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The Nile River basin is the main agricultural area in Egypt. In recent decades, human activities and climate change have remarkably influenced the ecological environment there. Those changes have caused land degradation, sea level rise, and conflicts between land and population, threatening the agricultural system and food security of Egypt. In this study, cropland mapping along the Nile in Egypt over the past three decades (1984–2015) was conducted at annual frequency, using 961 Landsat TM/ETM+/OLI images. Spectral features of selected growing season images and band ratio-based indices were used in supervised classification. Thereafter, terrain and time series information were used to filter possible classification errors on the basis of logical judgment and statistical analysis. The average overall classification accuracy of cropland was greater than 90%. Furthermore, temporal and spatial characteristics of cropland expansion were analysed. The results highlight the annual geographical distribution of cropland dynamics from the Nile Valley to desert. In total, cropland areas had increased by 33.7% from 2848.1 kha in 1984 to 3807.8 kha in 2015, with an annual average increase of 31.0 kha in these 32 years. [ABSTRACT FROM PUBLISHER]

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively. [ABSTRACT FROM PUBLISHER]

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes. Cross-correlogram spectral matching (CCSM) can tell the degree of shape similarity between VI profiles and be used to detect land-cover conversion. However, this method may omit some land conversion in which the before and after land-cover types are rather similar in VI profile shape but differ significantly in absolute VI values. This article proposes a new approach that improves TCVA with an adapted use of CCSM. First, TCVA is employed for preliminary detection of land-cover changes. Second, the changes caused by temporal fluctuations of VI values are identified through the CCSM analysis and excluded to only keep the most likely land-cover conversions. Finally, classification is performed to map the different types of land-cover conversions. The improved change vector analysis (ICVA) was applied to detect land-cover conversions from 2000 to 2008, using a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced VI images for the Beijing–Tianjin–Tangshan urban agglomeration district, China. The results show that ICVA is able to detect land-cover conversion with a significantly higher accuracy (78.00%, κ = 0.56) than TCVA (64.00%, κ = 0.35) or CCSM (66.60%, κ = 0.27). The proposed approach is of particular value in distinguishing actual land-cover conversion from land-cover modifications resulting from phenological changes. [ABSTRACT FROM PUBLISHER]

Timely and accurate monitoring of grassland vegetation dynamics is essential for sustainable grassland management in China. We coupled linear trend analysis (LTA) with change vector analysis (CVA) to improve the effectiveness of grassland monitoring. LTA was used to detect continuous inter-annual vegetation trends to identify significant change trend regions (SCTRs) in location and significant change trend periods (SCTPs) in time. Then CVA was used to depict intra-annual change intensities in SCTRs for a SCTP. The Xilingol steppe in northern China was selected to evaluate the method's performance. Digital images of degraded grasslands derived by the proposed method using data from the VEGETATION instrument on board the Systme Probatoire d’Observation de la Terre (SPOT/VGT) were compared to those derived from Landsat images of the same area. Linear regression analysis comparing degraded grassland areas from the two imagery sources showed good correspondence. An overall accuracy of 85.33% and a kappa coefficient of 0.66 were obtained through error matrix analysis. The results showed a general grassland degradation trend from 1998 to 2007. The SCTRs were mostly distributed in the north, and the grassland degradation trend in SCTRs was more significant from 1998 to 2001 and from 2003 to 2007 during the study period. About 19% of the vegetated area was composed of degraded steppe grassland for the two time periods. [ABSTRACT FROM AUTHOR]