scholarly journals Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

2018 ◽  
Vol 10 (11) ◽  
pp. 1724 ◽  
Author(s):  
Eileen Helmer ◽  
Thomas Ruzycki ◽  
Barry Wilson ◽  
Kirk Sherrill ◽  
Michael Lefsky ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Forest Biomass ◽  
Regression Tree ◽  
Correlation Coefficients ◽  
Environmental Data ◽  
Stand Age ◽  
Tree Models

We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.

scholarly journals MONITORING OF TIME-SERIES SOIL MOISTURE BASED ON ADVANCED DINSAR

2021 ◽  
Vol V-3-2021 ◽  
pp. 51-55
Author(s):  
C. H. Yang ◽  
A. Müterthies
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Large Scale ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Organic Soil ◽  
Underground Water ◽  
Multi Temporal ◽  
Mean Square Errors

Abstract. Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.

scholarly journals Assessing local trends in indicators of ecosystem services with a time series of forest resource maps

Silva Fennica ◽  
2020 ◽  
Vol 54 (4) ◽  
Author(s):  
Matti Katila ◽  
Tuomas Rajala ◽  
Annika Kangas
Keyword(s):  
Time Series ◽  
Ecosystem Services ◽  
Tree Species ◽  
Spatial Scales ◽  
Geographical Area ◽  
Stand Age ◽  
Forest Resource ◽  
Pixel Size ◽  
Thematic Maps ◽  
Map Data

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric -nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m pixel size and units of 240 × 240 m, 1200 × 1200 m and 12 000 × 12 000 m aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12 000 × 12 000 m. The multiple testing corrections decreased the amount of significant -values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.k22222p

scholarly journals Characterizing variation in Northwest Atlantic fish-stock abundance

2012 ◽  
Vol 69 (5) ◽  
pp. 913-922 ◽  
Author(s):  
Brian J. Rothschild ◽  
Yue Jiao
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Regression Tree ◽  
Correlation Coefficients ◽  
Fish Stock ◽  
Northwest Atlantic ◽  
Regression Tree Analysis ◽  
Statistical Time ◽  
Stock Abundance

Abstract Rothschild, B. J., and Jiao, Y. 2012. Characterizing variation in Northwest Atlantic fish-stock abundance. – ICES Journal of Marine Science, 69: 913–922. Catch-per-tow indices obtained by research vessels for the years 1963–2009 from NAFO statistical areas 4W, 4X, 5Y, and 5Z were studied to determine how fish “apparent abundance” in the decade 2000–2009 differed from the long-term time-series. Cluster analysis of normalized catch-per-tow data indicated that the abundance and species composition of stocks in each statistical area changed dramatically over the 50-year period. There were decreases in thorny skate, ocean pout, cusk, witch flounder, and monkfish and increases in herring, haddock, northern shrimp, and spiny dogfish. Cluster analysis suggested that these decreases and increases were not gradual, but abrupt, and that these abrupt decreases and increases were concentrated in the decade of the 1980s. Observations of abrupt change were supported by regression-tree analysis of individual stocks. Examination of the interrelationship among abundance indices from different stocks by Bonferroni-adjusted correlation coefficients showed that the abundance trajectories of most stocks were uncorrelated. It appears that the set of population transitions during the decade of the 1980s was a dominant event in the statistical time-series.

scholarly journals Satellite Monitoring of Vegetation Phenology and Fire Fuel Conditions in Hawaiian Drylands

2005 ◽  
Vol 9 (21) ◽  
pp. 1-21 ◽  
Author(s):  
Andrew J. Elmore ◽  
Gregory P. Asner ◽  
R. Flint Hughes
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Fire History ◽  
Monitoring Program ◽  
Grazing Intensity ◽  
Environmental Data ◽  
Fire Season ◽  
Satellite Monitoring ◽  
Vegetation Phenology ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Grass-fueled fires accelerate grassland expansion into dry Hawaiian woodlands by destroying native forests and by producing a disturbance regime that favors grass-dominated plant communities. Knowledge of grassland phenology is a key component of ecosystem assessments and fire management in Hawaii, but diverse topographic relief and poor field-sampling capabilities make ground studies impractical. Remote sensing offers the best approach for large-scale, spatially contiguous measurements of dryland vegetation phenology and fire fuel conditions. A 500-m spatial resolution, 8-day temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed substrate conditions was developed for the island of Hawaii between 2000 and 2004. The results compared favorably with similar measurements of drylands from higher-resolution aircraft data. The satellite time series was compared with available environmental data on precipitation, fire history, and grazing intensity. From these analyses, the temporal patterns of PV and its conversion to NPV and finally to bare substrate were observed. An NPV buildup following fire of 7–8 yr was projected, and more heavily grazed lands were found to exhibit reduced NPV cover, most notably during the summer fire season. These results demonstrate the effects that land use and disturbance history have on fire conditions, and they support the concept that grazed lands managed to reduce litter buildup pose a lower risk of fire across ample geographic scales. Time series of satellite observations with modern analysis techniques can be used with environmental data to support a regional fire-monitoring program throughout Hawaii.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Supply Potential and Annual Availability of Timber and Forest Biomass Resources for Energy Considering Inter-Prefectural Trade in Tohoku Region of Japan

2020 ◽  
Vol 3 (1) ◽  
pp. 40
Author(s):  
Yusuke Matsuoka ◽  
Hiroaki Shirasawa ◽  
Uichi Hayashi ◽  
Kazuhiro Aruga
Keyword(s):  
Tree Species ◽  
Forest Biomass ◽  
Wood Chip ◽  
Site Index ◽  
Geographical Survey Institute ◽  
Digital Elevation ◽  
Gis Data ◽  
Biomass Resource ◽  
Biomass Resources ◽  
Generation Capacity

To promote sustainable timber and forest biomass utilization, this study estimated technically feasible and economically viable availability considering forest regenerations. This study focuses on five prefectures, namely, Aomori, Iwate, Miyagi, Akita, and Yamagata, and considers the trade between these prefectures. The data used in this study include forest registration (tree species and site index) and GIS data (information on roads and subcompartment layers) from the prefectures for private and communal forests. Additionally, this study includes GIS data (subcompartment layers, including tree species) from the Forestry Agency of Japan for national forests as well as 10-m-grid digital elevation models (DEMs) from the Geographical Survey Institute. As a result, supply potentials of timber and forest biomass resources were estimated at 11,388,960 m3/year and 2,277,792 m3/year, respectively. Then, those availabilities were estimated at 1,631,624 m3/year and 326,325 m3/year. Therefore, the rate of availabilities to supply potentials was 14.3%. Since timber production, and wood chip usage from thinned woods and logging residues in 2018 were 4,667,000 m3/year and 889,600 m3/year, respectively, the rates of timber and forest biomass resource availabilities to those values were 35.0% and 36.7%, respectively. Furthermore, the demand was estimated at 951,740 m3/year from 100,000 m3/year with the generation capacity of 5 MW. The rate of forest biomass resource availability versus the demand was 34.2%. The rates were increased to 64.1% with an additional regeneration subsidy, 173.3% with the thinning subsidy, and 181.5% with both subsidies. Thus, the estimated availability with both subsidies met the demand sufficiently in this region.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

Sign in / Sign up

Export Citation Format

Share Document