Land Cover Mapping and Change Analysis in Tropical Humid-highlands: Case of Ndakaini Water Reservoir in Central Kenya

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

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Land cover mapping with ultra‐high‐resolution aerial imagery

Remote Sensing in Ecology and Conservation ◽

10.1002/rse2.189 ◽

2020 ◽

Vol 6 (4) ◽

pp. 429-430 ◽

Cited By ~ 1

Author(s):

Ned Horning

Keyword(s):

High Resolution ◽

Land Cover ◽

Aerial Imagery ◽

Land Cover Mapping

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Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

Remote Sensing ◽

10.3390/rs13122299 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2299

Author(s):

Andrea Tassi ◽

Daniela Gigante ◽

Giuseppe Modica ◽

Luciano Di Martino ◽

Marco Vizzari

Keyword(s):

Land Cover ◽

National Park ◽

Time Window ◽

Central Italy ◽

Google Earth ◽

Data Cube ◽

Landsat 8 ◽

Change Analysis ◽

Object Based ◽

Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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