Mapping forest transition trends in Okomu reserve using Landsat and UK-DMC-2 satellite data

This study aims to assess and quantify forest transition within and around one of the few remaining forest  protected landscapes in south west Nigeria, Okomu forest reserve. The study utilised multi-temporal Landsat and UK-DMC-2 satellite images over three epochs (1987, 1999 and 2011) to assess forest landscape change  over the study area. The unsupervised Iterative Self Organising Data Analysis technique was used to generate forest maps and subsequently used for forest change detection over two periods (1987 – 1999 and 1999 – 2011). From the results generated we were able to determine the effectiveness level of forest protected status assigned the study area as a means of reducing deforestation from 1987 to 2011. To achieve this objective,  trends of forest change within and around the reserve were investigated. These result showed that forest  protected status assigned to the reserve has not fully mitigated the effects of deforestation within and around the reserve. The annual deforestation rates of the reserve increased from 3.5% in period 1 (1987 – 1999) to  5.1% in period 2 (1999 – 2011). We suggest that government needs to review forest policies and laws and  improve upon the technical capacity of forest managers to improve forest management. Overall, the study has demonstrated the usefulness of using remote sensing and geographic information system to better understand dynamics of forest cover transition in forest protected areas across tropical forests

Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close days. The 250 m data can provide more information for the abrupt changes and heterogeneous landscapes than the original 500 m MODIS data, thus increasing the accuracy of spatio-temporal fusion predictions. The effectiveness of the proposed scheme was demonstrated using two datasets

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world

Fusion of Landsat 8 OLI and Sentinel-2 MSI data

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes

Reedbed mapping using remotely sensed data

In the UK reedbeds dominated by Phragmites australis have been identified as a priority habitat for most regional Biodiversity Partnerships. Information on the current distribution and quality of reedbed sites across the UK is lacking, yet such information is vital in developing suitable management plans for the conservation and expansion of this threatened habitat. The focus of this thesis is to develop a suitable methodology for accurately mapping the distribution and assessing the biophysical properties of reedbed habitats using remotely sensed data. Three study sites situated in the North West region of the UK were used: Leighton Moss nature reserve in Lancashire, and the River Leven and Esthwaite Water situated in Cumbria. The remotely sensed data used in this study included high-resolution satellite and airborne imagery and ground-based spectral data. Results of the first analytical chapter (i.e. chapter 3) demonstrated the potential of using high resolution QuickBird multi spectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, careful selection of input bands, spatial degradation of input bands, selection of a suitable classification algorithm and post-classification refinement using terrain data. Results of the second analytical chapter (chapter 4) demonstrated the benefits of using multi-seasonal images over single-date images and the effectiveness of incorporating spectral bands with textural measures. Through careful selection of appropriate classification technique, the input image datasets could be used to generate optimal reedbed maps. The results of the multi-seasonal reedbed mapping experiment conducted using QuickBird imagery was the basis for the field spectrometry experiment. The study aimed at monitoring and understanding variations in the spectral reflectance and biophysical properties of reedbeds canopies throughout the seasonal phenological cycle and to identify the optimal spectral indices for quantifying biophysical properties (chapter five ). The results of the experiment indicated that the narrow- band derived Difference Vegetation Index (DV I) and Renormalised Difference Vegetation Index (RDVI) provided the most accurate e'~~iIi1~tes of the leaf area index (LAl) for reedbed canopies (r = 0.77 and 0.72 respectively). Having observed the limitations of accurately deriving canopy heights from experiments conducted in chapter 5, the potential for quantifying canopy biophysical properties from light detection and radar (LiDAR) data (elevation and intensity) was investigated in chapter 6. The study demonstrated some of the potential and limitations of using LiDAR data for characterising reedbed canopies. A canopy height model (CHM) was generated by subtracting the Ordnance Survey (OS) derived digital terrain model (DTM) from the LiDAR- derived digital surface model (DSM). The density of first return points was high for reedbeds and these were able to generate an accurate CHM, when validated against field measurements. LiDAR intensity data displayed specular reflection along the centre of the flight line over reedbeds and water bodies, but not for other land cover/vegetation types. The LiDAR intensity data showed potential for containing considerable information on reedbed canopy structure and pattern that is valuable from an ecological perspective. Results of the final analytical chapter (chapter 7) demonstrated the value in combining appropriately compressed hyperspectral imagery with LiDAR data for the effective mapping of reedbed habitats. The most effective image compression technique was the spectrally segmented principal component analysis (SSPCA), which had the optimal combination of reedbed accuracy and processing efficiency. A substantial improvement in the accuracy of reedbed delineation was achieved when a mask generated by applying a 3m threshold to the LiDAR- derived CHM was used to filter the reedbed map derived from the optimal SSPCA image dataset. Based on the fmdings of chapter 5 and 6, the hyperspectral and LiDAR data was used to derive LAI and canopy height (CH) maps of reedbeds respectively, two vital biophysical measures needed in estimating the quality of reedbed canopies. Hence, this study is a step forward in utilizing spectral, spatial and structural data contained in remotely sensed data for the mapping of reedbed quantity and quality. This research has demonstrated the potential of using remotely sensed data, complemented with adequate ground based information for mapping the spatial extent and quality of reedbed canopies in three specific sites across the North " West region in the UK. Based on the success with a specific habitat type, suggestions are made to further expand these techniques to explore fine scale mapping of more habitats using remotely sensed data of high spatial resolution. Hence, two major studies are recommended for future work, namely (1) updating the Phase 1 habitat survey map using remote sensing techniques, and (2) the integration of high spatial resolution satellite imagery (hyperspectral or QuickBird) and LiDAR data for vegetation mapping and deriving biophysical measures.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Protected Area Monitoring in the Niger Delta Using Multi-Temporal Remote Sensing

Despite their importance, available information on the dynamics of forest protected areas and their management in the Niger delta are insufficient. We present results showing the distribution and structure of forest landscapes across protected areas in two states (Cross River and Delta) within the Niger Delta using multi-temporal remote sensing. Satellite images were classified and validated using ground data, existing maps, Google Earth, and historic aerial photographs over 1986, 2000 and 2014. The total area of forest landscape for 1986, 2000 and 2014 across the identified protected areas were 535,671 ha, 494,009 ha and 469,684 ha (Cross River) and 74,631 ha, 68,470 ha and 58,824 ha (Delta) respectively. The study showed annual deforestation rates for protected areas across both states from 1986 to 2000 were 0.8%. However, the overall annual deforestation rate between 2000 and 2014 was higher in Delta (1.9%) compared to Cross River (0.7%). This study shows accelerated levels of forest fragmentation across protected areas in both states as a side effect of the prevalence of agricultural practices and unsupervised urbanisation. The results show the need for government intervention and policy implementation, in addition to efforts by local communities and conservation organisations in protected area management across ecologically fragile areas of Nigeria

Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats

Reedbeds are important habitats for supporting biodiversity and delivering a range of ecosystem services, yet reedbeds in the UK are under threat from intensified agriculture, changing land use and pollution. To develop appropriate conservation strategies, information on the distribution of reedbeds is required. Field surveys of these wetland environments are difficult, time consuming and expensive to execute for large areas. Remote sensing has the potential to replace or complement such field surveys, yet the specific application to reedbed habitats has not been fully investigated. In the present study, airborne hyperspectral and LiDAR imagery were acquired for two sites in Cumbria, UK. The research aimed to determine the most effective means of analysing hyperspectral data covering the visible, near infrared (NIR) and shortwave infrared (SWIR) regions for mapping reedbeds and to investigate the effects of incorporating image textural information and LiDAR-derived measures of canopy structure on the accuracy of reedbed delineation. Due to the high dimensionality of the hyperspectral data, three image compression algorithms were evaluated: principal component analysis (PCA), spectrally segmented PCA (SSPCA) and minimum noise fraction (MNF). The LiDAR-derived measures tested were the canopy height model (CHM), digital surface model (DSM) and the DSM-derived slope map. The SSPCA-compressed data produced the highest reedbed accuracy and processing efficiency. The optimal SSPCA dataset incorporated 12 PCs comprised of the first 3 PCs derived from each of the spectral segments: visible (392700 nm), NIR (701972 nm), SWIR-1 (9731366 nm) and SWIR-2 (15302240 nm). Incorporating image textural measures produced a significant improvement in the classification accuracy when using MNF-compressed data, but had no impact when using the SSPCA-compressed imagery. A significant improvement (+ 11%) in the accuracy of reedbed delineation was achieved when a mask generated by applying a 3 m threshold to the LiDAR-derived CHM was used to filter the reedbed map derived from the optimal SSPCA dataset. This paper demonstrates the value in combining appropriately compressed hyperspectral imagery with LiDAR data for the effective mapping of reedbed habitats. (C) 2011 Elsevier Inc. All rights reserved

Exploiting high resolution multi-seasonal textural measures and spectral information for reedbed mapping

Reedbeds across the UK are amongst the most important habitats for rare and endangered birds, wildlife and organisms. However, over the past century, this valued wetland habitat has experienced a drastic reduction in quality and spatial coverage due to pressures from human related activities. To this end, conservation organisations across the UK have been charged with the task of conserving and expanding this threatened habitat. With this backdrop, the study aimed to develop a methodology for accurate reedbed mapping through the combined use of multi-seasonal texture measures and spectral information contained in high resolution QuickBird satellite imagery. The key objectives were to determine the most effective single-date (autumn or summer) and multi-seasonal QuickBird imagery suitable for reedbed mapping over the study area; to evaluate the effectiveness of combining multi-seasonal texture measures and spectral information for reedbed mapping using a variety of combinations; and to evaluate the most suitable classification technique for reedbed mapping from three selected classification techniques, namely maximum likelihood classifier, spectral angular mapper and artificial neural network. Using two selected grey-level co-occurrence textural measures (entropy and angular second moment), a series of experiments were conducted using varied combinations of single-date and multi-seasonal QuickBird imagery. Overall, the results indicate the multi-seasonal pansharpened multispectral bands (eight layers) combined with all eight grey level co-occurrence matrix texture measures (entropy and angular second moment computed using windows 3 × 3 and 7 × 7) produced the optimal reedbed (76.5%) and overall classification (78.1%) accuracies using the maximum likelihood classifier technique. Using the optimal 16 layer multi-seasonal pansharpened multispectral and texture combined image dataset, a total reedbed area of 9.8 hectares was successfully mapped over the three study sites. In conclusion, the study has demonstrated the value of utilizing multi-seasonal texture measures and pansharpened multispectral data for reedbed mapping