Tree Sway Time Series of 7 Amazon Tree Species (July 2015–May 2016)

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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PERUBAHAN KOMPOSISI JENIS VEGETASI DAN BURUNG SETELAH PENANAMAN KELAPA SAWIT DI LAHAN KERING PELAIHARI KALIMANTAN SELATAN

EnviroScienteae ◽

10.20527/es.v12i1.1097 ◽

2016 ◽

Vol 12 (1) ◽

pp. 28 ◽

Cited By ~ 2

Author(s):

Kissinger Kissinger ◽

Rina Muhayah Noor Pitri ◽

Hamdani Hamdani

Keyword(s):

Time Series ◽

Land Cover ◽

Tree Species ◽

Time Series Data ◽

Series Data ◽

Species Number ◽

Vegetation Types ◽

Multiple Effect

Elais guenensis planting programe have changed land cover and compotition of vegetation and animal. Vegetation changing by E.guenensis planting becomes multiple effect to other sector. The aims of this research are: to describe changing of land cover and to know changing of vegetation and aves composition after E.guenensis planting. Teresterial survey had arranged to collected data. Analyze of data used time series data and matrice tabulation, descriptive and comparison. Land coverage changing caused by E.guenensis planting. Number of tree species vegetation had been changed from: 33 species to 16 species. Number of aves species had been changed from 21 species to 15 species. Decreasing number and composition of vegetation had influenced decreasing number and composition of aves. Loosing on variation habitat vegetation types which produce food caused population of aves are limited.

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Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Remote Sensing ◽

10.3390/rs8090734 ◽

2016 ◽

Vol 8 (9) ◽

pp. 734 ◽

Cited By ~ 34

Author(s):

David Sheeren ◽

Mathieu Fauvel ◽

Veliborka Josipović ◽

Maïlys Lopes ◽

Carole Planque ◽

...

Keyword(s):

Time Series ◽

Tree Species ◽

Temperate Forests ◽

Satellite Image ◽

Species Classification ◽

Tree Species Classification

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Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery

PLoS ONE ◽

10.1371/journal.pone.0141006 ◽

2015 ◽

Vol 10 (11) ◽

pp. e0141006 ◽

Cited By ~ 46

Author(s):

Jonathan Lisein ◽

Adrien Michez ◽

Hugues Claessens ◽

Philippe Lejeune

Keyword(s):

Time Series ◽

Tree Species ◽

Deciduous Tree ◽

Unmanned Aerial System

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Discerning Effects of Climate Variability, Local Land Degradation and Tree Species Diversity on Remote Sensing Derived Resilience and Resistance of Dry Afromontane Forest

10.21203/rs.3.rs-91187/v1 ◽

2020 ◽

Author(s):

Hadgu Hishe ◽

Louis Oosterlynck ◽

Kidane Giday ◽

Wanda De Keersmaecker ◽

Ben Somers ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Species Diversity ◽

Tree Species ◽

Tree Diversity ◽

Tree Species Diversity ◽

Factors Associated ◽

Afromontane Forest ◽

Growth Stability ◽

Dry Afromontane Forest

Abstract Introduction: Anthropogenic disturbances are increasingly affecting the vitality of tropical dry forests. The future condition of this important biome will depend on its capability to resist, and recover from these disturbances. So far, the temporal stability of dryland forests is rarely studied, but could serve as a basis for forest management and restoration. Methodology: In a degraded dry Afromontane forest in northern Ethiopia, we explored remote sensing derived indicators of forest stability, using MODIS satellite derived NDVI time series from 2001 to 2018. Resilience, resistance and variability were measured using the anomalies (remainders) after time series decomposition into seasonality, trend and remainder components. Growth stability was calculated using the integral of the undecomposed NDVI data. These NDVI derived stability indicators were then related to environmental factors of climate, topography, soil, tree species diversity, and disturbance, obtained from a systematic grid of field inventory plots, using boosted regression trees in R. Resilience and resistance were adequately predicted by these factors with an R2 of 0.67 and 0.48, respectively, but the models for variability and growth stability were weaker. Precipitation of the wettest month, distance from settlements and slope were the most important factors associated with resilience, explaining 51% of the effect. Altitude, temperature seasonality and humus accumulation were the significant factors associated with the resistance of the forest, explaining 61% of the overall effect. A positive effect of tree diversity on resilience was also significant, except that the impact of species evenness declined above a threshold value of 0.70, indicating that perfect evenness reduced the resilience of the forest. Conclusion: A combination of climate, topographic variables and disturbance indicators controlled the stability of the dry forest. Tree diversity is an important component that should be considered in the management and restoration programs of such degraded forests. If local disturbances are alleviated the recovery time of dryland forests could be shortened, which is vital to maintain the ecosystem services these forests provide to local communities and global climate change.

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

Silva Fennica ◽

10.14214/sf.10347 ◽

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 1990âs, 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 Ã 16 m pixel size and units of 240 Ã 240 m, 1200 Ã 1200 m and 12â000 Ã 12â000 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, 12â000 Ã 12â000 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

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Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

10.20944/preprints201910.0039.v1 ◽

2019 ◽

Author(s):

Nicolas Karasiak ◽

Jean-François Dejoux ◽

Mathieu Fauvel ◽

Jérôme Willm ◽

Claude Monteil ◽

...

Keyword(s):

Time Series ◽

Tree Species ◽

Cross Validation ◽

Satellite Image ◽

Forest Composition ◽

Validation Data ◽

Spatial Instability ◽

Classification Errors ◽

Leave One Out ◽

The Impact

Mapping forest composition using multiseasonal optical time series is still challenging. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on ten Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the SVM algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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Forest Stand Species Mapping Using the Sentinel-2 Time Series

Remote Sensing ◽

10.3390/rs11101197 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1197 ◽

Cited By ~ 32

Author(s):

Ewa Grabska ◽

Patrick Hostert ◽

Dirk Pflugmacher ◽

Katarzyna Ostapowicz

Keyword(s):

Time Series ◽

Tree Species ◽

Forest Tree ◽

Species Discrimination ◽

Accurate Information ◽

Silver Fir ◽

Forest Tree Species ◽

Wide Range ◽

Species Mapping ◽

Sentinel 2

Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

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