten years

Peatlands are an important terrestrial carbon store, but disturbance has resulted in the degradation of many peatland ecosystems and caused them to act as a net carbon source. Restoration work is being undertaken but monitoring the success of these schemes can be difficult and costly using traditional field-based methods. A landscape-scale alternative is to use satellite data to assess the condition of peatlands and to estimate gaseous carbon fluxes. In this study we used Moderate Resolution Imaging Spectroradiometer (MODIS) products to model Gross Primary Productivity (GPP) over peatland sites at various stages of restoration. We found that the MOD17A2H GPP product overestimates GPP modelled from data collected by eddy covariance towers situated at two ex-forestry sites undergoing restoration towards blanket bog at the Forsinard Flows RSPB reserve, Scotland, UK (one full year of data), and a near-natural Atlantic blanket bog site in Glencar, Ireland (ten-year data series). We calibrated a Temperature and Greenness (TG) model for the Forsinard sites and found it to be more accurate than the MODIS GPP product at local scale. We also found that inclusion of a wetness factor using the Normalised Difference Water Index (NDWI) improved inter-annual accuracy of the model. This TGWa (annual Temperature, Greenness and Wetness) model was then applied to six control sites comprising near-natural bog across the reserve, and to six sites on which restoration began between 1998 and 2006. GPP from 2005 to 2016 was estimated for each site using the model. The resulting modelled trends are positive at all six restored sites, increasing by approximately 5.5 g C/m²/yr every year since restoration began in the Forsinard Flows reserve. The results suggest that peatland sites undergoing restoration at Forsinard Flows reach the carbon assimilation potential of near-natural bog sites between 5 and 10 years after restoration was begun.

Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau.