Monitoring of Indonesia Tropical Rainforests and Land Cover Change Using Hybrid Approach of Time Series Landsat Data

Abstract. Andean headwater catchments play a pivotal role to supply fresh water for downstream water users. However, few long-term studies exist on the relative importance of climate change and direct anthropogenic perturbations on flow regimes. In this paper, we assess multi-decadal change in freshwater provision based on long time series (1974–2008) of hydrometeorological data and land cover reconstructions for a 282 km2 catchment located in the tropical Andes. Three main land cover change trajectories can be distinguished: (1) rapid decline of native vegetation in montane forest and páramo ecosystems in ~1/5 or 20% of the catchment area, (2) expansion of agricultural land by 14% of the catchment area, (3) afforestation of 12% of native páramo grasslands with exotic tree species in recent years. Given the strong temporal variability of precipitation and streamflow data related to El Niño–Southern Oscillation, we use empirical mode decomposition techniques to detrend the time series. The long-term increasing trend in rainfall is remarkably different from the observed changes in streamflow that exhibit a decreasing trend. Hence, observed changes in streamflow are not the result of long-term climate change but very likely result from direct anthropogenic disturbances after land cover change. Partial water budgets for montane cloud forest and páramo ecosystems suggest that the strongest changes in evaporative water losses are observed in páramo ecosystems, where progressive colonization and afforestation of high alpine grasslands leads to a strong increase in transpiration losses.

Download Full-text

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

Remote Sensing ◽

10.3390/rs13193951 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3951

Author(s):

Kim André Vanselow ◽

Harald Zandler ◽

Cyrus Samimi

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Land Cover Change ◽

Vegetation Index ◽

Demographic Data ◽

Mountainous Areas ◽

Generalized Additive Mixed Models ◽

Additive Mixed Models ◽

Irregular Data

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

Download Full-text

Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data

Remote Sensing ◽

10.3390/rs12183091 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3091

Author(s):

Shuai Xie ◽

Liangyun Liu ◽

Jiangning Yang

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Data ◽

Land Cover Classification ◽

Time Series Model ◽

Training Data ◽

Series Data ◽

Landsat Data ◽

Data Sampling ◽

Continuous Change

Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage.

Download Full-text

A New Time Series Change Detection Method for Landsat Land use and Land Cover Change

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) ◽

10.1109/multi-temp.2019.8866909 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yukun Lin ◽

Lifu Zhang ◽

Nan Wang

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Detection Method ◽

New Time

Download Full-text

A framework for radiometric sensitivity evaluation of medium resolution remote sensing time series data to built-up land cover change

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127351 ◽

2017 ◽

Cited By ~ 1

Author(s):

Johannes H. Uhl ◽

Stefan Leyk

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Land Cover Change ◽

Time Series Data ◽

Series Data ◽

Sensitivity Evaluation ◽

Medium Resolution ◽

Sensing Time

Download Full-text

Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves

Remote Sensing ◽

10.3390/rs10081251 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1251 ◽

Cited By ~ 4

Author(s):

Boyu Liu ◽

Jun Chen ◽

Jiage Chen ◽

Weiwei Zhang

Keyword(s):

Time Series ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Shape Features ◽

Shape Parameters ◽

Shape Information ◽

Zero Crossing ◽

Ndvi Time Series ◽

Land Cover Change Detection

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

Download Full-text