Cover Estimates Research Articles

Evaluation of the impact of crop residue on fractional vegetation cover estimation by vegetation indices over conservation tillage cropland: a simulation study

ABSTRACT Accurate estimation of fractional vegetation cover (FVC) is of great significance to agricultural production. Crop residue management affect crop residue cover (CRC) over croplands. Crop and crop residue on the soil surface both contribute to overall canopy reflectance. Few studies, however, have examined the effect of crop residue on vegetation indices (VIs) and estimated FVC. The present study evaluated the response of eight commonly used VIs to crop residues and FVC uncertainty caused by crop residue based on the dimidiate pixel model (DPM) by using simulated reflectance of low-tilled cropland via a three-dimensional radiative transfer model. The absolute difference (AD) was used to quantify the spectral difference between crop residues and soils in red and near infrared wavelengths. Increases in normalized difference VI (NDVI), ratio VI (RVI), transformed soil-adjusted VI (TSAVI), and normalized difference phenology index (NDPI) were observed when green crops were mixed with crop residue that had negative ADs with soils, but decreases in enhance VI (EVI), perpendicular VI (PVI), SAVI, and litter-soil-adjusted VI (L-SAVI) were observed when crop residue was present under medium and high vegetation cover. The presence of crop residue with a positive AD with soils reduced NDVI, RVI, TSAVI, and NDPI while increased the other VIs. Crop residue had the least impact on EVI- and SAVI-based DPMs, with FVC-estimated uncertainty less than 0.1, followed by the NDPI- and L-SAVI-based model, while DPMs based on NDVI- and RVI performed poorly. Each VI-based DPM’s estimated uncertainty was highly correlated with AD values. Furthermore, the majority of the VI-based models were sensitive to solar position except for the NDPI-based model. Our findings highlight the need of considering the impact of crop residue on FVC retrieval over low-tilled cropland in future research.

Rethinking forest monitoring for more meaningful global forest landscape change assessments.

Forest ecosystems play an indispensable role in addressing various pressing sustainability and social-ecological challenges such as climate change, biodiversity loss, and ecosystem services deterioration, hence the monitoring of the world's forests is crucial. As part of the global forest assessment workflow, a forest is generally classified and mapped based on land use and/or using a tree canopy cover threshold. In this paper, we examine the limitations of this approach and argue that the use of a land use-based forest definition and tree canopy cover thresholds can overlook forest degradation and enhancement, disguise the actual status of forest landscapes, and misinform management planning. These limitations can delay the development and implementation of forest restoration and conservation measures. To help overcome these issues, we propose some enhancements to the global forest assessment workflow, including the sharing of spatial data and inclusion of tree canopy cover estimates in assessment reports. Such enhancements are needed to achieve more meaningful forest monitoring and reporting in the context of global environmental initiatives, such as those related to climate change mitigation and adaptation, forest restoration, biodiversity conservation, and ecosystem services monitoring.

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.

Cover of Ramalina species as an indicator of habitat quality in threatened coastal woodlands

Coastal forests in the Mediterranean area are threatened habitats due to intense human influence. In the context of global change, a rapid tool is needed for the evaluation of habitat quality in woodlands by calculating lichen cover on twigs rather than weight biomass. We selected epiphytic Ramalina species as indicators of habitat quality and evaluated them in threatened woodlands dominated by Olea europea var. sylvestris on the island of Menorca, a UNESCO Biosphere Reserve in the Mediterranean area. We measured species richness, percentage cover and dry weight in nine sites grouped into urban, rural and naturalized areas to evaluate the effect of land uses, and regressed dry weight against percentage cover. Percentage cover, dry weight, species richness of Ramalina and of its species varied depending on land use, reaching higher rates in naturalized and rural areas rather than in urban areas. In addition, we showed for the first time that field estimates of Ramalina cover at the field were related to dry weight by applying a logarithmic transformation to dry weight. Percentage cover of Ramalina was a good indicator of land uses and will be a rapid, easy and non-destructive tool to assess the quality of woodland habitats in the coastal zone.

SnowWarp: An open science and open data tool for daily monitoring of snow dynamics

Snow cover is a key hydrological variable, critical to understanding water cycles and informing management decisions around resource extraction and recreational activities. Remote sensing open-access data and cloud-based computing platforms are two innovative tools for snow cover estimation. In this paper, we present SnowWarp, a processing framework that uses Google Earth Engine and the R programming languages to combine Landsat 30 m with MODIS 500 m satellite imagery and produce daily-30-m spatial resolution snow cover data anywhere globally.SnowWarp was applied in an alpine catchment in Northern Italy from 2000-2019 and validated using hydrometeorological datasets. Strong correlations between snow cover and ground data were found with correlations in terms of R up to −0.84 for temperature, −0.17 for precipitation, 0.74 for snow depth, and −0.43 for streamflow. The SnowWarp tool is an open-source framework enabling users to map fine spatial and temporal dynamics of snow cover to the ecosystem and hydrological monitoring.

Perceptions of land use and land cover analysed using geospatial data

Considering how individuals perceive landscape is important when undertaking land management decisions. However, understanding how the community perceives a landscape is complex involving their experiences in that landscape, their lived experiences and other socio-cultural processes. One of the key challenges to understanding perception is linking the landscape properties to what is experienced when individuals interact with the landscape. Increasingly the use of geospatial data offers a tool for understanding the landscape and perceptions. This study compares satellite estimates of land use with the perceptions of the inhabitants of Yungay, Chile. Viewshed analysis is used to reconcile the differences seen in the satellite image cover estimates and those of the participants. It was found that restricting the cover estimate to the visible landscape, provided similar estimates of cover to those made by the participants. This varied between demographic groups. With differences between participants living in urban and rural settings particularly evident.

Detecting Mountain Forest Dynamics in the Eastern Himalayas

Forest dynamics is critical to forested ecosystems, and considerable efforts have been devoted to monitoring long-term forest dynamics with the goals of sustainable management and conservation of forests. However, little attention has been given to mountain forests, which are more challenging to monitor due to complex topography, weather, and their distribution. We developed a 30-m resolution tree-canopy cover (TCC) and forest change dataset for the Eastern Himalayas from 1986 to 2021. The tree-canopy cover estimation was validated against estimates from the space-borne Global Ecosystem Dynamics Investigation (GEDI), demonstrating strong consistency (R-square greater than 0.81). A comprehensive assessment for the forest change dataset was performed using 448 visually interpreted points and reported high accuracy of the dataset, i.e., 97.7% and 95.9% for forest loss and gain, respectively. Higher producer and user accuracies were reported for forest loss (PA = 78.0%, UA = 60.9%) than these for forest gain (PA = 61.7%, UA = 56.7%). The results indicated that (1) the mean tree-canopy cover in the region increased by 2.76% over the past three decades, from 40.67% in 1990 to 43.43% in 2020, suggesting the forests have improved during the period; (2) forest loss was identified for a total area of 6990 km2 across the study area, which is less than the 10,700 km2 identified as forest gain; (3) stronger forest gains were found at elevations greater than 3000 m asl, indicating faster forest growth in high elevations likely influenced by the warming temperatures in the Eastern Himalayas.

UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert

Natural vegetation is an important indicator for the maintenance of symbiosis in an oasis in extremely arid zones. Unmanned aerial vehicles have advantages of high resolution and multiple wavebands to obtain details of sparse vegetation cover. So far, studies on the selection of machine learning methods are relatively limited and usually focus on only a few selected methods. In this study, the natural vegetation of the Dariyabui Oasis in the hinterland of the Taklamakan Desert in China was mapped using 2,550 samples of data and 14 visible and multispectral vegetation indices as model variables. Six machine learning methods were used to construct fractional vegetation cover (FVC) predictive regression models. Coefficient of determination (R2), root-mean-square error (RMSE), and mean-absolute error (MAE) were used to evaluate the models. The regression models were divided into four components: visible (RF: R2 = 0.65, RMSE = 0.59 %, MAE = 0.41 %), multispectral (RF: R2 = 0.71, RMSE = 0.54 %, MAE = 0.36 %), visible and multispectral (RF: R2 = 0.69, RMSE = 0.55 %, MAE = 0.37 %), and the product of visible and multispectral vegetation indices (RF: R2 = 0.68, RMSE = 0.57 %, MAE = 0.39 %). Besides, the visible vegetation index results were validated using different years and different aerial height data. The results show that these four regression models can effectively obtain the FVC of sparse vegetation of the desert. This study applied the Random Forest model, which was selected based on a comparison of other models, to predict the status of desert vegetation cover based on spectral data to provide information for its conservation and management.

Historical Spruce Abundance in Central Europe: A Combined Dendrochronological and Palynological Approach

Spruce is the most cultivated tree species in modern forestry in Central Europe, since it has the ability to grow on many soil types with profitable biomass accumulation. However, even-aged and uniform spruce forests are affected by recurring droughts and associated biotic stressors leading to large-scale diebacks across Central Europe causing controversies among foresters and nature conservationists. We investigate the role of spruce in historical woodlands by using 15666 spruce timbers from historical buildings and on the basis of pollen-based land cover estimates using the REVEALS model from 157 pollen sites in southern Central Europe. Start and end dates of the spruce timber samples and their dendrological characteristics (age, growth rates and stem diameters) were used to obtain information on past forest structures. Tree rings and REVEALS estimates are combined at a spatial scale of 1° × 1° resolution, grouped in four sub-regions, and a temporal resolution of 100-year time windows starting from 1150 to 1850 CE. We found that spruce dominates the species assemblage of construction timber with almost 41% and that the harvest age varies little through time, whereas a declining trend in growth rates and stem diameters are observed toward times before modern forestry. Temporal and regional differences in spruce abundance and building activity were found highlighting periods of (i) land abandonment and forest expansion in the 14th century, (ii) increased wood consumption during the 16th century due to population increase and beginning industrial developments, (iii) a forest recovery during and after the Thirty years' war, and (iv) afforestation efforts from the 1650s onwards. Furthermore, this study shows that spruce was constantly present in the study area in most studied sub-regions for the last 800 years. We demonstrate the need of combining tree-ring and pollen data to identify spatiotemporal patterns in spruce abundance and utilization.

Integrating 250 m MODIS data in spectral unmixing for 500 m fractional vegetation cover estimation

• The 250 m MODIS data are integrated with the 500 m MODIS data for FVC estimation. • The 250 m features are stacked with the 500 m features for spectral unmixing. • The proposed method increases the accuracy of FVC estimation obviously. • The method has great potential for enhancing current 500 m FVC products. Fractional vegetation cover (FVC) is an important indicator to measure the potential for carbon neutrality in the terrestrial ecosystem. Spectral unmixing is a common choice for FVC estimation. To deal with the lack of endmembers and intra-class spectral variation, machine learning-based spectral unmixing methods have been developed, but the training samples were constructed based on the data acquired at a time different from the prediction time, which leads to great uncertainty in FVC estimation. Moreover, the used sample features may not cope with areas with strong heterogeneity, where vegetation cannot be reliably identified from the complex background. In this paper, for 500 m FVC estimation, the recently developed the spatio-temporal spectral unmixing model is applied to deal with the inconsistency between training and predicting data by extracting training samples at prediction time directly. Furthermore, more features from 250 m MODIS data are proposed to be integrated with the original 500 m MODIS data. Specifically, for sample construction, new features from 250 m bands (Red and NIR) are stacked with the original 500 m features. For model training, the random forest (RF) model is applied, which can handle high-dimensional data composed of features from different sources. The trained model is employed to predict the FVC of all 500 m MODIS pixels. The proposed method of incorporating 250 m data in 500 m FVC mapping was validated through experiments in five different areas. The results show that the accuracy of FVC mapping can be increased obviously by integrating 250 m features, and the training samples extracted at the prediction time are more reliable for modeling training. This paper has great potential for enhancing current 500 m FVC products at the global scale.

Choosing the optimal method of stock assessment for Saccharina japonica in the northwestern Tatar Strait

Experiments on comparing the methods for assessment of Saccharina japonica commercial stock were conducted in 2021. In total, 11 sites in the northwestern Tatar Strait southward from the Sovetskaya Gavan Bay, along the 44 km stretch of coastline between Cape Krasny Partizan (48о58′ N 140о23′ E) and Cape Korovin (48о37′ N 140о11′ E), were surveyed aboard RV Ubezhdenny and small boats. This is a traditional area of Saccharina japonica commercial harvesting. Four methods were compared: 1) visual assessment of projective cover from the sea surface by two observers independently; 2) assessment of projective cover by two observers independently on the images obtained from a remotely operated underwater vehicle (ROV); 3) estimation of projective cover by a diver; and 4) assessment on the data of quantitative diving samples. The former three methods are comparable and differ by 1.2–1.7 times. The estimates made by the latter method of diving sampling exceed them by 2.4–4.1 times because of artifacts specific to this method. To eliminate inaccuracies, diving samples should be collected under control from the sea surface by ROV. Evaluation of the projective cover by diver is inadvisable. Visual assessments of the projective cover from the sea surface are simple and cheap but inconvenient for documentation. The stock assessment with micro-ROV is recognized as the optimal method provided an adequate accounting of the kelp abundance and complete documenting of the survey materials.

Terrestrial Laser Scanning: An Operational Tool for Fuel Hazard Mapping?

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a sampling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within individual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10%, elevated vegetation within 15%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping individual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard.

Estimation of Snow Cover over Mountain Region based on the Surface Wet-bulb Temperature Measurements

Estimation of Snow Cover over Mountain Region based on the Surface Wet-bulb Temperature Measurements

Multi-band bottom index: A novel approach for coastal environmental monitoring using hyperspectral data

Hyperspectral remote sensing has potential for improving shallow-water environmental monitoring, including benthic cover mapping and estimation of bathymetry and water optical properties. Detailed spectral information provided by hyperspectral sensors is particularly required to discriminate benthic covers that have similar spectral patterns (e.g., coral and algae), which cannot be achieved by multispectral sensors with a limited number of wavebands. An important process in such applications is separation of the bottom reflection from the absorption and scattering caused by the water column. Recently, inversion of a semi-analytical radiative transfer model using airborne hyperspectral data has become popular for simultaneous estimation of benthic cover, bathymetry, and water optical properties. However, it requires non-trivial settings and ancillary information, and generally is not user friendly. A cost-effective water-column correction method based on two-band combination has also been popular for multi-band remote sensing; however, it is not suited to hyperspectral data, because of the combinatorial proliferation of many hyperspectral wave bands. To fill the gap between the two approaches, in this paper, a new cost-effective method for water-column correction optimal for hyperspectral data is proposed. The method, called the multi-band bottom index (MBI), incorporates empirical radiative transfer theory and a traditional spectral enhancement technique (i.e., logarithmic residual method). A case study was conducted for the coral reef habitats around Ishigaki Island, using free hyperspectral satellite imagery (Hyperion). The result shows that our MBI approach has more robust performance in the estimation of the water attenuation coefficient than the existing water-column correction approach. The derived MBI map successfully mitigates water-column effects and enhances the spectral pattern of benthic materials. Comparison with in-situ spectra in the literature revealed that the overall spectral pattern of the MBI is reasonable for the benthic categories of coral, seagrass, and sandy bottom. The MBI approach is also promising for application to recently launched hyperspectral satellite sensors, such as HISUI, DESIS, and PRISMA.

The glacial–terrestrial–fluvial pathway: A multiparametrical analysis of spatiotemporal dissolved organic matter variation in three catchments of Lake Nam Co, Tibetan Plateau

The Tibetan Plateau (TP) is a sensitive alpine environment of global importance, being Asia's water tower, featuring vast ice masses and comprising the world's largest alpine grasslands. Intensified land-use and pronounced global climate change have put pressure on the environment of the TP. We studied the tempo-spatial variability of dissolved organic matter (DOM) to better understand the fluxes of nutrients and energy from terrestrial to aquatic ecosystems in the TP. We used a multiparametrical approach, based on inorganic water chemistry, dissolved organic carbon (DOC) concentration, dissolved organic matter (DOM) characteristics (chromophoric DOM, fluorescence DOM and δ13C of DOM) in stream samples of three catchments of the Nam Co watershed and the lake itself. Satellite based plant cover estimates were used to link biogeochemical data to the structure and degradation of vegetation zones in the catchments. Catchment streams showed site-specific DOM signatures inherited from glaciers, wetlands, groundwater, and Kobresia pygmaea pastures. By comparing stream and lake samples, we found DOM processing and unification by loss of chromophoric DOM signatures and a change towards an autochthonous source of lake DOM. DOM diversity was largest in the headwaters of the catchments and heavily modified in terminal aquatic systems. Seasonality was characterized by a minor influence of freshet and by a very strong impact of the Indian summer monsoon on DOM composition, with more microbial DOM sources. The DOM of Lake Nam Co differed chemically from stream water samples, indicating the lake to be a quasi-marine environment in regards to the degree of chemical modification and sources of DOM. DOM proved to be a powerful marker to elucidate consequences of land use and climatic change on biogeochemical processes in High Asian alpine ecosystems.

Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe

AbstractInterannual variation, especially weather, is an often‐cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post‐fire treatments within sagebrush‐steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management‐relevance of weather differ across space and with time since treatment? Our analysis quantified range‐wide patterns of sagebrush (Artemisia spp.) recovery, by integrating long‐term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post‐fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites' long‐term climates, suggesting differing ecophysiological limitations across sagebrush's range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time‐invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery.

The effect of crude oil pollution on macrophyte assemblage in Kporghor River, southern Nigeria

A study of the effects of crude oil pollution on macrophyte assemblage was carried out in Kporghor River, Rivers State. Standard procedures were used in data collection. Temperature, total dissolved solutes, dissolved oxygen, pH and electrical conductivity were taken in-situ using Extech multi-parameter equipment while biological oxygen demand was incubated in a BOD bottle and determined using standard methods. Aquatic macrophytes were sampled with 0.5m × 0.5m quadrant and estimates of the percentage cover of each species inside the 0.5 × 0.5 m quadrant was transformed onto a scale based on Braun-Blanquet. The diversity and abundance of the different species were higher in Station (2), followed by station (3) and Station (1) respectively. Bambus vulgaris having a percentage of (29.7%) being the most abundant with percentage abundance, Cyrtospermum senegalense (8.72%), Nymphaea lotus (5.81), Acanthophoenix crinite (4.65%), Pistia stratiotes and Eichornia grassippes (2.91%) respectively. Unidentified species of macrophyte collected from the sampling area accounted for 11.62% species abundance. The result of physico-chemical parameters: temperature (29±0.63oC to 29.15±0.72oC), pH (6.03±0.12 to 6.9±0.15), TDS (36.23±6.82mg/ to 40.05±5.30mg/L), conductivity (4430±792.42μS/cm to 4914.75±569.42μS/cm), DO (5.20±1.20mg/L to 5.50±0.49mg/L) and BOD (3.08±0.94mg/L to 3.40±0.61mg/L). Copper (0.015mg/L) level was below WHO, SON and DPR standards for Cu in water bodies. Cadmium (0.10mg/L), Lead (0.18mg/L) and Nickel (0.28mg/L) concentrations were above WHO permissible limits. The abundance of Bambusa vulgaris (an alien species) suggests its adaptation to the study area. However, mitigation actions are recommended to restore this polluted ecosystem.

Factors influencing predictions of understory vegetation biomass from visual cover estimates

AbstractAccurately estimating forage biomass is essential for understanding the nutritional value of vegetation communities for wild herbivores. However, clipping, drying, and weighing vegetation by species requires substantial labor, which restricts the size and number of plots that can be sampled over a large geographical area. Labor‐intensive sampling effort can be reduced by using a double sampling strategy that pairs visual estimates of horizontal cover of vegetation with a subset of clipped plots to create equations that convert horizontal cover to biomass. However, sampling strategy and environmental covariates have rarely been considered when developing cover‐biomass equations. We evaluated environmental and sampling factors that could affect the cover‐biomass relationship using 3,404 cover‐biomass pairs measured in the Clearwater River Basin in North‐central Idaho, USA, collected from May 31–October 4 in 2016 and 2017. Our study demonstrated that when carefully applied, double sampling techniques can streamline field sampling while providing cover‐biomass conversion equations with moderate to high predictive ability (pseudo R2 = 0.25–0.99, = 0.70) for both biomass of current annual growth and high‐quality (e.g., leaves of shrubs) portion of plants (pseudo R2 = 0.65–1). Based on our results, we recommend comparing fit and predictive ability of regression types (linear, log‐linear, Gamma) and collecting >20 cover‐biomass pairs spanning a wide range of horizontal cover (1 to >50%) for each plant species. We found that accounting for overstory canopy cover and season through interaction terms improved the fit of equations by as much as 20% for over half of the 81 species‐specific and 12 structure groups. We found a modest degree of observer bias; thus, training should reduce observer bias and improve consistency but may not alleviate inconsistencies among studies. Careful classification of plant species into plant structure groups can reduce the number of equations, but species‐specific equations for the most abundant and objectively relevant plant species should be developed.

Barrier swaption pricing formulae of mean-reverting model in uncertain environment

Barrier swaption is an exotic option, in which the option purchaser has the right to decide whether the swaption will come into effect within a period and it becomes effective (invalid) only when the underlying rises (falls) to the barrier price. This paper studies four kinds of barrier swaptions based on the mean-reverting model, which are up-and-in payer swaption, down-and-in receiver swaption, down-and-out payer swaption, up-and-out receiver swaption, and the price calculation formulae are given. Then, the related parameters are calculated by the minimum cover estimation method. Finally, the examples are given.

Combining post-disturbance land cover and tree canopy cover from Landsat time series data for mapping deforestation, forest degradation, and recovery across Cambodia

ABSTRACT Mapping of deforestation, forest degradation, and recovery is essential to characterize country-level forest change and formulate mitigation actions. Previous studies have mainly used a simple forest/non-forest classification after forest disturbance to identify deforestation and forest degradation. However, a more flexible approach that is applicable to different forest conditions is desirable. In this study, we examined an approach for mapping deforestation, forest degradation, and recovery using disturbance types and tree canopy cover estimates from annual Landsat time-series data from 1988 to 2020 across Cambodia. We developed models to estimate both disturbance types and tree canopy cover based on a random forest algorithm using predictor variables derived from a trajectory-based temporal segmentation approach. The estimated disturbance types and canopy cover in each year were then used in a rule-based classification of deforestation, forest degradation, and recovery. The producer’s and user’s accuracies ranged from 59.1% to 72.9% and 60.8% to 91.6%, respectively, for the forest change classes of mapping deforestation, forest degradation, and recovery. The approach developed here can be adjusted for different definitions of deforestation, forest degradation, and recovery according to research objectives and thus has the potential to be applied to other study areas.