Forest Mapping Research Articles

Long-Term Changes in Cork Oak and Holm Oak Patches Connectivity. The Algarve, Portugal, a Mediterranean Landscape Case Study

Structural connectivity can be inferred by several landscape metrics that appear to be relevant for characterizing how landscapes constrain or favor the presence and movement of animal species at the level of the regional landscape. Trends of change can be estimated trough spatial time-series analysis. The use of historical maps increases the time span of analysis of the landscape dynamic, relative to the use of remote sensing-related information. Supported by GIS, in this study, a framework for the analyses of the long-term trends of change in the connectivity of the Algarve regional landscape was used to seize the possibility of expanding the span of the spatial time series by integrating an unpublished agricultural and forest map from the turn of the 19th to the 20th century with another historical map and two recent maps. The total area covered by cork and holm oak-related community patches and their connectivity increased over the 20th century and stabilized in the 21st century. A reflection on Portuguese contemporary land-cover policies is urged, to face the sustainable planning and management challenges concerning biodiversity.

Інформаційна технологія аналізу динаміки вуглецевого балансу лісів Польщі

Forest management involves the use of large areas of land and a large amount of related information. Therefore, the use of geographic information technologies (GIS) is substantiated for the processing, analysis, and visualization of information in forest ecosystems. Green plants and trees deposit carbon and retain it for a long time, they are the main absorbers of carbon on land, so to determine the carbon balance the study of wood growth, accumulation of phytomass in forest ecosystems is necessary. The purpose of the work is to develop a toolkit for determining the geo-distributed phytomass of forests for the calculation of deposited carbon according to statistical data of planting stock, species composition, age class, and other silvicultural and taxonomic indicators provided by the official sources. The article provides a description of Polish forests, GIS analysis of tabular data on taxonomic indicators of forest ecosystems, diagrams, and digital maps of forests. The analysis of the forests state in some voivodships of Poland in 2009 and 2019 was conducted to assess emissions and removals of greenhouse gases by forest ecosystems. The three-level scheme of greenhouse gas inventory recommended by the IPCC technique is described. To carry out the inventory, a multilevel mathematical model is used, which includes: the highest level of inventory (level of national inventory), the middle level of inventory (level of region or district), and the inventory lowest level (level of the elementary plots). A comparative analysis of forestry and tax indicators of Polish forests for the period from 2009 to 2019 and the methods used to calculate greenhouse gas emissions and removals are presented. The amount of carbon deposited over a ten-year period by Polish forest ecosystems has been determined. The architecture of geographic information technology for spatial analysis of carbon deposition and greenhouse gas emissions in the Polish forestry sector has been improved and supplemented. Calculations of the deposited carbon were made according to statistical data of planting stock, species composition, age class, and other silvicultural and taxonomic indicators, which are taken from the official sources. The geoinformation approach to the comparative analysis of greenhouse gases for different time intervals is based on the use of the appropriate digital map layers, mathematical models of inventory, statistical information on business results, as well as the use of a number of other specific parameters describing complex greenhouse gas emission and absorption processes. Keywords: GIS; digital maps; greenhouse gases; forests of Poland.

Forest disturbances and the attribution derived from yearly Landsat time series over 1990\u20132020 in the Hengduan Mountains Region of Southwest China

BackgroundNatural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.MethodsWe used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model.ResultsThe conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.ConclusionsThis study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects.

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.

Comparing global and local maps of the Caribbean pine forests of Andros, home of the critically endangered Bahama Oriole.

Forest loss is occurring at alarming rates across the globe. The pine rockland forests of Andros, The Bahamas, likely represent some of the largest stands of Bahamian subspecies of Caribbean pine in the world. Given the unique species that inhabit these pine forests, such as the endemic and critically endangered Bahama Oriole, monitoring habitats on Andros is crucial to inform conservation planning. We developed a 2019 land classification map to assess the status of nine terrestrial habitats on Andros. Our Random Forest classification model predicted habitat classes with high overall accuracy. Caribbean pine was the dominant land class making up roughly one-third of the total terrestrial area. Whereas much of the pine forest area was found as small patches, most were close to other patches of pine suggesting isolation of forest patches is low. We compared our known intact forest areas to recent forest loss identified by the Hansen et al. Global Forest Change product and assessed areas of habitat disturbance in high-resolution imagery. Our results suggest that this global map overpredicted forest loss on Andros. The small degree of true forest loss on Andros was driven mostly by anthropogenic activity. A cross-tabulation of the Hansen forest loss with fire data showed that understory fires were frequently associated with falsely classified deforestation. Given the threats of climate change to this open forest type-intensifying fire regimes, strengthening hurricanes, and sea level rise-monitoring changes in open forest extent is a critical task across the Caribbean region and the world.

A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms.

Countrywide mapping of shrub forest using multi-sensor data and bias correction techniques

The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km2), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data.

Mapping Phenological Functional Types (PhFT) in the Indian Eastern Himalayas using machine learning algorithm in Google Earth Engine

Phenological studies involve capturing information on dates of recurrent and seasonal biological events in plants and animals. In plants, phenological events of leaf flushing, full bloom, autumn discolouration, leaf fall etc. can be used to distinguish vegetation into separate classes. We refer here such phenological distinction as Phenological Functional Types (PhFT). The PhFT can be considered as the precursor of Plant Functional Types (PFTs) where PFT uses additional traits (e.g. leaf area, tree height, leaf structure, rate of evapotranspiration and photosynthesis, etc.) to classify vegetation. The PFT classification is essentially needed for developing and running dynamic global vegetation models (DGVMs) used in studying impacts of climate change on vegetation. We used archived long time series satellite remote sensing data, Landsat 5,7 and 8 (1985–2019), for classifying a landscape into appropriate PhFT. Normalized Difference Vegetation Index (NDVI) was calculated for the given period in the Google Earth Engine (GEE). The GEE is a cloud-based platform developed for the retrieval and processing of remotely sensed images and other data. The monthly median values of NDVI for the mentioned period was used to label each pixel into appropriate PhFT classes using Random Forest (RF) algorithm in GEE to obtain four distinct classes of evergreen forest, deciduous forest, agriculture and non-forest. The comparison of PhFT map was done with reference maps of global MCD12Q1 and the forest type map of India; with an overall moderate agreement of 68.55% and 66.22%, respectively. MCD12Q1 has an accuracy of 73% for the land cover map and the forest type map of India has 75% accuracy, whereas, we achieved an overall accuracy of 78%. The PhFT classification accuracy can further be improved using additional indices and topographic variables. The methodology demonstrated in this study can be adopted for classifying a landscape into distinct PhFT/PFT classes.

Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes

Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% ± 1.4%) compared to a generalized, national model (70.3% ± 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures.

Shorea albida Sym. does not regenerate in the Badas peat swamp forest, Brunei Darussalam – An assessment using remote sensing technology

The Badas peat swamp forest, Brunei Darussalam, Borneo, is a large Baram river peatland formation facing pressures from natural and increasingly anthropogenic disturbances. Although the peat forest should regenerate naturally after disturbances, there is little quantitative information documenting this regeneration. Given the impenetrability and remoteness of peatland forests, remote sensing technology can fill the peatland forest regeneration knowledge gap. This paper reports on the findings of a study on the resilience of the Badas peat swamp forest, composed primarily of Shorea albida Sym. We identified the scars caused by logging and caterpillar infestation using photo interpretation and the forest inventory map. We employed the Shuttle Radar Topography Mission (SRTM), TanDEM-X 90 m (TDX) digital elevation products, and AW3D30 m (AW) digital surface model (DSM) to assess the regeneration of disturbed forest scars over 14 years. Through measurements of forest height and other indices, we found that the Badas peat swamp forest did not recover from these past disturbances and the depletion of scars progressed even further. Our findings support other authors' conclusions suggesting irrevocable degradation of Bornean peatland and peat forests. We also introduce an accuracy assessment of the forest biomass estimation relying on a comparison of DSMs and present a detailed morphology of the surface of the Badas peat dome.

Multi-sensor change detection for within-year capture and labelling of forest disturbance

Knowledge of forest change type and timing is required for forest management, reporting, and science. Time series of historic satellite data (e.g. Landsat) have resulted in an invaluable record of changes in forest conditions. Natural resource management and reporting typically operate at an annual time step, yet the recent addition of data streams from compatible satellites (e.g., Sentinel-2) offer the possibility of generating frequent, management-relevant forest status assessments and maps of change. Analytical approaches that rely on a time series of observations to identify change often struggle to provide reliable estimates of change events in terminal years of the time series until subsequent, additional observations are available. Methods to meaningfully integrate observations from compatible satellite platforms can provide short-term information to augment and refine estimates of change area and type in those terminal years of the time series. In this research we fuse Landsat-8 and Sentinel-2A and -2B data streams to capture, with reduced latency, stand replacing forest change (harvest and wildfire), tagged to a temporal window of occurrence over an ~10,000 km2 area of central British Columbia, Canada. We introduce a new algorithm, SLIMS (Shrinking Latency in Multiple Streams), to rapidly and reliably detect change, and then use an established Bayesian approach to meaningfully combine changes detected in the Landsat and Sentinel data streams. Our results indicate that the type and timing of stand-replacing disturbances can be identified in these forests with high accuracy. Overall, 13.9% of the study area was disturbed between the end of 2016 and the end of 2017, with the majority of disturbed area attributable to wildfire and a smaller amount attributed to forest harvesting, mostly in the winter 2016–2017 with some limited summer harvest also occurring. Overall accuracy of the change, assessed using independent validation data, was 95% ± 2.3%. The capacity of these change results to augment a trend-based assessment of change for 2017 was also demonstrated and provides a framework for how short- and long-term change detection approaches provide complementary information that can increase the timeliness and accuracy of change area estimates in the terminal years of a time series. These findings also demonstrate the capacity to regard Landsat and Sentinel-2 sensors as elements of a virtual constellation to obtain forest change information in a timely (i.e., end of growing season) and reliable fashion over large areas.

Optimization of the distance-constrained multi-based multi-UAV routing problem with simulated annealing and local search-based matheuristic to detect forest fires: The case of Turkey

Forests cover nearly a third of the Earth’s land area, and they are a key factor for all life on Earth, but unfortunately, forest fires are the greatest danger to their presence. The wildfires jeopardize general wellbeing, security, and require high levels of government resources. They also lead to noteworthy debasement of nature, property loss, and high rates of human death and injury. This paper proposes an algorithm to use and route unmanned aerial vehicles (UAVs) to mitigate forest fire risks. The developed matheuristic algorithm hybridizes simulated annealing and local search metaheuristics with an integer linear programming model. The mathematical model was developed to solve the distance-constrained multi-based multi-UAV routing problem, and because of the complexity of the problem, the generated metaheuristics helps the model to find better solutions. The effectiveness of the proposed matheuristic is tested with a real-life case study for Turkey and is also compared with a genetic algorithm. The Turkish State Meteorological Service generates forest fire-risk maps countrywide every day to predict fire risks 3 days later by using meteorological data. These maps are used to generate the risky regions to be visited by the UAVs, and the existing airports are considered for the UAVs to take off and land. The algorithm is coded using MATLAB and ILOG. The metaheuristics are designed with problem-based operators, and their parameters are tuned by experiments. Computational results demonstrate the effectiveness of the algorithm and the hybridization procedures. Results demonstrate that the CPU times for the methods are acceptable.

Regional Variation in Forest Canopy Height and Implications for Koala (Phascolarctos cinereus) Habitat Mapping and Forest Management

Previous research has shown that the Koala (Phascolarctos cinereus) prefers larger trees, potentially making this a key factor influencing koala habitat quality. Generally, tree height is considered at regional scales which may overlook variation at patch or local scales. In this study, we aimed to derive a set of parameters to assist in classifying koala habitat in terms of tree height, which can then be used as an overlay for existing habitat maps. To determine canopy height variation within a specific forest community across a broad area in eastern Australia, we used freely available Airborne Laser Scanning (ALS) data and adopted a straightforward approach by extracting maximum-height ALS returns within a total of 288 30 m × 30 m “virtual” ALS plots. Our findings show that while maximum tree heights generally fall within published regional-scale parameters (mean height 33.2 m), they vary significantly between subregions (mean height 28.8–39.0 m), within subregions (e.g., mean height 21.3–29.4 m), and at local scales, the tree heights vary in response to previous land-use (mean height 28.0–34.2 m). A canopy height dataset useful for habitat management needs to recognise and incorporate these variations. To examine how this information might be synthesised into a usable map, we used a wall-to-wall canopy height map derived from ALS to investigate spatial and nonspatial clustering techniques that capture canopy height variability at both intra-subregional (100s of hectares) and local (60 hectare) scales. We found that nonspatial K-medians clustering with three or four height classes is suited to intra-subregional extents because it allows for simultaneous assessment and comparison of multiple forest community polygons. Spatially constrained clustering algorithms are suited to individual polygons, and we recommend the use of the Redcap algorithm because it delineates contiguous height classes recognisable on a map. For habitat management, an overlay combining these height classification approaches as separate attributes would provide the greatest utility at a range of scales. In addition to koala habitat management, canopy height maps could also assist in managing other fauna; identifying forest disturbance, regenerating forest, and old-growth forest; and identifying errors in existing forest maps.

The Study of Land Conflict of Mining Activities in the Forest Areas in Morowali Regency

This research aims to describe the chronologies of land conflicts of mining activities in the forest (Borrow- to- Use Permit for Forest Area of PT. Mahligai Artha Sejahtera), identify the dominant factors that cause conflict, and provide an overview of conflict resolution efforts. This research was conducted by collecting information from the public, the permit holder, in this case, PT. Mahligai Artha Sejahtera and related parties. This research was a qualitative descriptive method in terms of subject and history study and facts of the field. The data of this research was collected from August to October 2020. Based on the decree of the Minister of Forestry and Plantation No. SK.757 / Kpts-II/ 1999 on September 23, about the designation map of Forest and Territorial Waters in Central Sulawesi Province, the land location of Buleleng communities was an area for other use but in 2013 (No. SK.635 / Menhut-II / 2013 on September 24, 2013) and 2014 (SK.869 / Menhut-II / 2014 on September 29, 2014) turned into Forest Areas. However, PT Mahligai Artha Sejahtera has an approval license, an environmental feasibility license, an Increase Exploration Mining Business to a Production Operation Mining Business License, and a borrow-to-use permit from the local Government and the Ministry of Environment and Forestry. The function change of the areas in Buleleng village has resulted in conflicts. It is necessary to resolve land disputes by revising the RTRWP/K and establishing forests. Keywords: Conflict, the Function Change of Areas, Land Conflict, Mining Activities, Forest Areas.

Elevated expression of MKRN3 in squamous cell carcinoma of the head and neck and its clinical significance

BackgroundSquamous cell carcinoma of the head and neck (SCCHN) is one of the most common types of cancer that cause a substantial number of cancer-related deaths. Our previous study has revealed that makorin ring finger protein 3 (MKRN3) may act as a key regulator of the SCCHN tumorigenesis; however, its specific role in SCCHN progression has not been reported.MethodsThe Cancer Genome Atlas (TCGA) data analysis and quantitative polymerase chain reaction (qPCR) were used to quantify the MKRN3 mRNA expression levels in SCCHN; immunohistochemical staining or immunoblotting analyses were performed to detect MKRN3 protein expression. Kaplan–Meier plotter was used to assess the prognostic values of MKRN3 in terms of overall survival and disease-free survival. The expression differences based on various clinicopathological features were evaluated using subgroup analysis and forest map analysis. The regulatory mechanism of MKRN3 was further investigated using gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Subsequently, STRING was used to perform a co-expression and enrichment analysis for MKRN3. Homologous modeling, molecular docking, and western blot analyses were performed to investigate the relationship between MKRN3 and its potential target gene P53.ResultsMKRN3 was ectopically expressed between cancerous and noncancerous SCCHN tissues, and its expression level was tightly associated with high T classifications as well as advanced clinical stages. qPCR analysis revealed that MKRN3 was upregulated in the SCCHN cell line. Moreover, Kaplan–Meier and Cox regression analyses indicated that SCCHN patients with high MKRN3 expression had poorer prognosis and that MKRN3 was a potential prognostic marker for SCCHN. Using gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we determined that MKRN3 may be involved in the regulation of synthesis and metabolism and cell growth, death and motility, as well as cancer pathways associated with SCCHN progression. Mechanism investigation further revealed that P53, a potential target of MKRN3, may be involved in the SCCHN tumorigenesis mediated by MKRN3.ConclusionsWe performed a comprehensive evaluation of the clinical significance of MKRN3 and explored its underlying mechanisms. We concluded that MKRN3 represents a valuable predictive biomarker and potential therapeutic target in SCCHN.

Network meta-analysis of 6 kinds of Chinese patent medicines combined with mifepristone in the treatment of uterine fibroids: A protocol for systematic review and network meta-analysis.

Background:Uterine fibroids are benign. They belong to the category of “abdominal mass” in traditional Chinese medicine, and pathogenesis is mainly caused by weakness of the body, qi stagnation, and blood stasis. Drug therapy is the preferred treatment of uterine fibroids in clinical practice, and mifepristone is the most commonly used drug. In the past decade, a large number of clinical randomized controlled trials have proven that Chinese patent medicine combined with mifepristone in the treatment of uterine fibroids has a better curative effect, fewer adverse reactions, and higher safety than mifepristone alone. However, there is a lack of evidence-based research. This study aims to integrate clinical data through network meta-analysis to provide more evidence-based medical evidence for clinical medication.Methods:The comprehensive search included Chinese and other-language databases, such as MEDLINE (PubMed), Web of Science, The Cochrane Library, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, China Scientific Journal Database, and China Biomedical Literature Database. Clinical randomized controlled trials of 6 Chinese patent medicines combined with mifepristone for the treatment of uterine fibroids, including Guizhi Fuling Capsule, Gongliuxiao Capsule, Gongliuqing Capsule, Danbie Capsule, Gongliuning Capsule, and Xiaojiean Capsule were retrieved. The search period was from January 2010 to April 2021. Two researchers screened the literature through EndNote and used Excel to extract data. RevMan 5.3 was used to evaluate the quality of the literature. Treatment measures were analyzed in R language, and a forest map and probability ranking map of various interventions were drawn. The network evidence map and correction comparison funnel map of various interventions were drawn by STATA 14.0 software.Results:This study provides the clinical efficacy and safety of network meta-analysis of 6 kinds of Chinese patent medicines combined with mifepristone in the treatment of uterine fibroids will be systematically evaluated or descriptively analyzed.Conclusion:This study's purpose is to provide a reference for the clinical treatment of uterine fibroids to choose more effective intervention therapies.

Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region

An estimation of where forest fragmentation is likely to occur is critically important for improving the integrity of the forest landscape. We prepare a forest fragmentation susceptibility map for the first time by developing an integrated model and identify its causative factors in the forest landscape. Our proposed model is based upon the synergistic use of the earth observation data, forest fragmentation approach, patch forests, causative factors, and the weight-of-evidence (WOE) method in a Geographical Information System (GIS) platform. We evaluate the applicability of the proposed model in the Indian Himalayan region, a region of rich biodiversity and environmental significance in the Indian subcontinent. To obtain a forest fragmentation susceptibility map, we used patch forests as past evidence of completely degraded forests. Subsequently, we used these patch forests in the WOE method to assign the standardized weight value to each class of causative factors tested by the Variance Inflation Factor (VIF) method. Finally, we prepare a forest fragmentation susceptibility map and classify it into five levels: very low, low, medium, high, and very high and test its validity using 30% randomly selected patch forests. Our study reveals that around 40% of the study area is highly susceptible to forest fragmentation. This study identifies that forest fragmentation is more likely to occur if proximity to built-up areas, roads, agricultural lands, and streams is low, whereas it is less likely to occur in higher altitude zones (more than 2000 m a.s.l.). Additionally, forest fragmentation will likely occur in areas mainly facing south, east, southwest, and southeast directions and on very gentle and gentle slopes (less than 25 degrees). This study identifies Himalayan moist temperate and pine forests as being likely to be most affected by forest fragmentation in the future. The results suggest that the study area would experience more forest fragmentation in the future, meaning loss of forest landscape integrity and rich biodiversity in the Indian Himalayan region. Our integrated model achieved a prediction accuracy of 88.7%, indicating good accuracy of the model. This study will be helpful to minimize forest fragmentation and improve the integrity of the forest landscape by implementing forest restoration and reforestation schemes.

Mapping forest in the southern Great Plains with ALOS-2 PALSAR-2 and Landsat 7/8 data

Southern Great Plains (SGP) of the United States, comprising the states Kansas, Oklahoma, and Texas, spans diverse climatic regions. In recent decades, woody plant continues to expand and form forest (above 2 m in height) across the SGP. However, our knowledge of the forest amount and distribution in this region is very limited. This study aims to map forest, especially evergreen forest (above 2 m in height) in the SGP for the time period of 2015–2017. Annual mosaic data of HH and HV polarization backscattering (25 m) from Phased Arrayed L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard Advanced Land Observing Satellite-2 (ALOS-2), along with their difference (HH-HV) and ratio (HH/HV) were utilized. With the four bands (HH, HV, difference, ratio) of 2017, decision rules of forest were developed based on 30 randomly selected forest plots (as of 2017) across the study area. With the decision rules, a PALSAR-2 based forest map was created for each year from 2015 to 2017. Then an annual maximum normalized difference vegetation index (NDVI) threshold of 0.5, derived from Landsat 8 data of 2017 and the 30 forest plots, was used to filter potential commission error of rough surface and building in each PALSAR-2 based forest map. After that, a forest map circa 2016 was generated, in which each forest pixel was identified as such at least twice during 2015 and 2017. Lastly, a threshold of seasonal NDVI change (0.3) was derived to extract evergreen forest out of the forest map circa 2016. Accuracy assessment for the result forest map suggests a user’s accuracy of 99.2% and a producer’s accuracy of 88.7% for forest. Accuracy assessment for the evergreen forest map suggests a user’s accuracy of 97.3% and a producer’s accuracy of 90.5% for evergreen forest. The result forest map, especially the evergreen forest map, paves the way for follow-up studies on forest resource and woody plant encroachment in the SGP.

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.