scholarly journals MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE

2018 ◽  
Vol XLII-1 ◽  
pp. 339-346 ◽  
Author(s):  
F. N. Numbisi ◽  
F. Van Coillie ◽  
R. De Wulf
Keyword(s):  
Land Cover ◽  
Vegetation Indices ◽  
Image Resolution ◽  
Optical Image ◽  
Congo Basin ◽  
Sar Image ◽  
Radar Images ◽  
Tropical Conditions ◽  
Cocoa Agroforestry ◽  
Occurrence Matrix

<p><strong>Abstract.</strong> Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07<span class="thinspace"></span>% (95<span class="thinspace"></span>% CI: 85.52&amp;ndash;90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region.</p>

scholarly journals The climatic imprint of bimodal distributions in vegetation cover for western Africa

2016 ◽  
Vol 13 (11) ◽  
pp. 3343-3357 ◽  
Author(s):  
Zun Yin ◽  
Stefan C. Dekker ◽  
Bart J. J. M. van den Hurk ◽  
Henk A. Dijkstra
Keyword(s):  
Land Cover ◽  
Congo Basin ◽  
Shortwave Radiation ◽  
Mean Annual Precipitation ◽  
Above Ground Biomass ◽  
Precipitation Regime ◽  
Ground Biomass ◽  
Western Africa ◽  
Precipitation Regimes ◽  
Woody Cover

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

scholarly journals A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

2016 ◽  
Vol 100 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Grazia Caradonna ◽  
Antonio Novelli ◽  
Eufemia Tarantino ◽  
Raffaela Cefalo ◽  
Umberto Fratino
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geospatial Data ◽  
Usage Patterns ◽  
Mediterranean Regions ◽  
Multi Temporal ◽  
Using Data ◽  
Set Up

Abstract Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

Scenarios of the Tropical Dry Forest of Purulia District West Bengal

2020 ◽  
pp. 254-267
Author(s):  
Masuma Begum ◽  
Niloy Pramanick ◽  
Anirban Mukhopadhyay ◽  
Sayani Datta Majumdar
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Tropical Dry Forest ◽  
Dry Forest ◽  
Training Data ◽  
Change Dynamics ◽  
Land Cover Map ◽  
Purulia District

In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.

scholarly journals Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Hung Nguyen Trong ◽  
The Dung Nguyen ◽  
Martin Kappas
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Forest Type ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Tropical Lowland ◽  
Central Vietnam ◽  
Lowland Forests ◽  
Type Classification ◽  
Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

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